POJO Quick Start

This document describes how to use H2O’s Generated POJO Models to make predictions.

Note: Also see the Generated POJO Model Javadoc.

Note: POJOs are not supported for source files larger than 1G. For more information, refer to the FAQ below.

What is a POJO?

H2O allows you to convert the models you have built to a Plain Old Java Object (POJO).

POJOs allow users to build a model using H2O and then deploy the model to score in real-time.

  1. Start H2O in terminal window #1:
$ java -jar h2o.jar
  1. Build a model using your web browser:

    1. Go to http://localhost:54321
    2. Click View Example Flows near the right edge of the screen.
    3. Click GBM_Airlines_Classification.flow
    4. If a confirmation prompt appears asking you to “Load Notebook”, click it.
    5. From the “Flow” menu choose the “Run all cells” option.
    6. Scroll down and find the “Model” cell in the notebook. Click on the Download POJO button as shown in the following screenshot:

    Note: The instructions below assume that the POJO model was downloaded to the “Downloads” folder.

  2. Download model pieces in a new terminal window - H2O must still be running in terminal window #1:

    $ mkdir experiment
    $ cd experiment
    $ mv ~/Downloads/gbm_pojo_test.java .
    $ curl http://localhost:54321/3/h2o-genmodel.jar > h2o-genmodel.jar
  3. Create your main program in terminal window #2 by creating a new file called main.java (vim main.java) with the following contents:

    import java.io.*;
    import hex.genmodel.easy.RowData;
    import hex.genmodel.easy.EasyPredictModelWrapper;
    import hex.genmodel.easy.prediction.*;
    public class main {
      private static String modelClassName = "gbm_pojo_test";
      public static void main(String[] args) throws Exception {
        hex.genmodel.GenModel rawModel;
        rawModel = (hex.genmodel.GenModel) Class.forName(modelClassName).newInstance();
        EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel);
        RowData row = new RowData();
        row.put("Year", "1987");
        row.put("Month", "10");
        row.put("DayofMonth", "14");
        row.put("DayOfWeek", "3");
        row.put("CRSDepTime", "730");
        row.put("UniqueCarrier", "PS");
        row.put("Origin", "SAN");
        row.put("Dest", "SFO");
        BinomialModelPrediction p = model.predictBinomial(row);
        System.out.println("Label (aka prediction) is flight departure delayed: " + p.label);
        System.out.print("Class probabilities: ");
        for (int i = 0; i < p.classProbabilities.length; i++) {
          if (i > 0) {
  4. Compile and run in terminal window 2:

    $ javac -cp h2o-genmodel.jar -J-Xmx2g -J-XX:MaxPermSize=128m gbm_pojo_test.java main.java
    $ java -cp .:h2o-genmodel.jar main

The following output displays:

Label (aka prediction) is flight departure delayed: YES
Class probabilities: 0.4790490513429604,0.5209509486570396

Extracting Models from H2O

Generated models can be extracted from H2O in the following ways:

From the H2O Flow Web UI

When viewing a model, click the Download POJO button at the top of the model cell, as shown in the example in the Quick start section. You can also preview the POJO inside Flow, but it will only show the first thousand lines or so in the web browser, truncating large models.

From R and Python

The following code snippet shows an example of H2O building a model and downloading its corresponding POJO from an R script and a Python script.

path = system.file("extdata", "prostate.csv", package = "h2o")
h2o_df = h2o.importFile(path)
h2o_df$CAPSULE = as.factor(h2o_df$CAPSULE)
model = h2o.glm(y = "CAPSULE",
                x = c("AGE", "RACE", "PSA", "GLEASON"),
                training_frame = h2o_df,
                family = "binomial")
import h2o
path = h2o.system_file("prostate.csv")
h2o_df = h2o.import_file(path)
h2o_df['CAPSULE'] = h2o_df['CAPSULE'].asfactor()
model = h2o.glm(y = "CAPSULE",
                x = ["AGE", "RACE", "PSA", "GLEASON"],
                training_frame = h2o_df,
                family = "binomial")

Use Cases

See the section on Productionizing H2O.


  • How do I score new cases in real-time in a production environment?

    If you’re using the Flow Web UI, click the Preview POJO button for your model. This produces a Java class with methods that you can reference and use in your production app.

  • What kind of technology would I need to use?

    Anything that runs in a JVM. The POJO is a standalone Java class with no dependencies on the full H2O stack (only the h2o-genmodel.jar file, which defines the POJO interface).

  • How should I format my data before calling the POJO?

    Here are our requirements (assuming you are using the “easy” Prediction API for the POJO as described in the Javadoc).

    • By default, input columns must only contain categorical levels that were seen during training
    • Any additional input columns not used for training are ignored
    • If an input column is not specified, it will be treated as an NA
    • Any transformations applied to data before model training must also be applied before calling the POJO predict method
  • How do I run a POJO on a Spark Cluster?

    The POJO provides just the math logic to do predictions, so you won’t find any Spark (or even H2O) specific code there. If you want to use the POJO to make predictions on a dataset in Spark, create a map to call the POJO for each row and save the result to a new column, row-by-row.

  • How do I communicate with a remote POJO using a REST API?

    The Steam scoring server product provides a user-friendly way to do this. For a self-contained low-level do-it-yourself example, look here.

  • Is it possible to make predictions using my H2O cluster with the REST API?

    Yes, but this way of making predictions is separate from the POJO. For more information about in-H2O predictions (as opposed to POJO predictions), see the documentation for the H2O REST API endpoint /3/Predictions.

  • Why did I receive the following error when trying to compile the POJO?

Michals-MBP:b michal$ javac -cp h2o-genmodel.jar -J-Xmx2g -J-XX:MaxPermSize=128m drf_b9b9d3be_cf5a_464a_b518_90701549c12a.java
An exception has occurred in the compiler (1.7.0_60). Please file a bug at the Java Developer Connection (http://java.sun.com/webapps/bugreport)  after checking the Bug Parade for duplicates. Include your program and the following diagnostic in your report.  Thank you.
    at java.nio.ByteBuffer.allocate(ByteBuffer.java:330)
    at com.sun.tools.javac.util.BaseFileManager$ByteBufferCache.get(BaseFileManager.java:308)
    at com.sun.tools.javac.util.BaseFileManager.makeByteBuffer(BaseFileManager.java:280)
    at com.sun.tools.javac.file.RegularFileObject.getCharContent(RegularFileObject.java:112)
    at com.sun.tools.javac.file.RegularFileObject.getCharContent(RegularFileObject.java:52)
    at com.sun.tools.javac.main.JavaCompiler.readSource(JavaCompiler.java:571)
    at com.sun.tools.javac.main.JavaCompiler.parse(JavaCompiler.java:632)
    at com.sun.tools.javac.main.JavaCompiler.parseFiles(JavaCompiler.java:909)
    at com.sun.tools.javac.main.JavaCompiler.compile(JavaCompiler.java:824)
    at com.sun.tools.javac.main.Main.compile(Main.java:439)
    at com.sun.tools.javac.main.Main.compile(Main.java:353)
    at com.sun.tools.javac.main.Main.compile(Main.java:342)
    at com.sun.tools.javac.main.Main.compile(Main.java:333)
    at com.sun.tools.javac.Main.compile(Main.java:76)
    at com.sun.tools.javac.Main.main(Main.java:61)

This error is generated when the source file is larger than 1G.