Documentation
Classes
PowerTransformer

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.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new PowerTransformer(opts?: object): PowerTransformer;

Parameters

NameTypeDescription
opts?object-
opts.copy?booleanSet to false to perform inplace computation during transformation. Default Value true
opts.method?"yeo-johnson" | "box-cox"The power transform method. Available methods are: Default Value 'yeo-johnson'
opts.standardize?booleanSet to true to apply zero-mean, unit-variance normalization to the transformed output. Default Value true

Returns

PowerTransformer

Defined in: generated/preprocessing/PowerTransformer.ts:31 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/preprocessing/PowerTransformer.ts:29 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/preprocessing/PowerTransformer.ts:28 (opens in a new tab)

_py

PythonBridge

Defined in: generated/preprocessing/PowerTransformer.ts:27 (opens in a new tab)

id

string

Defined in: generated/preprocessing/PowerTransformer.ts:24 (opens in a new tab)

opts

any

Defined in: generated/preprocessing/PowerTransformer.ts:25 (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/preprocessing/PowerTransformer.ts:418 (opens in a new tab)

lambdas_

The parameters of the power transformation for the selected features.

Signature

lambdas_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/preprocessing/PowerTransformer.ts:364 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/preprocessing/PowerTransformer.ts:391 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/preprocessing/PowerTransformer.ts:57 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/preprocessing/PowerTransformer.ts:61 (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/preprocessing/PowerTransformer.ts:114 (opens in a new tab)

fit()

Estimate the optimal parameter lambda for each feature.

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

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The data used to estimate the optimal transformation parameters.
opts.y?anyIgnored.

Returns

Promise<any>

Defined in: generated/preprocessing/PowerTransformer.ts:133 (opens in a new tab)

fit_transform()

Fit PowerTransformer to X, then transform X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The data used to estimate the optimal transformation parameters and to be transformed using a power transformation.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<ArrayLike[]>

Defined in: generated/preprocessing/PowerTransformer.ts:173 (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

NameTypeDescription
optsobject-
opts.input_features?anyInput features.

Returns

Promise<any>

Defined in: generated/preprocessing/PowerTransformer.ts:215 (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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/preprocessing/PowerTransformer.ts:70 (opens in a new tab)

inverse_transform()

Apply the inverse power transformation using the fitted lambdas.

The inverse of the Box-Cox transformation is given by:

Signature

inverse_transform(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The transformed data.

Returns

Promise<ArrayLike[]>

Defined in: generated/preprocessing/PowerTransformer.ts:255 (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

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/preprocessing/PowerTransformer.ts:294 (opens in a new tab)

transform()

Apply the power transform to each feature using the fitted lambdas.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The data to be transformed using a power transformation.

Returns

Promise<ArrayLike[]>

Defined in: generated/preprocessing/PowerTransformer.ts:329 (opens in a new tab)