FeatureUnion

Concatenates results of multiple transformer objects.

This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer.

Parameters of the transformers may be set using its name and the parameter name separated by a ‘__’. A transformer may be replaced entirely by setting the parameter with its name to another transformer, removed by setting to ‘drop’ or disabled by setting to ‘passthrough’ (features are passed without transformation).

Constructors

constructor()

Signature

new FeatureUnion(opts?: object): FeatureUnion;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.n_jobs?`	`number`	Number of jobs to run in parallel. `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.transformer_list?`	`any`	List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. The transformer can be ‘drop’ for it to be ignored or can be ‘passthrough’ for features to be passed unchanged.
`opts.transformer_weights?`	`any`	Multiplicative weights for features per transformer. Keys are transformer names, values the weights. Raises ValueError if key not present in `transformer\_list`.
`opts.verbose?`	`boolean`	If `true`, the time elapsed while fitting each transformer will be printed as it is completed. `Default Value` `false`

Returns

FeatureUnion

Defined in: generated/pipeline/FeatureUnion.ts:27 (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/pipeline/FeatureUnion.ts:109 (opens in a new tab)

fit()

Fit all transformers using X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Input data, used to fit transformers.
`opts.fit_params?`	`any`	Parameters to pass to the fit method of the estimator.
`opts.y?`	`ArrayLike`[]	Targets for supervised learning.

Returns

Promise<any>

Defined in: generated/pipeline/FeatureUnion.ts:126 (opens in a new tab)

fit_transform()

Fit all transformers, transform the data and concatenate results.

Signature

fit_transform(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Input data to be transformed.
`opts.fit_params?`	`any`	Parameters to pass to the fit method of the estimator.
`opts.y?`	`ArrayLike`[]	Targets for supervised learning.

Returns

Promise<ArrayLike>

Defined in: generated/pipeline/FeatureUnion.ts:171 (opens in a new tab)

get_feature_names_out()

Get output feature names for transformation.

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/pipeline/FeatureUnion.ts:216 (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/pipeline/FeatureUnion.ts:254 (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/pipeline/FeatureUnion.ts:67 (opens in a new tab)

set_output()

Set the output container when "transform" and "fit\_transform" are called.

set\_output will set the output of all estimators in transformer\_list.

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/pipeline/FeatureUnion.ts:291 (opens in a new tab)

transform()

Transform X separately by each transformer, concatenate results.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Input data to be transformed.

Returns

Promise<ArrayLike>

Defined in: generated/pipeline/FeatureUnion.ts:324 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/pipeline/FeatureUnion.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/pipeline/FeatureUnion.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/pipeline/FeatureUnion.ts:23 (opens in a new tab)

id

string

Defined in: generated/pipeline/FeatureUnion.ts:20 (opens in a new tab)

opts

any

Defined in: generated/pipeline/FeatureUnion.ts:21 (opens in a new tab)

Accessors

named_transformers

Dictionary-like object, with the following attributes. Read-only attribute to access any transformer parameter by user given name. Keys are transformer names and values are transformer parameters.