Score: POJO¶

H2O has the ability to code in any front-end API and export the model as a POJO (Plain Old Java Object). This provide the flexibility to use the model outside of H2O, either to run as a standalone or by integrating the Java Object into a platform like Hadoop’s Storm. The following walkthrough describes the steps required to export a model object via the command line and to score it using a sample class object.

The working example or unit test for scoring using the Java code is available on github.

Walk-through¶

Step 1

To export an H2O instance sitting on localhost:54321 by default and a GBM model with 50 trees, run the following commands to grab the h2o-model jar file and the Java code for the example model GBM_a2647515ded07d5b710c82015a6842a9. We recommend creating a new directory for each model.

$ mkdir GBM_a2647515ded07d5b710c82015a6842a9
$ cd GBM_a2647515ded07d5b710c82015a6842a9
$ curl http://localhost:54321/h2o-model.jar > h2o-model.jar
$ curl http://localhost:54321/2/GBMModelView.java?_modelKey=GBM_a2647515ded07d5b710c82015a6842a9 >
GBM_a2647515ded07d5b710c82015a6842a9.java

Step 2

Download from git the PredictCSV class object that will be used to compile the model object. You can write your own script; the one available in git is a working example that is tested on all the builds at h2o.ai. It uses four arguments:

- -header

specify if the input data set has headers

- -model

model key name used to score on the input file

- -input

the input data that will be scored

- -output

the resulting output csv file with all the s scores for each entry of the input data

Note: Make sure that both the PredictCSV.java object and the original dataset are located in the directory you created in step 1.

Step 3

Next, set up a Java instance to compile the model object using PredictCSV.java, which should generate over 50 tree class objects.

$ javac -cp h2o-model.jar -J-Xmx2g -J-XX:MaxPermSize=256m PredictCSV.java
GBM_a2647515ded07d5b710c82015a6842a9.java

Step 4

Finally, submit the testing data for scoring by running the following command:

$ java -ea -cp .:./h2o-model.jar -Xmx4g -XX:MaxPermSize=256m -XX:ReservedCodeCacheSize=256m
PredictCSV --header --model GBM_a2647515ded07d5b710c82015a6842a9 --input iris_test.csv
--output out_pojo.csv

Generic command example:

$ java -ea -cp .:./h2o-model.jar -Xmx4g -XX:MaxPermSize=256m -XX:ReservedCodeCacheSize=256m
PredictCSV --header --model <model key> --input <path to input data>
--output <path to output csv>