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, H2OGAM, H2ODeepLearning, H2OKMeans, H2OCoxPH, and H2OIsolationForest. 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. See also Parameters of H2OGridSearch.

• 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")

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.

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 the 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)

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
algo = H2ODRF(labelCol = "CAPSULE")

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 to be worried about column data types, you can explicitly identify the problem by using H2ODRFClassifier or H2ODRFRegressor instead.

Define a hyper-space which will be traversed

hyperSpace = {"ntrees": [1, 10, 30], "mtries": [-1, 5, 10]}

Pass the algorithm and hyper-space to the grid search and set properties defining the 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

from pysparkling.ml import H2OGridSearch
grid = H2OGridSearch(hyperParameters=hyperSpace, algo=algo, strategy="Cartesian")

Fit the grid search to get the best DRF model.

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(truncate = False)