Train Deep Learning Model in Sparkling Water

Sparkling Water provides API for H2O Deep Learning in Scala and Python. The following sections describe how to train the Deep Learning model in Sparkling Water in both languages. See also Parameters of H2ODeepLearning.

Scala

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))

Train the model. You can configure all the available DeepLearning arguments using provided setters, such as the label column or the layout of hidden layers.

import ai.h2o.sparkling.ml.algos.H2ODeepLearning
val estimator = new H2ODeepLearning().setLabelCol("CAPSULE").setHidden(Array(3, 2))
val model = estimator.fit(trainingDF)

By default, the H2ODeepLearning 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 worry about column data types, you can explicitly specify the problem by using ai.h2o.sparkling.ml.algos.classification.H2ODeepLearningClassifier or ai.h2o.sparkling.ml.algos.regression.H2ODeepLearningRegressor instead.

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 Deep Learning arguments using provided setters or constructor parameters, such as the label column or the layout of hidden layers.

from pysparkling.ml import H2ODeepLearning
estimator = H2ODeepLearning(labelCol = "CAPSULE", hidden=[3, 2])
model = estimator.fit(trainingDF)

By default, the H2ODeepLearning 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 worry about column data types, you can explicitly specify the problem by using H2ODeepLearningClassifier or H2ODeepLearningRegressor instead.

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)