Train Sparkling Water Algorithms with Grid Search¶
Grid Search serves for finding optimal values for hyper parameters of a given H2O/SW algorithm. Grid Search in Sparkling Water is able to traverse hyper space for H2OGBM, H2OXGBoost, H2ODRF, H2OGLM, H2ODeepLearning. For more details about hyper parameters for a specific algorithm (see H2O-3 documentation).
Sparkling Water provides API in Scala and Python for Grid Search. The following sections describe how to Apply Grid Search on H2ODRF in both languages.
- 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
spark.sparkContext.addFile("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv")
val rawSparkDF = spark.read.option("header", "true").option("inferSchema", "true").csv(SparkFiles.get("prostate.csv"))
val sparkDF = rawSparkDF.withColumn("CAPSULE", $"CAPSULE" cast "string")
val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2))
Define the algorithm, which will be a subject of hyper parameter tuning
import ai.h2o.sparkling.ml.algos.H2ODRF
val algo = new H2ODRF().setLabelCol("CAPSULE")
Define a hyper space which will be traversed
import scala.collection.mutable.HashMap
val hyperSpace: HashMap[String, Array[AnyRef]] = HashMap()
hyperSpace += "ntrees" -> Array(1, 10, 30).map(_.asInstanceOf[AnyRef])
hyperSpace += "mtries" -> Array(-1, 5, 10).map(_.asInstanceOf[AnyRef])
Pass the algorithm and hyper space to the grid search and set properties defining tha way how the hyper space will be traversed.
Sparkling Water supports two strategies for traversing hyperspace:
Cartesian - (Default) This strategy tries out every possible combination of hyper parameter values and finishes after the whole space is traversed.
RandomDiscrete - In each iteration, the strategy randomly selects the combination of values from the hyper space and can be terminated before the whole space is traversed. The termination depends on various criteria (consider parameters:
maxRuntimeSecs
,maxModels
,stoppingRounds
,stoppingTolerance
,stoppingMetric
). For details see H2O-3 documentation
import ai.h2o.sparkling.ml.algos.H2OGridSearch
val grid = new H2OGridSearch()
.setHyperParameters(hyperSpace)
.setAlgo(algo)
.setStrategy("Cartesian")
Fit the grid search to get the best DRF model.
val model = grid.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)