SelectFromModel
Meta-transformer for selecting features based on importance weights.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new SelectFromModel(opts?: object): SelectFromModel;Parameters
| Name | 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. Default Value 'auto' |
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. Default Value 1 |
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. Default Value false |
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
Defined in: generated/feature_selection/SelectFromModel.ts:21 (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/SelectFromModel.ts:123 (opens in a new tab)
fit()
Fit the SelectFromModel meta-transformer.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The training input samples. |
opts.fit_params? | any | Other estimator specific parameters. |
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:140 (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/SelectFromModel.ts:189 (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/SelectFromModel.ts:236 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/feature_selection/SelectFromModel.ts:274 (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/SelectFromModel.ts:309 (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/SelectFromModel.ts:79 (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/SelectFromModel.ts:344 (opens in a new tab)
partial_fit()
Fit the SelectFromModel meta-transformer only once.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The training input samples. |
opts.fit_params? | any | Other estimator specific parameters. |
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:379 (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/SelectFromModel.ts:428 (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/SelectFromModel.ts:461 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/feature_selection/SelectFromModel.ts:19 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/feature_selection/SelectFromModel.ts:18 (opens in a new tab)
_py
PythonBridge
Defined in: generated/feature_selection/SelectFromModel.ts:17 (opens in a new tab)
id
string
Defined in: generated/feature_selection/SelectFromModel.ts:14 (opens in a new tab)
opts
any
Defined in: generated/feature_selection/SelectFromModel.ts:15 (opens in a new tab)
Accessors
estimator_
The base estimator from which the transformer is built. This attribute exist only when fit has been called.
Signature
estimator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/feature_selection/SelectFromModel.ts:494 (opens in a new tab)
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/SelectFromModel.ts:544 (opens in a new tab)
max_features_
Maximum number of features calculated during fit. Only defined if the max\_features is not undefined.
Signature
max_features_(): Promise<number>;Returns
Promise<number>
Defined in: generated/feature_selection/SelectFromModel.ts:519 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/feature_selection/SelectFromModel.ts:66 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/feature_selection/SelectFromModel.ts:70 (opens in a new tab)