h2oensemble Error in value[[3L]](cond) : argument

Question

While trying to run the example on H2OEnsemble found on http://learn.h2o.ai/content/tutorials/ensembles-stacking/index.html from within Rstudio, I encounter the following error:

Error in value[3L] : argument "training_frame" must be a valid H2O H2OFrame or id

after defining the ensemble

fit <- h2o.ensemble(x = x, y = y, 
                    training_frame = train, 
                     family = family, 
                     learner = learner, 
                     metalearner = metalearner,
                     cvControl = list(V = 5, shuffle = TRUE))

I installed the latest version of both h2o and h2oEnsemble but the issue remains. I have read here `h2o.cbind` accepts only of H2OFrame objects - R that the naming convention in h2o changed over time, but I assume by installing the latest version of both this should not be any longer the issue.

Any suggestions?

library(readr)
library(h2oEnsemble)  # Requires version >=0.0.4 of h2oEnsemble
library(cvAUC)  # Used to calculate test set AUC (requires version >=1.0.1 of cvAUC)
localH2O <-  h2o.init(nthreads = -1)  # Start an H2O cluster with nthreads = num cores on your machine





# Import a sample binary outcome train/test set into R
train <- h2o.importFile("http://www.stat.berkeley.edu/~ledell/data/higgs_10k.csv")
test <- h2o.importFile("http://www.stat.berkeley.edu/~ledell/data/higgs_test_5k.csv")
y <- "C1"
x <- setdiff(names(train), y)
family <- "binomial"

#For binary classification, response should be a factor
train[,y] <- as.factor(train[,y])  
test[,y] <- as.factor(test[,y])


# Specify the base learner library & the metalearner
learner <- c("h2o.glm.wrapper", "h2o.randomForest.wrapper", 
               "h2o.gbm.wrapper", "h2o.deeplearning.wrapper")
metalearner <- "h2o.deeplearning.wrapper"


# Train the ensemble using 5-fold CV to generate level-one data
# More CV folds will take longer to train, but should increase performance
fit <- h2o.ensemble(x = x, y = y, 
                    training_frame = train, 
                    family = family, 
                    learner = learner, 
                    metalearner = metalearner,
                    cvControl = list(V = 5, shuffle = TRUE))

Answer 1

This bug was recently introduced by a bulk find/replace change of a class name made to the h2o R code. The change was inadvertently applied to the ensemble code folder as well (where we currently have manual instead of automatic tests -- soon to be automatic to prevent this sort of thing). I've fixed the bug.

To fix, reinstall the h2oEnsemble package from GitHub:

library(devtools)
install_github("h2oai/h2o-3/h2o-r/ensemble/h2oEnsemble-package")

Thanks for the report! For a quicker response, post bugs and questions here: https://groups.google.com/forum/#!forum/h2ostream