Reverse Box-Cox transformation

Question

I am using SciPy's boxcox function to perform a Box-Cox transformation on a continuous variable.

from scipy.stats import boxcox import numpy as np y = np.random.random(100) y_box, lambda_ = ss.boxcox(y + 1) # Add 1 to be able to transform 0 values 

Then, I fit a statistical model to predict the values of this Box-Cox transformed variable. The model predictions are in the Box-Cox scale and I want to transform them to the original scale of the variable.

from sklearn.ensemble import RandomForestRegressor rf = RandomForestRegressor() X = np.random.random((100, 100)) rf.fit(X, y_box) pred_box = rf.predict(X) 

However, I can't find a SciPy function that performs a reverse Box-Cox transformation given transformed data and lambda. Is there such a function? I coded an inverse transformation for now.

pred_y = np.power((y_box * lambda_) + 1, 1 / lambda_) - 1 

Answer 1

SciPy has added an inverse Box-Cox transformation.

https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.inv_boxcox.html

scipy.special.inv_boxcox scipy.special.inv_boxcox(y, lmbda) =

Compute the inverse of the Box-Cox transformation.

Find x such that:

y = (x**lmbda - 1) / lmbda  if lmbda != 0     log(x)                  if lmbda == 0 

Parameters: y : array_like

Data to be transformed.

lmbda : array_like

Power parameter of the Box-Cox transform.

Returns:
x : array

Transformed data.

Notes

New in version 0.16.0.

Example:

from scipy.special import boxcox, inv_boxcox y = boxcox([1, 4, 10], 2.5) inv_boxcox(y, 2.5)  output: array([1., 4., 10.])