pyts.preprocessing.PowerTransformer

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

Apply a power transform sample-wise 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.

Parameters:
method : ‘yeo-johnson’ or ‘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
standardize : boolean (default = True)

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

Notes

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

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 pyts.preprocessing import PowerTransformer
>>> X = [[1, 3, 4], [2, 2, 5]]
>>> pt = PowerTransformer()
>>> print(pt.transform(X))
[[-1.316...  0.209...  1.106...]
 [-0.707... -0.707...  1.414...]]

Methods

__init__([method, standardize]) Initialize self.
fit([X, y]) Pass.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X) Transform the data.
__init__(method='yeo-johnson', standardize=True)[source]

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

fit(X=None, y=None)[source]

Pass.

Parameters:
X

Ignored

y

Ignored
Returns:
self : object
fit_transform(X, y=None, **fit_params)

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:
X : array-like, shape = (n_samples, n_timestamps)

Univariate time series.

y : None or array-like, shape = (n_samples,) (default = None)

Target values (None for unsupervised transformations).

**fit_params : dict

Additional fit parameters.
Returns:
X_new : array

Transformed array.
get_params(deep=True)

Get parameters for this estimator.

Parameters:
deep : bool, default=True

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

Parameter names mapped to their values.
set_params(**params)

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:
**params : dict

Estimator parameters.
Returns:
self : estimator instance

Estimator instance.
transform(X)[source]

Transform the data.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Data to transform.
Returns:
X_new : array-like, shape = (n_samples, n_timestamps)

Transformed data.

Examples using pyts.preprocessing.PowerTransformer

Transformers

Transformers

Transformers