Predicting on new data using locally weighted regression (LOESS/LOWESS)

How to fit a locally weighted regression in python so that it can be used to predict on new data?



There is statsmodels.nonparametric.smoothers_lowess.lowess, but it returns the estimates only for the original data set; so it seems to only do fit and predict together, rather than separately as I expected.



scikit-learn always has a fit method that allows the object to be used later on new data with predict; but it doesn't implement lowess.