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h2o

h2o.mojo_predict_csv

H2O Prediction from R without having H2O running

Description

Provides the method h2o.mojo_predict_csv with which you can predict a MOJO model from R.

Usage

h2o.mojo_predict_csv(
  input_csv_path,
  mojo_zip_path,
  output_csv_path = NULL,
  genmodel_jar_path = NULL,
  classpath = NULL,
  java_options = NULL,
  verbose = F,
  setInvNumNA = F
)

Arguments

input_csv_path

Path to input CSV file.
mojo_zip_path

Path to MOJO zip downloaded from H2O.
output_csv_path

Optional, path to the output CSV file with computed predictions. If NULL (default), then predictions will be saved as prediction.csv in the same folder as the MOJO zip.
genmodel_jar_path

Optional, path to genmodel jar file. If NULL (default) then the h2o-genmodel.jar in the same folder as the MOJO zip will be used.
classpath

Optional, specifies custom user defined classpath which will be used when scoring. If NULL (default) then the default classpath for this MOJO model will be used.
java_options

Optional, custom user defined options for Java. By default '-Xmx4g -XX:ReservedCodeCacheSize=256m' is used.
verbose

Optional, if TRUE, then additional debug information will be printed. FALSE by default.
setInvNumNA

Optional, if TRUE, then then for an string that cannot be parsed into a number an N/A value will be produced, if false the command will fail. FALSE by default.

Value

Returns a data.frame containing computed predictions

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

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