Train DRF Model in Sparkling Water

Sparkling Water provides API for H2O DRF in Scala and Python. The following sections describe how to train the DRF model in Sparkling Water in both languages.

Scala

First, let’s start Sparkling Shell as

./bin/sparkling-shell

Start H2O cluster inside the Spark environment

import org.apache.spark.h2o._
import java.net.URI
val hc = H2OContext.getOrCreate()

Parse the data using H2O and convert them to Spark Frame

val frame = new H2OFrame(new URI("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv"))
val sparkDF = hc.asSparkFrame(frame).withColumn("CAPSULE", $"CAPSULE" cast "string")
val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2))

Train the model. You can configure all the available DRF arguments using provided setters, such as the label column.

import ai.h2o.sparkling.ml.algos.H2ODRF
val estimator = new H2ODRF().setLabelCol("CAPSULE")
val model = estimator.fit(trainingDF)

You can also get raw model details by calling the getModelDetails() method available on the model as:

model.getModelDetails()

Run Predictions

model.transform(testingDF).show(false)

Python

First, let’s start PySparkling Shell as

./bin/pysparkling

Start H2O cluster inside the Spark environment

from pysparkling import *
hc = H2OContext.getOrCreate()

Parse the data using H2O and convert them to Spark Frame

import h2o
frame = h2o.import_file("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv")
sparkDF = hc.asSparkFrame(frame)
sparkDF = sparkDF.withColumn("CAPSULE", sparkDF.CAPSULE.cast("string"))
[trainingDF, testingDF] = sparkDF.randomSplit([0.8, 0.2])

Train the model. You can configure all the available DRF arguments using provided setters or constructor parameters, such as the label column.

from pysparkling.ml import H2ODRF
estimator = H2ODRF(labelCol = "CAPSULE", withDetailedPredictionCol = False)
model = estimator.fit(trainingDF)

You can also get raw model details by calling the getModelDetails() method available on the model as:

model.getModelDetails()

Run Predictions

model.transform(testingDF).show(truncate = False)