SequentialFeatureSelector

Transformer that performs Sequential Feature Selection.

This Sequential Feature Selector adds (forward selection) or removes (backward selection) features to form a feature subset in a greedy fashion. At each stage, this estimator chooses the best feature to add or remove based on the cross-validation score of an estimator. In the case of unsupervised learning, this Sequential Feature Selector looks only at the features (X), not the desired outputs (y).

Constructors

constructor()

Signature

new SequentialFeatureSelector(opts?: object): SequentialFeatureSelector;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.cv?`	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.direction?`	`"forward"` \| `"backward"`	Whether to perform forward selection or backward selection. `Default Value` `'forward'`
`opts.estimator?`	`any`	An unfitted estimator.
`opts.n_features_to_select?`	`number` \| `"auto"`	If `"auto"`, the behaviour depends on the `tol` parameter: `Default Value` `'warn'`
`opts.n_jobs?`	`number`	Number of jobs to run in parallel. When evaluating a new feature to add or remove, the cross-validation procedure is parallel over the folds. `undefined` means 1 unless in a `joblib.parallel\_backend` (opens in a new tab) context. `\-1` means using all processors. See Glossary for more details.
`opts.scoring?`	`string`	A single str (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set. NOTE that when using a custom scorer, it should return a single value. If `undefined`, the estimator’s score method is used.
`opts.tol?`	`number`	If the score is not incremented by at least `tol` between two consecutive feature additions or removals, stop adding or removing. `tol` can be negative when removing features using `direction="backward"`. It can be useful to reduce the number of features at the cost of a small decrease in the score. `tol` is enabled only when `n\_features\_to\_select` is `"auto"`.

Returns

SequentialFeatureSelector

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:21 (opens in a new tab)

id

string

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:18 (opens in a new tab)

opts

any

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:19 (opens in a new tab)

Accessors

feature_names_in_

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

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:473 (opens in a new tab)

n_features_in_

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:446 (opens in a new tab)

n_features_to_select_

The number of features that were selected.

Signature

n_features_to_select_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:500 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:77 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:81 (opens in a new tab)

support_

The mask of selected features.

Signature

support_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:527 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

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

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:140 (opens in a new tab)

fit()

Learn the features to select from X.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training vectors, where `n\_samples` is the number of samples and `n\_features` is the number of predictors.
`opts.y?`	`ArrayLike`	Target values. This parameter may be ignored for unsupervised learning.

Returns

Promise<any>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:157 (opens in a new tab)

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

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

Returns

Promise<any[]>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:201 (opens in a new tab)

get_feature_names_out()

Mask feature names according to selected features.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

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

Returns

Promise<any>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:253 (opens in a new tab)

get_support()

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

Signature

get_support(opts: object): Promise<any>;

Parameters

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

Returns

Promise<any>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:291 (opens in a new tab)

init()

Initializes the underlying Python resources.

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

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:90 (opens in a new tab)

inverse_transform()

Reverse the transformation operation.

Signature

inverse_transform(opts: object): Promise<any>;

Parameters

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

Returns

Promise<any>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:330 (opens in a new tab)

set_output()

Set output container.

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

Signature

set_output(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.transform?`	`"default"` \| `"pandas"`	Configure output of `transform` and `fit\_transform`.

Returns

Promise<any>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:370 (opens in a new tab)

transform()

Reduce X to the selected features.

Signature

transform(opts: object): Promise<any>;

Parameters

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

Returns

Promise<any>

Defined in: generated/feature_selection/SequentialFeatureSelector.ts:408 (opens in a new tab)

SelfTrainingClassifier SGDClassifier