sklearn.preprocessing.PowerTransformer

class sklearn.preprocessing.PowerTransformer(method='yeo-johnson', *, standardize=True, copy=True)[source]

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.

New in version 0.20.

Parameters:
method{‘yeo-johnson’, ‘box-cox’}, default=’yeo-johnson’

The power transform method. Available methods are:

  • ‘yeo-johnson’ [1], works with positive and negative values

  • ‘box-cox’ [2], only works with strictly positive values

standardizebool, default=True

Set to True to apply zero-mean, unit-variance normalization to the transformed output.

copybool, default=True

Set to False to perform inplace computation during transformation.

Attributes:
lambdas_ndarray of float of shape (n_features,)

The parameters of the power transformation for the selected features.

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

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

New in version 1.0.

See also

power_transform

Equivalent function without the estimator API.

QuantileTransformer

Maps data to a standard normal distribution with the parameter output_distribution='normal'.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.

References

[1]

I.K. Yeo and R.A. Johnson, “A new family of power transformations to improve normality or symmetry.” Biometrika, 87(4), pp.954-959, (2000).

[2]

G.E.P. Box and D.R. Cox, “An Analysis of Transformations”, Journal of the Royal Statistical Society B, 26, 211-252 (1964).

Examples

>>> import numpy as np
>>> from sklearn.preprocessing import PowerTransformer
>>> pt = PowerTransformer()
>>> data = [[1, 2], [3, 2], [4, 5]]
>>> print(pt.fit(data))
PowerTransformer()
>>> print(pt.lambdas_)
[ 1.386... -3.100...]
>>> print(pt.transform(data))
[[-1.316... -0.707...]
 [ 0.209... -0.707...]
 [ 1.106...  1.414...]]

Methods

fit(X[, y])

Estimate the optimal parameter lambda for each feature.

fit_transform(X[, y])

Fit PowerTransformer to X, then transform X.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X)

Apply the inverse power transformation using the fitted lambdas.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Apply the power transform to each feature using the fitted lambdas.
fit(X, y=None)[source]

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:
Xarray-like of shape (n_samples, n_features)

The data used to estimate the optimal transformation parameters.

yNone

Ignored.

Returns:
selfobject

Fitted transformer.

fit_transform(X, y=None)[source]

Fit PowerTransformer to X, then transform X.

Parameters:
Xarray-like of shape (n_samples, n_features)

The data used to estimate the optimal transformation parameters and to be transformed using a power transformation.

yIgnored

Not used, present for API consistency by convention.

Returns:
X_newndarray of shape (n_samples, n_features)

Transformed data.

get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Same as input features.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

inverse_transform(X)[source]

Apply the inverse power transformation using the fitted lambdas.

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

if lambda_ == 0:
    X = exp(X_trans)
else:
    X = (X_trans * lambda_ + 1) ** (1 / lambda_)

The inverse of the Yeo-Johnson transformation is given by:

if X >= 0 and lambda_ == 0:
    X = exp(X_trans) - 1
elif X >= 0 and lambda_ != 0:
    X = (X_trans * lambda_ + 1) ** (1 / lambda_) - 1
elif X < 0 and lambda_ != 2:
    X = 1 - (-(2 - lambda_) * X_trans + 1) ** (1 / (2 - lambda_))
elif X < 0 and lambda_ == 2:
    X = 1 - exp(-X_trans)
Parameters:
Xarray-like of shape (n_samples, n_features)

The transformed data.

Returns:
Xndarray of shape (n_samples, n_features)

The original data.

set_output(*, transform=None)[source]

Set output container.

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

Parameters:
transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

  • "default": Default output format of a transformer

  • "pandas": DataFrame output

  • None: Transform configuration is unchanged

Returns:
selfestimator instance

Estimator instance.

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

transform(X)[source]

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

Parameters:
Xarray-like of shape (n_samples, n_features)

The data to be transformed using a power transformation.

Returns:
X_transndarray of shape (n_samples, n_features)

The transformed data.

Examples using sklearn.preprocessing.PowerTransformer

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers
Map data to a normal distribution

Map data to a normal distribution

Map data to a normal distribution