Train DRF Model in Sparkling Water

Introduction

Distributed Random Forest (DRF) is a powerful classification and regression tool. When given a set of data, DRF generates a forest of classification or regression trees, rather than a single classification or regression tree. For more more comprehensive description see H2O-3 DRF documentation.

Example

The following section describes how to train the Distributed Random Forest model in Sparkling Water in Scala & Python following the same example as H2O-3 documentation mentioned above. See also Parameters of H2ODRF and Details of H2ODRFMOJOModel.

  • Scala
  • Python

First, let’s start Sparkling Shell as

./bin/sparkling-shell

Start H2O cluster inside the Spark environment

import ai.h2o.sparkling._
import java.net.URI
val hc = H2OContext.getOrCreate()

Parse the data using H2O and convert them to Spark Frame

import org.apache.spark.SparkFiles
val datasetUrl = "https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/cars_20mpg.csv"
spark.sparkContext.addFile(datasetUrl) //for example purposes, on a real cluster it's better to load directly from distributed storage
val sparkDF = spark.read.option("header", "true").option("inferSchema", "true").csv(SparkFiles.get("cars_20mpg.csv"))
val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2))

Set the predictors and response columns

val predictors = Array("displacement", "power", "weight", "acceleration", "year")
val response = "economy_20mpg"

Build and 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()
      .setNtrees(10)
      .setMaxDepth(5)
      .setMinRows(10)
      .setCalibrateModel(true)
      .setCalibrationDataFrame(testingDF)
      .setBinomialDoubleTrees(true)
      .setFeaturesCols(predictors)
      .setLabelCol(response)
      .setColumnsToCategorical(response) //set the response as a factor, please see the comment below
val model = estimator.fit(trainingDF)

By default, the H2ODRF algorithm distinguishes between a classification and regression problem based on the type of the label column of the training dataset. If the label column is a string column, a classification model will be trained. If the label column is a numeric column, a regression model will be trained. If you don’t want be worried about column data types, you can explicitly identify the problem by using ai.h2o.sparkling.ml.algos.classification.H2ODRFClassifier or ai.h2o.sparkling.ml.algos.regression.H2ODRFRegressor instead.

Eval performance

val metrics = model.getTrainingMetrics()
println(metrics)

Run Predictions

model.transform(testingDF).show(false)

You can also get model details via calling methods listed in Details of H2ODRFMOJOModel.