Spark MLlib 入門學習筆記 - 樸素貝葉斯

在官方的API文檔可以查到用法。

def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel
input RDD of (label, array of features) pairs. Every vector should be a frequency vector or a count vector.
lambda The smoothing parameter
modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be multinomial or bernoulli，缺省爲multinomial

調用方法很簡單，用Iris數據集進行測試，代碼如下。

package classify

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.evaluation.MulticlassMetrics

object bayes {

  def isValid(line: String): Boolean = {
    val parts = line.split(",")
    return parts.length == 5
  }

  def parseLine(line: String): LabeledPoint = {
    val parts = line.split(",")
    val vd: Vector = Vectors.dense(parts(0).toDouble, parts(1).toDouble, parts(2).toDouble, parts(3).toDouble)
    var target = 0
    parts(4) match {
      case "Iris-setosa"   => target=1;
      case "Iris-versicolor"   => target = 2;
      case "Iris-virginica"    => target = 3;
    }
    return LabeledPoint(target, vd )
  }
  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster(args(0)).setAppName("")
    val sc = new SparkContext(conf)
    val data = sc.textFile(args(1)).filter(isValid(_)).map(parseLine(_))

    val splits = data.randomSplit(Array(0.7, 0.3), seed=11L)
    val trainData = splits(0)
    val testData = splits(1)

    val model = NaiveBayes.train(trainData, lambda = 1.0)

    val predictionAndLabel = testData.map(p => (model.predict(p.features), p.label))
    predictionAndLabel.foreach(println)

    val metrics = new MulticlassMetrics(predictionAndLabel)
    val precision = metrics.precision
    println("Precision = " + precision)

  }
}