multiLabelClassification

def magicMultiLabelClassification(): Unit = {
    val conf = new SparkConf().setAppName("Simple Application").setMaster("local[2]")
    val sc = new SparkContext(conf)
    val data = MLUtils.loadLibSVMFile(sc, "test3.txt")
    val splits = data.randomSplit(Array(0.7, 0.3))
    val (trainingData, testData) = (splits(0), splits(1))

    //Inputs for random forest
    val numClasses = 2
    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 labels = trainingData.map(d => d.label).take(1000).distinct // Take is a hack to allow me to get distinct.
    val groupedRdds = labels.map { l => trainingData.filter(m => m.label == l) }

    val models = groupedRdds.map(rdd => RandomForest.trainClassifier(rdd, numClasses, categoricalFeaturesInfo,
      numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins))

    val labelsAndPredictions = testData.map { point => models.map(model => (point.label, model.predict(point.features))) }
    
    // I could then filter here for predictions above 0.5 and return the labels
  }