if (FALSE) {
library(h2o)
h2o.init()
iris <- as.h2o(iris)
ntrees_opts = c(1, 5)
learn_rate_opts = c(0.1, 0.01)
size_of_hyper_space = length(ntrees_opts) * length(learn_rate_opts)
hyper_parameters = list(ntrees = ntrees_opts, learn_rate = learn_rate_opts)
# Tempdir is chosen arbitrarily. May be any valid folder on an H2O-supported filesystem.
baseline_grid <- h2o.grid("gbm", grid_id="gbm_grid_test", x=1:4, y=5, training_frame=iris,
hyper_params = hyper_parameters, export_checkpoints_dir = tempdir())
# Remove everything from the cluster or restart it
h2o.removeAll()
grid <- h2o.loadGrid(paste0(tempdir(),"/",baseline_grid@grid_id))
}
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