How do I run the Spark decision tree with a categorical feature set using Scala?

Question

I have a feature set with a corresponding categoricalFeaturesInfo: Map[Int,Int]. However, for the life of me I cannot figure out how I am supposed to get the DecisionTree class to work. It will not accept anything, but a LabeledPoint as data. However, LabeledPoint requires (double, vector) where the vector requires doubles.

val LP = featureSet.map(x => LabeledPoint(classMap(x(0)),Vectors.dense(x.tail)))

// Run training algorithm to build the model
val maxDepth: Int = 3
val isMulticlassWithCategoricalFeatures: Boolean = true
val numClassesForClassification: Int = countPossibilities(labelCol) 
val model = DecisionTree.train(LP, Classification, Gini, isMulticlassWithCategoricalFeatures, maxDepth, numClassesForClassification,categoricalFeaturesInfo)

The error I get:

scala> val LP = featureSet.map(x => LabeledPoint(classMap(x(0)),Vectors.dense(x.tail)))
<console>:32: error: overloaded method value dense with alternatives:
  (values: Array[Double])org.apache.spark.mllib.linalg.Vector <and>
  (firstValue: Double,otherValues: Double*)org.apache.spark.mllib.linalg.Vector
 cannot be applied to (Array[String])
       val LP = featureSet.map(x => LabeledPoint(classMap(x(0)),Vectors.dense(x.tail)))

My resources thus far: tree config, decision tree, labeledpoint

Answer 1

You can first transform categories to numbers, then load data as if all features are numerical.

When you build a decision tree model in Spark, you just need to tell spark which features are categorical and also the feature's arity (the number of distinct categories of that feature) by specifying a map Map[Int, Int]() from feature indices to its arity.

For example if you have data as:

1,a,add
2,b,more
1,c,thinking
3,a,to
1,c,me

You can first transform data into numerical format as:

1,0,0
2,1,1
1,2,2
3,0,3
1,2,4

In that format you can load data to Spark. Then if you want to tell Spark the second and the third columns are categorical, you should create a map:

categoricalFeaturesInfo = Map[Int, Int]((1,3),(2,5))

The map tells us that feature with index 1 has arity 3, and feature with index 2 has artity 5. They will be considered as categorical when we build a decision tree model passing that map as a parameter of the training function:

val model = DecisionTree.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo, impurity, maxDepth, maxBins)