library(h2o)

# Download the data into the pums2013 directory if necessary.

pumsdir <- "pums2013"

if (! file.exists(pumsdir)) {

dir.create(pumsdir)

}

trainfile <- file.path(pumsdir, "adult_2013_train.csv.gz")

if (! file.exists(trainfile)) {

download.file("http://h2o-training.s3.amazonaws.com/pums2013/adult_2013_train.csv.gz", trainfile)

}

testfile <- file.path(pumsdir, "adult_2013_test.csv.gz")

if (! file.exists(testfile)) {

download.file("http://h2o-training.s3.amazonaws.com/pums2013/adult_2013_test.csv.gz", testfile)

}

# Run the example.

h2o.init(nthreads = -1)

adult_2013_train <- h2o.importFile(trainfile, destination_frame = "adult_2013_train")

adult_2013_test <- h2o.importFile(testfile, destination_frame = "adult_2013_test")

dim(adult_2013_train)

dim(adult_2013_test)

actual_log_wagp <- h2o.assign(adult_2013_test[, "LOG_WAGP"], key = "actual_log_wagp")

for (j in c("COW", "SCHL", "MAR", "INDP", "RELP", "RAC1P", "SEX", "POBP")) {

adult_2013_train[[j]] <- as.factor(adult_2013_train[[j]])

adult_2013_test[[j]] <- as.factor(adult_2013_test[[j]])

}

predset <- c("RELP", "SCHL", "COW", "MAR", "INDP", "RAC1P", "SEX", "POBP", "AGEP",

"WKHP", "LOG_CAPGAIN", "LOG_CAPLOSS")

# Train the model.

log_wagp_gbm <- h2o.gbm(x = predset,

y = "LOG_WAGP",

training_frame = adult_2013_train,

model_id = "GBMModel",

distribution = "gaussian",

max_depth = 5,

ntrees = 110,

learn_rate = 0.25,

validation_frame = adult_2013_test,

importance = TRUE)

log_wagp_gbm

# Do in-H2O predictions for reference. This is not using the POJO.

h2o.predict(log_wagp_gbm, adult_2013_test)

h2o.mse(h2o.performance(log_wagp_gbm, adult_2013_test))

# Export the POJO.

tmpdir_name <- "generated_model"

dir.create(tmpdir_name)

h2o.download_pojo(log_wagp_gbm, tmpdir_name)