Class: SelectFromModel

Meta-transformer for selecting features based on importance weights.

Constructors

new SelectFromModel()

new SelectFromModel(opts?): SelectFromModel

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.estimator`?	`any`	The base estimator from which the transformer is built. This can be both a fitted (if `prefit` is set to `true`) or a non-fitted estimator. The estimator should have a `feature_importances_` or `coef_` attribute after fitting. Otherwise, the `importance_getter` parameter should be used.
`opts.importance_getter`?	`string`	If ‘auto’, uses the feature importance either through a `coef_` attribute or `feature_importances_` attribute of estimator. Also accepts a string that specifies an attribute name/path for extracting feature importance (implemented with `attrgetter`). For example, give `regressor_.coef_` in case of `TransformedTargetRegressor` or `named_steps.clf.feature_importances_` in case of `Pipeline` with its last step named `clf`. If `callable`, overrides the default feature importance getter. The callable is passed with the fitted estimator and it should return importance for each feature.
`opts.max_features`?	`number`	The maximum number of features to select.
`opts.norm_order`?	`any`	Order of the norm used to filter the vectors of coefficients below `threshold` in the case where the `coef_` attribute of the estimator is of dimension 2.
`opts.prefit`?	`boolean`	Whether a prefit model is expected to be passed into the constructor directly or not. If `true`, `estimator` must be a fitted estimator. If `false`, `estimator` is fitted and updated by calling `fit` and `partial_fit`, respectively.
`opts.threshold`?	`string` \| `number`	The threshold value to use for feature selection. Features whose absolute importance value is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the `threshold` value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If `undefined` and if the estimator has a parameter penalty set to l1, either explicitly or implicitly (e.g, Lasso), the threshold used is 1e-5. Otherwise, “mean” is used by default.

Returns SelectFromModel

Defined in generated/feature_selection/SelectFromModel.ts:21

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/feature_selection/SelectFromModel.ts:19
`_isInitialized`	`boolean`	`false`	generated/feature_selection/SelectFromModel.ts:18
`_py`	`PythonBridge`	`undefined`	generated/feature_selection/SelectFromModel.ts:17
`id`	`string`	`undefined`	generated/feature_selection/SelectFromModel.ts:14
`opts`	`any`	`undefined`	generated/feature_selection/SelectFromModel.ts:15

Accessors

estimator_

Get Signature

get estimator_(): Promise<any>

The base estimator from which the transformer is built. This attribute exist only when fit has been called.

Returns Promise<any>

Defined in generated/feature_selection/SelectFromModel.ts:467

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/SelectFromModel.ts:517

max_features_

Get Signature

get max_features_(): Promise<number>

Maximum number of features calculated during fit. Only defined if the max_features is not undefined.

Returns Promise<number>

Defined in generated/feature_selection/SelectFromModel.ts:492

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/feature_selection/SelectFromModel.ts:66

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/SelectFromModel.ts:118

fit()

fit(opts): Promise<any>

Fit the SelectFromModel meta-transformer.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	If `enable_metadata_routing=False` (default):
`opts.X`?	`ArrayLike`[]	The training input samples.
`opts.y`?	`ArrayLike`	The target values (integers that correspond to classes in classification, real numbers in regression).

Returns Promise<any>

Defined in generated/feature_selection/SelectFromModel.ts:135

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

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Additional fit parameters.
`opts.X`?	`ArrayLike`[]	Input samples.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns Promise<any[]>

Defined in generated/feature_selection/SelectFromModel.ts:179

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Mask feature names according to selected features.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.input_features`?	`any`	Input features.

Returns Promise<any>

Defined in generated/feature_selection/SelectFromModel.ts:221

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

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRouter` encapsulating routing information.

Returns Promise<any>

Defined in generated/feature_selection/SelectFromModel.ts:257

get_support()

get_support(opts): Promise<any>

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

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.indices`?	`boolean`	If `true`, the return value will be an array of integers, rather than a boolean mask.

Returns Promise<any>

Defined in generated/feature_selection/SelectFromModel.ts:291

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/feature_selection/SelectFromModel.ts:79

inverse_transform()

inverse_transform(opts): Promise<any>

Reverse the transformation operation.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	The input samples.

Returns Promise<any>

Defined in generated/feature_selection/SelectFromModel.ts:325

partial_fit()

partial_fit(opts): Promise<any>

Fit the SelectFromModel meta-transformer only once.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.partial_fit_params`?	`any`	If `enable_metadata_routing=False` (default):
`opts.X`?	`ArrayLike`[]	The training input samples.
`opts.y`?	`ArrayLike`	The target values (integers that correspond to classes in classification, real numbers in regression).