ck37/superlearner-guide

SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.

superlearner-guide's Issues

SL.predict annoying failure...

Just as it took the entire math/philosophy world something like 15 years to realize their definition of a set failed miserably due to Russell's paradox, this bug has gone on way too long...

`# This should not happen with SL.predict with a failed algorithm

set up easy regression data

X = rnorm(100)
Y = 5*X + rnorm(100) + 2
X = data.frame(X = X)

set up bad algorithm

SL.nothing = function(x) {x}

declare the library

SL.library = c("SL.glm", "SL.nothing")

call superlearner with bad alg in library

fit = SuperLearner(Y, X, family = gaussian, SL.library = SL.library)

SL.predict breaks

preds = fit$SL.predict[1:100]

library.predict still fine

fit$library.predict

And this work around breaks if only one learner works

preds = fit$library.predict[,fit$coef != 0] %*% fit$coef[fit$coef != 0]

So I code the following--maybe dumb and inefficient but works

if (length(fit$coef[fit$coef != 0]) == 1) {
preds = fit$library.predict[,fit$coef != 0]
} else {
preds = fit$library.predict[,fit$coef != 0] %*% fit$coef[fit$coef != 0]
}

preds
`

include HAL maybe, it works and is stellar...

I have been using David's SL wrapper for hal that comes with his package:

if (!require(devtools)) install.packages(devtools)
devtools::install_github("benkeser/halplus")

It is a fabulous wrapper so maybe we can just include it with a warning in the vignette that one should time it first before putting it in the library. Anyway, I'm sure that HAL with screeners will also be excellent even if dimension is high.