Building a random forest in spark, explanation? - scala

Question

I have the a data frame df with the following structure:
amount gender_num marital_num
10000 1 1
20000 1 2
1400 2 1
Lets say I am building an ML to predict the column 'gender_num' in spark using random forest
I am doing the following:
val df1 = df("loan_amount", 'loan_amount.cast("Double")).withColumn("gender_num", 'gender_num.cast("String")).
withColumn("marital_num", 'marital_num.cast("String"))
val labeled = df1.map(row => LabeledPoint(df1.gender_num, Vectors.dense(df1.loan_amount, df1.marital_num)))
val numClasses = 7
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 3 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "gini"
val maxDepth = 4
val maxBins = 32
val model = RandomForest.trainClassifier(labeled, categoricalFeaturesInfo,
numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)
Error:
My code is failing at the second step:
138: error: value gender_num is not a member of org.apache.spark.sql.DataFrame
I would really appreciate if someone can explain this to me, the documentation is very hard to follow, newbie here!

Answer 1

That's because you are using R like syntax of DataFrame.
You should access row's data like this:
val labeled = df1.map { row => LabeledPoint(row(1).toDouble, Vectors.dense(row(0).toDouble, row(1).toDouble))}
You can also create case class and use Dataset syntax:
case class ParsedData (amount : Double, gender_num : Int, marital_num : Int)
val labeled = df1.as[ParsedData].map(row => LabeledPoint(df1.gender_num, Vectors.dense(df1.loan_amount, df1.marital_num)))