Win10+IDEA+Scala+Spark2.1.0官方實例——樸素貝葉斯

從Spark 2.0開始，Spark中的基於RDD的spark.mllib包已進入維護模式，現在Spark主要的機器學習API是基於DataFrame的spark.ml包，基於RDD的API將在Spark3.0之後移除，所以還是建議大家重點學習spark.ml包裏的調用方式。詳見Spark MLlib
如果要運行官方給出的例子的話，需要在複製之前創建spark對象，下圖展示了在Win10本地運行時對源代碼做出的修改：

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// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.log4j.{Level, Logger}
import org.apache.spark.ml.classification.NaiveBayes
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// $example off$
import org.apache.spark.sql.SparkSession

object NaiveBayesExample {
  def main(args: Array[String]): Unit = {
    //!!!注意：如果在Windows上執行，指定Hadoop的Home
    System.setProperty("hadoop.home.dir", "D:\\temp\\hadoop-2.4.1\\hadoop-2.4.1")
    //不打印日誌
    Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

    val spark = SparkSession
      .builder
      .appName("NaiveBayesExample").master("local")
      .getOrCreate()

    // $example on$
    // Load the data stored in LIBSVM format as a DataFrame.
    val data = spark.read.format("libsvm").load("D:\\maven\\repository\\spark-2.1.0\\data\\mllib\\sample_libsvm_data.txt")

    // Split the data into training and test sets (30% held out for testing)
    val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3), seed = 1234L)

    // Train a NaiveBayes model.
    val model = new NaiveBayes()
      .fit(trainingData)

    // Select example rows to display.
    val predictions = model.transform(testData)
    predictions.show()

    // Select (prediction, true label) and compute test error
    val evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("label")
      .setPredictionCol("prediction")
      .setMetricName("accuracy")
    val accuracy = evaluator.evaluate(predictions)
    println("Test set accuracy = " + accuracy)
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println