h2o: h2o.download_pojo – R documentation

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h2o

h2o.download_pojo

Download the Scoring POJO (Plain Old Java Object) of an H2O Model

Description

Download the Scoring POJO (Plain Old Java Object) of an H2O Model

Usage

h2o.download_pojo(
  model,
  path = NULL,
  getjar = NULL,
  get_jar = TRUE,
  jar_name = ""
)

Arguments

`model`	An H2OModel
`path`	The path to the directory to store the POJO (no trailing slash). If NULL, then print to to console. The file name will be a compilable java file name.
`getjar`	(DEPRECATED) Whether to also download the h2o-genmodel.jar file needed to compile the POJO. This argument is now called 'get_jar'.
`get_jar`	Whether to also download the h2o-genmodel.jar file needed to compile the POJO
`jar_name`	Custom name of genmodel jar.

Value

If path is NULL, then pretty print the POJO to the console. Otherwise save it to the specified directory and return POJO file name.

Examples

## Not run: 
library(h2o)
h <- h2o.init()
fr <- as.h2o(iris)
my_model <- h2o.gbm(x = 1:4, y = 5, training_frame = fr)

h2o.download_pojo(my_model)  # print the model to screen
# h2o.download_pojo(my_model, getwd())  # save the POJO and jar file to the current working
#                                         directory, NOT RUN
# h2o.download_pojo(my_model, getwd(), get_jar = FALSE )  # save only the POJO to the current
#                                                           working directory, NOT RUN
h2o.download_pojo(my_model, getwd())  # save to the current working directory

## End(Not run)

h2o

R Interface for the 'H2O' Scalable Machine Learning Platform

v3.32.1.2

Apache License (== 2.0)

Authors

Erin LeDell [aut, cre], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Wendy Wong [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], Tomas Fryda [ctb], H2O.ai [cph, fnd]

Initial release

2021-04-29