Catch22Clusterer¶
- class Catch22Clusterer(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) clusterer.
This clusterer 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 clusterer, default=None
An sklearn estimator to be built using the transformed data. Defaults to sklearn KMeans().
- 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.
See also
Catch22
Catch22 transformer in aeon/transformations/collection.
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
>>> import numpy as np >>> from sklearn.cluster import KMeans >>> from aeon.clustering.feature_based import Catch22Clusterer >>> X = np.random.random(size=(10,2,20)) >>> clst = Catch22Clusterer(estimator=KMeans(n_clusters=2)) >>> clst.fit(X) Catch22Clusterer(...) >>> preds = clst.predict(X)
Methods
clone
([random_state])Obtain a clone of the object with the same hyperparameters.
fit
(X[, y])Fit time series clusterer to training data.
fit_predict
(X[, y])Compute cluster centers and predict cluster index for each time series.
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.
predict
(X)Predict the closest cluster each sample in X belongs to.
Predicts labels probabilities for sequences in X.
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.
- 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) BaseCollectionEstimator [source]¶
Fit time series clusterer to training data.
- Parameters:
- X3D np.ndarray (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.
- y: ignored, exists for API consistency reasons.
- Returns:
- self:
Fitted estimator.
- fit_predict(X, y=None) ndarray [source]¶
Compute cluster centers and predict cluster index for each time series.
Convenience method; equivalent of calling fit(X) followed by predict(X)
- Parameters:
- Xnp.ndarray (2d or 3d array of shape (n_cases, n_timepoints) or shape
(n_cases, n_channels, n_timepoints)). Time series instances to train clusterer and then have indexes each belong to return.
- y: ignored, exists for API consistency reasons.
- Returns:
- np.ndarray (1d array of shape (n_cases,))
Index of the cluster each time series in X belongs to.
- 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
.
- predict(X) ndarray [source]¶
Predict the closest cluster each sample in X belongs to.
- Parameters:
- X3D np.ndarray
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)
, wheren_timepoints_i
is length of seriesi
. Other types are allowed and converted into one of the above.
- Returns:
- np.array
shape ``(n_cases)`, index of the cluster each time series in X. belongs to.
- predict_proba(X) ndarray [source]¶
Predicts labels probabilities for sequences in X.
Default behaviour is to call _predict and set the predicted class probability to 1, other class probabilities to 0. Override if better estimates are obtainable.
- Parameters:
- X3D np.ndarray
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)
, wheren_timepoints_i
is length of seriesi
. Other types are allowed and converted into one of the above.
- Returns:
- y2D array of shape [n_cases, n_classes] - predicted class probabilities
1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j
- 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.