TSelect

class TSelect(irrelevant_selector=True, irrelevant_hard_threshold=0.5, irrelevant_percentage_to_keep=0.6, redundant_selector=True, redundant_correlation_threshold=0.7, validation_size=None, random_state=0)

Bases: BaseChannelSelector

Select relevant and non-redundant channels using TSelect.

TSelect first trains a simple classifier on summary statistics extracted independently from each channel. Channels with weak validation ROC AUC are removed. Remaining channels are ranked by their class-probability outputs and redundant channels are grouped using Spearman rank correlation.

Parameters:

irrelevant_selectorbool, default=True

Whether to remove channels with low individual predictive performance.

irrelevant_hard_thresholdfloat, default=0.5

Hard ROC AUC threshold. Channels below this threshold are removed before percentage filtering. If no channel passes the threshold, all channels are restored before percentage filtering.

irrelevant_percentage_to_keepfloat, default=0.6

Proportion of channels to keep after hard-threshold filtering. The best channels by ROC AUC are retained.

redundant_selectorbool, default=True

Whether to remove redundant channels after relevance filtering.

redundant_correlation_thresholdfloat, default=0.7

Mean absolute Spearman rank correlation threshold used to cluster redundant channels.

validation_sizefloat or None, default=None

Size of the validation split. If None, use the TSelect rule: 0.25 for datasets with fewer than 100 cases, otherwise use max(100, round(0.25 * n_cases)) training cases.

random_stateint or None, default=0

Random state used for the validation split and logistic regression.

Attributes:

channels_selected_list[int]

Selected channel indices.

channel_scores_np.ndarray of shape (n_channels,)

Validation ROC AUC score for each channel.

channel_correlations_np.ndarray of shape (n_channels, n_channels)

Mean absolute Spearman rank correlation matrix. Entries not computed are set to np.nan.

clusters_list[list[int]]

Clusters of relevant channels before selecting the best channel from each cluster.

channel_min_np.ndarray of shape (n_channels,)

Per-channel minima used for fit-time preprocessing.

channel_max_np.ndarray of shape (n_channels,)

Per-channel maxima used for fit-time preprocessing.

rank_correlations_dict

Pairwise rank correlations computed during redundant-channel filtering. Empty if redundant-channel filtering is skipped.

removed_series_auc_dict

Mapping from channel index to AUC score for channels removed by AUC filtering.

removed_series_corr_set

Channels removed by correlation filtering.

Notes

Capabilities

Missing Values	No
Multithreading	No
Inverse Transform	No
Univariate	Yes
Multivariate	Yes
Unequal Length	No

transform only subsets the input collection using channels_selected_. Other fitted attributes are retained for inspection of the selection process. If redundant-channel filtering is skipped, off-diagonal entries in channel_correlations_ remain np.nan and rank_correlations_ is empty.

This implementation is based on the TSelect method and credits the original ML-KULeuven implementation: https://github.com/ML-KULeuven/TSelect

References

[1]

Nuyts, L., Perini, L. and Davis, J. “TSelect: selecting relevant and non-redundant channels for multivariate time series classification.” Data Mining and Knowledge Discovery, 39, 76, 2025. https://doi.org/10.1007/s10618-025-01132-4

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()	Get class tags from estimator class and all its parent classes.
get_fitted_params([deep])	Get fitted parameters.
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.
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)

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 to type(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)

Fit transformer to X, optionally using y if supervised.

Writes to self: - is_fitted : flag is set to True. - model attributes (ending in “_”) : dependent on estimator

Parameters:

Xnp.ndarray or list

Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (number of channels, series length) 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.

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, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

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)

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

Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (number of channels, series length) 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.

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, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

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)

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 if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

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 and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

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)

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)

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 if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

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__ or set_tags.

reset(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

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 of get_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.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

set_params(**params)

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)

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)

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

Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (number of channels, series length) 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.

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, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

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