sklearn.preprocessing.power_transform

sklearn.preprocessing.power_transform(X, method=’warn’, standardize=True, copy=True)[source]

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, power_transform 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:
X : array-like, shape (n_samples, n_features)

The data to be transformed using a power transformation.

method : str

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

The default method will be changed from ‘box-cox’ to ‘yeo-johnson’ in version 0.23. To suppress the FutureWarning, explicitly set the parameter.

standardize : boolean, default=True

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

copy : boolean, optional, default=True

Set to False to perform inplace computation during transformation.

Returns:
X_trans : array-like, shape (n_samples, n_features)

The transformed data.

See also

PowerTransformer
Equivalent transformation with the Transformer API (e.g. as part of a preprocessing sklearn.pipeline.Pipeline).
quantile_transform
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](1, 2) 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](1, 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 power_transform
>>> data = [[1, 2], [3, 2], [4, 5]]
>>> print(power_transform(data, method='box-cox'))  
[[-1.332... -0.707...]
 [ 0.256... -0.707...]
 [ 1.076...  1.414...]]