`wildboar.dimension_selection`#

Select a subset of dimensions

Classes#

`DistanceVarianceThreshold`	Distance selector that removes dimensions with low variance.
`ECSSelector`	ElbowClassSum (ECS) dimension selector.
`SelectDimensionPercentile`	Select the fraction of dimensions with largest score.
`SelectDimensionSignificance`	Select dimensions with a p-value below alpha.
`SelectDimensionTopK`	Select the dimensions with the k highest scores.
`SequentialDimensionSelector`	Sequentially select a subset of dimensions.

class wildboar.dimension_selection.DistanceVarianceThreshold(threshold=0, *, sample=None, metric='euclidean', metric_params=None, random_state=None, n_jobs=None)[source]#

Distance selector that removes dimensions with low variance.

This dimension selector is suitable for unsupervised learning since it only considers the input data and not the labels.

For each dimension, the pairwise distance between time series is computed and dimensions with variance below the specified threshold are removed.

Parameters:

thresholdfloat, optional

The variance threshold.

sampleint or float, optional

Draw a sample of time series for the pairwise distance calculation.

If None, use all samples.
If float, use the specified fraction of samples.
If int, use the specified number of samples.

metricstr, optional

The distance metric.

metric_paramsdict, optional

Optional parameters to the distance metric.

Read more about the metrics and their parameters in the User guide.

random_stateint or RandomState, optional

Controls the random sampling of kernels.

If int, random_state is the seed used by the random number generator.
If numpy.random.RandomState instance, random_state is the random number generator.
If None, the random number generator is the numpy.random.RandomState instance used by numpy.random.

n_jobsint, optional

The number of parallel jobs.

Examples

>>> from wildboar.datasets import load_ering
>>> from wildboar.dimension_selection import DistanceVarianceThreshold
>>> X, y = load_ering()
>>> dv = DistanceVarianceThreshold(threshold=9)
>>> dv.fit(X, y)
DistanceVarianceThreshold(threshold=9)
>>> dv.get_dimensions()
array([ True, False,  True,  True])
>>> dv.transform(X).shape

fit(X, y=None)[source]#

Learn the dimensions to select.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The training samples.
yarray-like of shape (n_samples, ), optional: Ignored.

Returns:

object: The instance itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_dimensions(indices=False)[source]#

Get a boolean mask with the selected dimensions.

Parameters:

indicesbool, optional: If True, return the indices instead of a boolean mask.

Returns:

ndarray of shape (n_selected_dims, ): An index that selects the retained dimensions.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

inverse_transform(X)[source]#

Reverse the transformation.

Parameters:

Xarray-like of shape (n_samples, n_selected_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_dims, n_timestep): The samples with zeros inserted where dimensions would have been removed by transform.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

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.

transform(X)[source]#

Reduce X to the selected dimensions.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_selected_dims, n_timestep): The samples with only the selected dimensions.

class wildboar.dimension_selection.ECSSelector(prototype='mean', r=None, metric='euclidean', metric_params=None)[source]#

ElbowClassSum (ECS) dimension selector.

Select time series dimensions based on the sum of distances between pairs of classes.

Parameters:

prototype{“mean”, “median”, “dtw”}, optional

The method for computing the class prototypes.

rfloat, optional

The warping width if prototype is “dtw”.

metricstr, optional

The distance metric.

metric_paramsdict, optional

Optional parameters to the distance metric.

Read more about the metrics and their parameters in the User guide.

fit(X, y)[source]#

Learn the dimensions to select.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The training samples.
yarray-like of shape (n_samples, ): Ignored.

Returns:

object: The instance itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_dimensions(indices=False)[source]#

Get a boolean mask with the selected dimensions.

Parameters:

indicesbool, optional: If True, return the indices instead of a boolean mask.

Returns:

ndarray of shape (n_selected_dims, ): An index that selects the retained dimensions.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

inverse_transform(X)[source]#

Reverse the transformation.

Parameters:

Xarray-like of shape (n_samples, n_selected_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_dims, n_timestep): The samples with zeros inserted where dimensions would have been removed by transform.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

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.

transform(X)[source]#

Reduce X to the selected dimensions.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_selected_dims, n_timestep): The samples with only the selected dimensions.

class wildboar.dimension_selection.SelectDimensionPercentile(score_func=f_classif, *, sample=None, percentile=10, metric='euclidean', metric_params=None, n_jobs=None, random_state=None)[source]#

Select the fraction of dimensions with largest score.

For each dimension, the pairwise distance between time series is computed and dimensions with the lowest scores are removed.

Parameters:

score_funccallable, optional

Function taking two arrays X and y and returning scores and optionally p-values. The default is f_classif.

sampleint or float, optional

Draw a sample of time series for the pairwise distance calculation.

If None, use all samples.
If float, use the specified fraction of samples.
If int, use the specified number of samples.

percentilefloat, optional

Percent of dimensions to retain.

metricstr, optional

The distance metric.

metric_paramsdict, optional

Optional parameters to the distance metric.

Read more about the metrics and their parameters in the User guide.

random_stateint or RandomState, optional

Controls the random sampling of kernels.

If int, random_state is the seed used by the random number generator.
If numpy.random.RandomState instance, random_state is the random number generator.
If None, the random number generator is the numpy.random.RandomState instance used by numpy.random.

random_stateint or RandomState, optional

Controls the random sampling of kernels.

If int, random_state is the seed used by the random number generator.
If numpy.random.RandomState instance, random_state is the random number generator.
If None, the random number generator is the numpy.random.RandomState instance used by numpy.random.

n_jobsint, optional

The number of parallel jobs.

Examples

>>> from wildboar.datasets import load_ering
>>> from wildboar.dimension_selection import SelectDimensionPercentile
>>> X, y = load_ering()
>>> sdp = SelectDimensionPercentile(percentile=50)
>>> sdp.fit(X, y)
SelectDimensionPercentile(percentile=50)
>>> sdp.get_dimensions()
array([False, False,  True,  True])
>>> sdp.transform(X).shape

fit(X, y=None)[source]#

Learn the dimensions to select.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The training samples.
yarray-like of shape (n_samples, ), optional: Ignored.

Returns:

object: The instance itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_dimensions(indices=False)[source]#

Get a boolean mask with the selected dimensions.

Parameters:

indicesbool, optional: If True, return the indices instead of a boolean mask.

Returns:

ndarray of shape (n_selected_dims, ): An index that selects the retained dimensions.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

inverse_transform(X)[source]#

Reverse the transformation.

Parameters:

Xarray-like of shape (n_samples, n_selected_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_dims, n_timestep): The samples with zeros inserted where dimensions would have been removed by transform.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

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.

transform(X)[source]#

Reduce X to the selected dimensions.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_selected_dims, n_timestep): The samples with only the selected dimensions.

class wildboar.dimension_selection.SelectDimensionSignificance(score_func=f_classif, *, sample=None, alpha=0.05, method='fpr', metric='euclidean', metric_params=None, random_state=None, n_jobs=None)[source]#

Select dimensions with a p-value below alpha.

For each dimension, the pairwise distance between time series is computed and dimensions with p-values above alpha is removed.

Parameters:

score_funccallable, optional

Function taking two arrays X and y and returning scores and optionally p-values. The default is f_classif.

sampleint or float, optional

Draw a sample of time series for the pairwise distance calculation.

If None, use all samples.
If float, use the specified fraction of samples.
If int, use the specified number of samples.

alphaint, optional

Percent of dimensions to retain.

method{“fpr”, “fdr”, “fwe”}, optional

The method for correcting the alpha value.

If “fpr”, false positive rate, apply no correction.
If “fdr”, false discovery rate, apply the Benjamini-Hochberg procedure.
If “fwer”, family-wise error rate, apply the Bonferroni procedure.

metricstr, optional

The distance metric.

metric_paramsdict, optional

Optional parameters to the distance metric.

Read more about the metrics and their parameters in the User guide.

random_stateint or RandomState, optional

Controls the random sampling of kernels.

If int, random_state is the seed used by the random number generator.
If numpy.random.RandomState instance, random_state is the random number generator.
If None, the random number generator is the numpy.random.RandomState instance used by numpy.random.

n_jobsint, optional

The number of parallel jobs.

Examples

>>> from wildboar.datasets import load_basic_motions
>>> from wildboar.dimension_selection import SelectDimensionSignificance
>>> X, y = load_basic_motions()
>>> sds = SelectDimensionSignificance(alpha=0.01)
>>> sds.fit(X, y)
SelectDimensionSignificance(alpha=0.01)
>>> sds.get_dimensions()
array([ True,  True,  True,  True,  True,  True])
>>> sds.transform(X).shape
(80, 6, 100)

fit(X, y=None)[source]#

Learn the dimensions to select.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The training samples.
yarray-like of shape (n_samples, ), optional: Ignored.

Returns:

object: The instance itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_dimensions(indices=False)[source]#

Get a boolean mask with the selected dimensions.

Parameters:

indicesbool, optional: If True, return the indices instead of a boolean mask.

Returns:

ndarray of shape (n_selected_dims, ): An index that selects the retained dimensions.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

inverse_transform(X)[source]#

Reverse the transformation.

Parameters:

Xarray-like of shape (n_samples, n_selected_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_dims, n_timestep): The samples with zeros inserted where dimensions would have been removed by transform.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

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.

transform(X)[source]#

Reduce X to the selected dimensions.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_selected_dims, n_timestep): The samples with only the selected dimensions.

class wildboar.dimension_selection.SelectDimensionTopK(score_func=f_classif, *, sample=None, k=None, metric='euclidean', metric_params=None, random_state=None, n_jobs=None)[source]#

Select the dimensions with the k highest scores.

For each dimension, the pairwise distance between time series is computed and dimensions with the lowest scores are removed.

Parameters:

score_funccallable, optional

Function taking two arrays X and y and returning scores and optionally p-values. The default is f_classif.

sampleint or float, optional

Draw a sample of time series for the pairwise distance calculation.

If None, use all samples.
If float, use the specified fraction of samples.
If int, use the specified number of samples.

kint, optional

The number of top dimensions to select.

metricstr, optional

The distance metric.

metric_paramsdict, optional

Optional parameters to the distance metric.

Read more about the metrics and their parameters in the User guide.

random_stateint or RandomState, optional

Controls the random sampling of kernels.

If int, random_state is the seed used by the random number generator.
If numpy.random.RandomState instance, random_state is the random number generator.
If None, the random number generator is the numpy.random.RandomState instance used by numpy.random.

n_jobsint, optional

The number of parallel jobs.

Examples

>>> from wildboar.datasets import load_ering
>>> from wildboar.dimension_selection import SelectDimensionTopK
>>> X, y = load_ering()
>>> sdt = SelectDimensionTopK(k=1)
>>> sdt.fit(X, y)
SelectDimensionTopK(k=1)
>>> sdt.get_dimensions()
array([False, False,  True, False])
>>> sdt.transform(X).shape
(300, 65)

fit(X, y=None)[source]#

Learn the dimensions to select.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The training samples.
yarray-like of shape (n_samples, ), optional: Ignored.

Returns:

object: The instance itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_dimensions(indices=False)[source]#

Get a boolean mask with the selected dimensions.

Parameters:

indicesbool, optional: If True, return the indices instead of a boolean mask.

Returns:

ndarray of shape (n_selected_dims, ): An index that selects the retained dimensions.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

inverse_transform(X)[source]#

Reverse the transformation.

Parameters:

Xarray-like of shape (n_samples, n_selected_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_dims, n_timestep): The samples with zeros inserted where dimensions would have been removed by transform.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

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.

transform(X)[source]#

Reduce X to the selected dimensions.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_selected_dims, n_timestep): The samples with only the selected dimensions.

class wildboar.dimension_selection.SequentialDimensionSelector(estimator, *, n_dims='auto', cv=5, scoring=None, direction='forward', tol=None)[source]#

Sequentially select a subset of dimensions.

Sequentially select a set of dimensions by adding (forward) or removing (backward) dimensions to greedily form a subset. At each iteration, the algorithm chooses the best dimension to add or remove based on the cross validation score.

Parameters:

estimatorestimator

An unfitted estimator.

n_dims{“auto”} or int, optional

The number of dimensions to select.

If “auto”, the behavior depends on tol:

if tol is not None, dimensions are selected as long as the increase in performance is larger than tol.
otherwise, we select half of the dimensions.

If integer, n_dims is the number of dimensions to select.

cvint, cross-validation generator or an iterable, optional

The cross-validation splitting strategy.

scoringstr or callable, optional

A str (see: The scoring parameter: defining model evaluation rules) or callable to evaluate the predictions on the test set.

direction{“forward”, “backward”}, optional

Backward of forward selection.

tolfloat, optional

The tolerance. If the score is not increased by tol between two iterations, return.

If direction=”backward”, tol can be negative to reduce the number of dimensions.

Examples

>>> from wildboar.datasets import load_ering
>>> from wildboar.dimension_selection import SequentialDimensionSelector
>>> from wildboar.distance import KNeighborsClassifier
>>> X, y = load_ering()
>>> clf = KNeighborsClassifier()
>>> sds = SequentialDimensionSelector(clf, n_dims=2)
>>> sds.fit(X, y)
SequentialDimensionSelector(estimator=KNeighborsClassifier(), n_dims=2)
>>> sds.get_dimensions()
array([ True, False, False,  True])
>>> sds.transform(X).shape
(300, 2, 65)

fit(X, y)[source]#

Learn the dimensions to select.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The training samples.
yarray-like of shape (n_samples, ): The training labels.

Returns:

object: The instance itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_dimensions(indices=False)[source]#

Get a boolean mask with the selected dimensions.

Parameters:

indicesbool, optional: If True, return the indices instead of a boolean mask.

Returns:

ndarray of shape (n_selected_dims, ): An index that selects the retained dimensions.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

inverse_transform(X)[source]#

Reverse the transformation.

Parameters:

Xarray-like of shape (n_samples, n_selected_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_dims, n_timestep): The samples with zeros inserted where dimensions would have been removed by transform.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

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.

transform(X)[source]#

Reduce X to the selected dimensions.

Parameters:

Xarray-like of shape (n_samples, n_dims, n_timestep): The samples.

Returns:

ndarray of shape (n_samples, n_selected_dims, n_timestep): The samples with only the selected dimensions.

wildboar.dimension_selection#

Classes#

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`wildboar.dimension_selection`#