DocumentationClassesGenericUnivariateSelect

Class: GenericUnivariateSelect

Univariate feature selector with configurable strategy.

Read more in the User Guide.

Python Reference

Constructors

new GenericUnivariateSelect()

new GenericUnivariateSelect(opts?): GenericUnivariateSelect

Parameters

ParameterTypeDescription
opts?object-
opts.mode?"percentile" | "k_best" | "fpr" | "fdr" | "fwe"Feature selection mode. Note that the 'percentile' and 'kbest' modes are supporting unsupervised feature selection (when y is undefined).
opts.param?number | "all"Parameter of the corresponding mode.
opts.score_func?anyFunction taking two arrays X and y, and returning a pair of arrays (scores, pvalues). For modes ‘percentile’ or ‘kbest’ it can return a single array scores.

Returns GenericUnivariateSelect

Defined in generated/feature_selection/GenericUnivariateSelect.ts:23

Properties

PropertyTypeDefault valueDefined in
_isDisposedbooleanfalsegenerated/feature_selection/GenericUnivariateSelect.ts:21
_isInitializedbooleanfalsegenerated/feature_selection/GenericUnivariateSelect.ts:20
_pyPythonBridgeundefinedgenerated/feature_selection/GenericUnivariateSelect.ts:19
idstringundefinedgenerated/feature_selection/GenericUnivariateSelect.ts:16
optsanyundefinedgenerated/feature_selection/GenericUnivariateSelect.ts:17

Accessors

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:510

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:483

pvalues_

Get Signature

get pvalues_(): Promise<ArrayLike>

p-values of feature scores, undefined if score_func returned scores only.

Returns Promise<ArrayLike>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:456

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

ParameterType
pythonBridgePythonBridge

Returns void

Defined in generated/feature_selection/GenericUnivariateSelect.ts:47

scores_

Get Signature

get scores_(): Promise<ArrayLike>

Scores of features.

Returns Promise<ArrayLike>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:429

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:103

fit()

fit(opts): Promise<any>

Run score function on (X, y) and get the appropriate features.

Parameters

ParameterTypeDescription
optsobject-
opts.X?ArrayLike[]The training input samples.
opts.y?ArrayLikeThe target values (class labels in classification, real numbers in regression). If the selector is unsupervised then y can be set to undefined.

Returns Promise<any>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:120

fit_transform()

fit_transform(opts): Promise<any[]>

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

ParameterTypeDescription
optsobject-
opts.fit_params?anyAdditional fit parameters.
opts.X?ArrayLike[]Input samples.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns Promise<any[]>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:161

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Mask feature names according to selected features.

Parameters

ParameterTypeDescription
optsobject-
opts.input_features?anyInput features.

Returns Promise<any>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:207

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

ParameterTypeDescription
optsobject-
opts.routing?anyA MetadataRequest encapsulating routing information.

Returns Promise<any>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:245

get_support()

get_support(opts): Promise<any>

Get a mask, or integer index, of the features selected.

Parameters

ParameterTypeDescription
optsobject-
opts.indices?booleanIf true, the return value will be an array of integers, rather than a boolean mask.

Returns Promise<any>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:281

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

ParameterType
pyPythonBridge

Returns Promise<void>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:60

inverse_transform()

inverse_transform(opts): Promise<any>

Reverse the transformation operation.

Parameters

ParameterTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns Promise<any>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:319

set_output()

set_output(opts): Promise<any>

Set output container.

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

Parameters

ParameterTypeDescription
optsobject-
opts.transform?"default" | "pandas" | "polars"Configure output of transform and fit_transform.

Returns Promise<any>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:357

transform()

transform(opts): Promise<any>

Reduce X to the selected features.

Parameters

ParameterTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns Promise<any>

Defined in generated/feature_selection/GenericUnivariateSelect.ts:393

GaussianRandomProjectionGradientBoostingClassifier