R h2o load a saved model from disk in MOJO or POJO format

Question

I'm catching up on h2o's MOJO and POJO model format. I'm able to save a model in MOJO/POJO with

h2o.download_mojo(model, path = "/media/somewhere/tmp") # ok
h2o.download_pojo(model, path = "/media/somewhere/tmp") # ok

which writes an object with name like mymodel.zip or mymodel.java to the directory.

However, it's not clear to me how to read it back into the server in R. I tried,

saved_model2 <- h2o.loadModel("/media/somewhere/tmp/mymodel.java") # not work
saved_model3 <- h2o.loadModel("/media/somewhere/tmp/mymodel.zip") # not work

but got error msg like this,

ERROR: Unexpected HTTP Status code: 400 Bad Request (url = http://localhost:54321/99/Models.bin/)

java.lang.IllegalArgumentException
 [1] "java.lang.IllegalArgumentException: Missing magic number 0x1CED at stream start"  
....
Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = page,  : 

ERROR MESSAGE:

Missing magic number 0x1CED at stream start

Answer 1

If you are looking to make predictions on an H2O model in R, then you have three options (which method you choose depends on your use-case):

1. You can use a binary model instead of a MOJO (or POJO). For this method, you export the model to disk using h2o.saveModel() and load it back into the H2O clsuter using h2o.loadModel() and make predictions using predict(model, test). This method requires having an H2O cluster running.
2. If you's still prefer to export a model to MOJO (or POJO) format, you can use the h2o.mojo_predict_df() or h2o.mojo_predict_csv() function in R to generate predictions on a test set (from an R data.frame or in a CSV file).
3. As an alternative to #2, if your data is in JSON format, you can use h2o.predict_json(), but it will only score one row at a time.

Answer 2

h2o.loadModel is meant to be used with h2o.saveModel. If you want to compile and run a MOJO you need to do the following:

first let's say you created a MOJO from a GBM:

library(h2o)
h2o.init(nthreads=-1)
path = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv"
h2o_df = h2o.importFile(path)
h2o_df$RACE = as.factor(h2o_df$RACE)
model = h2o.gbm(y="CAPSULE",
        x=c("AGE", "RACE", "PSA", "GLEASON"),
        training_frame=h2o_df,
        distribution="bernoulli",
        ntrees=100,
        max_depth=4,
        learn_rate=0.1)

and then downloaded the MOJO and the resulting h2o-genmodel.jar file to a new experiment folder. Note that the h2o-genmodel.jar file is a library that supports scoring and contains the required readers and interpreters. This file is required when MOJO models are deployed to production.

modelfile = model.download_mojo(path="~/experiment/", get_genmodel_jar=True)
print("Model saved to " + modelfile)
Model saved to /Users/user/GBM_model_R_1475248925871_74.zip"

Then you would open a new terminal window and change into the experiment directory where you have have the MOJO files .zip and .jar.

$ cd experiment

Then you would create your main program in the experiment folder by creating a new file called main.java (for example, using "vim main.java"). Include the following contents. Note that this file is referencing the GBM model created above using R.

import java.io.*;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.prediction.*;
import hex.genmodel.MojoModel;

public class main {
  public static void main(String[] args) throws Exception {
    EasyPredictModelWrapper model = new EasyPredictModelWrapper(MojoModel.load("GBM_model_R_1475248925871_74.zip"));

    RowData row = new RowData();
    row.put("AGE", "68");
    row.put("RACE", "2");
    row.put("DCAPS", "2");
    row.put("VOL", "0");
    row.put("GLEASON", "6");

    BinomialModelPrediction p = model.predictBinomial(row);
    System.out.println("Has penetrated the prostatic capsule (1=yes; 0=no): " + p.label);
    System.out.print("Class probabilities: ");
    for (int i = 0; i < p.classProbabilities.length; i++) {
      if (i > 0) {
    System.out.print(",");
      }
      System.out.print(p.classProbabilities[i]);
    }
    System.out.println("");
  }
}

Then compile and run in terminal window 2 to get a display of predicted probabilities

$ javac -cp h2o-genmodel.jar -J-Xms2g -J-XX:MaxPermSize=128m main.java
$ java -cp .:h2o-genmodel.jar main