PowerTransformer (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.
For an example visualization, refer to Compare PowerTransformer with other scalers. To see the effect of Box-Cox and Yeo-Johnson transformations on different distributions, see:Map data to a normal distribution.
Read more in the User Guide.
Added 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.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
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.
References
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...]]
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_featuresisNone, thenfeature_names_in_is used as feature names in. Iffeature_names_in_is not defined, then the following input feature names are generated:["x0", "x1", ..., "x(n_features_in_ - 1)"]. - If
input_featuresis an array-like, theninput_featuresmust matchfeature_names_in_iffeature_names_in_is defined.
Returns:
feature_names_outndarray of str objects
Same as input features.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
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.
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 APIfor an example on how to use the API.
Parameters:
transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
"default": Default output format of a transformer"pandas": DataFrame output"polars": Polars outputNone: Transform configuration is unchanged
Added in version 1.4: "polars" option was added.
Returns:
selfestimator instance
Estimator instance.
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.
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.