Class: PowerTransformer

Apply a power transform featurewise to make data more Gaussian-like.

Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling issues related to heteroscedasticity (non-constant variance), or other situations where normality is desired.

Currently, PowerTransformer supports the Box-Cox transform and the Yeo-Johnson transform. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood.

Box-Cox requires input data to be strictly positive, while Yeo-Johnson supports both positive or negative data.

By default, zero-mean, unit-variance normalization is applied to the transformed data.

For an example visualization, refer to Compare PowerTransformer with other scalers. To see the effect of Box-Cox and Yeo-Johnson transformations on different distributions, see: Map data to a normal distribution.

Constructors

new PowerTransformer()

new PowerTransformer(opts?): PowerTransformer

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.copy`?	`boolean`	Set to `false` to perform inplace computation during transformation.
`opts.method`?	`"yeo-johnson"` \| `"box-cox"`	The power transform method. Available methods are:
`opts.standardize`?	`boolean`	Set to `true` to apply zero-mean, unit-variance normalization to the transformed output.

Returns PowerTransformer

Defined in generated/preprocessing/PowerTransformer.ts:33

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/preprocessing/PowerTransformer.ts:31
`_isInitialized`	`boolean`	`false`	generated/preprocessing/PowerTransformer.ts:30
`_py`	`PythonBridge`	`undefined`	generated/preprocessing/PowerTransformer.ts:29
`id`	`string`	`undefined`	generated/preprocessing/PowerTransformer.ts:26
`opts`	`any`	`undefined`	generated/preprocessing/PowerTransformer.ts:27

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/preprocessing/PowerTransformer.ts:448

lambdas_

Get Signature

get lambdas_(): Promise<any[]>

The parameters of the power transformation for the selected features.

Returns Promise<any[]>

Defined in generated/preprocessing/PowerTransformer.ts:394

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/preprocessing/PowerTransformer.ts:421

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/preprocessing/PowerTransformer.ts:59

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/preprocessing/PowerTransformer.ts:113

fit()

fit(opts): Promise<any>

Estimate the optimal parameter lambda for each feature.

The optimal lambda parameter for minimizing skewness is estimated on each feature independently using maximum likelihood.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The data used to estimate the optimal transformation parameters.
`opts.y`?	`any`	Ignored.

Returns Promise<any>

Defined in generated/preprocessing/PowerTransformer.ts:132

fit_transform()

fit_transform(opts): Promise<ArrayLike[]>

Fit PowerTransformer to X, then transform X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The data used to estimate the optimal transformation parameters and to be transformed using a power transformation.
`opts.y`?	`any`	Not used, present for API consistency by convention.