sklearn.preprocessing.PowerTransformer — scikit-learn 0.20.4 documentation (original) (raw)
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: | method : str, (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 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. |
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| Attributes: | lambdas_ : array of float, shape (n_features,) The parameters of the power transformation for the selected features. |
See also
Equivalent function without the estimator API.
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). |
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| [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). |
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Examples
import numpy as np from sklearn.preprocessing import PowerTransformer pt = PowerTransformer() data = [[1, 2], [3, 2], [4, 5]] print(pt.fit(data)) PowerTransformer(copy=True, method='yeo-johnson', standardize=True) 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. |
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| fit_transform(X[, y]) | |
| get_params([deep]) | Get parameters for this estimator. |
| inverse_transform(X) | Apply the inverse power transformation using the fitted lambdas. |
| set_params(**params) | Set the parameters of this estimator. |
| transform(X) | Apply the power transform to each feature using the fitted lambdas. |
__init__(method='yeo-johnson', standardize=True, copy=True)[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: | X : array-like, shape (n_samples, n_features) The data used to estimate the optimal transformation parameters. y : Ignored |
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| Returns: | self : object |
get_params(deep=True)[source]¶
Get parameters for this estimator.
| Parameters: | deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
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: | X : array-like, shape (n_samples, n_features) The transformed data. |
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| Returns: | X : array-like, shape (n_samples, n_features) The original data |
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 |
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Apply the power transform to each feature using the fitted lambdas.
| Parameters: | X : array-like, shape (n_samples, n_features) The data to be transformed using a power transformation. |
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| Returns: | X_trans : array-like, shape (n_samples, n_features) The transformed data. |