Open tkasci opened 2 years ago
Hi, you might need to modify the SL.bartMachine
wrapper to add the serialize = TRUE
argument and retrain the SL model. Here is an example new wrapper (and then use SL.mybartMachine
in the SL.library):
SL.mybartMachine <- function(Y, X, newX, family, obsWeights, id, num_trees = 50,
num_burn_in = 250, verbose = FALSE, alpha = 0.95, beta = 2, k = 2,
q = 0.9, nu = 3, num_iterations_after_burn_in = 1000, ...)
{
require("bartMachine")
model = bartMachine::bartMachine(X = X,
y = Y,
num_trees = num_trees,
num_burn_in = num_burn_in,
verbose = verbose,
alpha = alpha,
beta = beta,
k = k,
q = q,
nu = nu,
num_iterations_after_burn_in = num_iterations_after_burn_in,
serialize = TRUE)
pred <- predict(model, newX)
fit <- list(object = model)
class(fit) <- c("SL.bartMachine")
out <- list(pred = pred, fit = fit)
return(out)
}
Hi, I built an SL model and saved it to disk. However, it contains a BART learner, so I get this error when trying to do anything with it:
What would be the best way to fix this? Is there a way to pass options to individual commands via SuperLearner?