Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

Building GBM Model in R and Exporting POJO and MOJO Models [Code Snippet]

DZone's Guide to

Building GBM Model in R and Exporting POJO and MOJO Models [Code Snippet]

Get the script that you need so that you can get started building a GBM model in R and then continue to export the POJO and MOJO models.

· Big Data Zone
Free Resource

Learn best practices according to DataOps. Download the free O'Reilly eBook on building a modern Big Data platform.

Before we get started building a GBM model in R and then exporting the POJO and MOJO models, be sure to check out the training here and to check out the test here

Now, here is the script to build the GBM grid model and to export the MOJO model:

library(h2o)
h2o.init()

# Importing Dataset
trainfile <- file.path("/Users/avkashchauhan/learn/adult_2013_train.csv.gzult_2013_train <- h2o.importFile(trainfile, destination_frame = "adult_2013_train")
testfile <- file.path("/Users/avkashchauhan/learn/adult_2013_test.csv.gzult_2013_test <- h2o.importFile(testfile, destination_frame = "adult_2013_test")

# Display Dataset
adult_2013_train
adult_2013_test

# Feature Engineering
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")

# Building GBM Model:
log_wagp_gbm_grid <- h2o.gbm(x = predset,
 y = "LOG_WAGP",
 training_frame = adult_2013_train,
 model_id = "GBMModel",
 distribution = "gaussian",
 max_depth = 5,
 ntrees = 110,
 validation_frame = adult_2013_test)

log_wagp_gbm_grid

# Prediction 
h2o.predict(log_wagp_gbm_grid, adult_2013_test)

# Download POJO Model:
h2o.download_pojo(log_wagp_gbm_grid, "/Users/avkashchauhan/learn", get_genmodel_jar = TRUE)

# Download MOJO model:
h2o.download_mojo(log_wagp_gbm_grid, "/Users/avkashchauhan/learn", get_genmodel_jar = TRUE)

You will see GBM_model.java (in the POJO Model) and GBM_model.zip (in the MOJO model) at the location where you will save these models.

That's it — enjoy!

Find the perfect platform for a scalable self-service model to manage Big Data workloads in the Cloud. Download the free O'Reilly eBook to learn more.

Topics:
r ,pojo ,mojo ,bgm ,big data

Published at DZone with permission of Avkash Chauhan, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}