val rawData = sc.textFile("file:///search/dje/spark-ml/train_noheader.tsv")
val records = rawData.map(line => line.split("\t"))
records.first()

import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
val data = records.map { r =>
val trimmed = r.map(_.replaceAll("\"",""))
val label = trimmed(r.size - 1).toInt
val features = trimmed.slice(4, r.size - 1).map(d => if (d == "?") 0.0 else d.toDouble)
LabeledPoint(label, Vectors.dense(features))
}

data.cache
val numData = data.count

val nbData = records.map { r =>
val trimmed = r.map(_.replaceAll("\"",""))
val label = trimmed(r.size - 1).toInt
val features = trimmed.slice(4, r.size - 1).map(d => if (d == "?") 0.0 else d.toDouble).map(d => if (d < 0) 0.0 else d )
LabeledPoint(label, Vectors.dense(features))
}

import org.apache.spark.mllib.classification.LogisticRegressionWithSGD
import org.apache.spark.mllib.classification.SVMWithSGD
import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.tree.configuration.Algo
import org.apache.spark.mllib.tree.impurity.Entropy

val numIterations = 10
val maxTreeDepth = 5

val lrModel = LogisticRegressionWithSGD.train(data, numIterations)
val svmModel = SVMWithSGD.train(data, numIterations)
val nbMobel = NaiveBayes.train(nbdata)
val dtModel = DecisionTree.train(data, Algo.Classification, Entropy, maxTreeDepth)