# 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.

Parameters
methodstr, (default=’yeo-johnson’)

The power transform method. Available methods are:

standardizeboolean, default=True

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

copyboolean, optional, default=True

Set to False to perform inplace computation during transformation.

Attributes
lambdas_array of float, shape (n_features,)

The parameters of the power transformation for the selected features.

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

Rf3e1504535de-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).

Rf3e1504535de-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(self, X[, y]) Estimate the optimal parameter lambda for each feature. fit_transform(self, X[, y]) Fit to data, then transform it. get_params(self[, deep]) Get parameters for this estimator. inverse_transform(self, X) Apply the inverse power transformation using the fitted lambdas. set_params(self, \*\*params) Set the parameters of this estimator. transform(self, X) Apply the power transform to each feature using the fitted lambdas.
__init__(self, method='yeo-johnson', standardize=True, copy=True)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, 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, shape (n_samples, n_features)

The data used to estimate the optimal transformation parameters.

yIgnored
Returns
selfobject
fit_transform(self, X, y=None)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

Returns
X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

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

Returns
paramsmapping of string to any

Parameter names mapped to their values.

inverse_transform(self, 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, shape (n_samples, n_features)

The transformed data.

Returns
Xarray-like, shape (n_samples, n_features)

The original data

set_params(self, **params)[source]

Set the parameters of this estimator.

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

Returns
self
transform(self, X)[source]

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

Parameters
Xarray-like, shape (n_samples, n_features)

The data to be transformed using a power transformation.

Returns
X_transarray-like, shape (n_samples, n_features)

The transformed data.