Spark ML（5）：聚类算法（Kmeans和LDA）

一、环境配置

1.spark2.1.0-cdh5.7.0（自编译）

2.cdh5.7.0

3.scala2.11.8

4.centos6.4

二、环境准备

参考https://blog.csdn.net/u010886217/article/details/90312617

三、代码实现

1.测试数据集iris样例

5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
...

2.Kmeans算法

package sparktest

import org.apache.spark.ml.clustering.{KMeans, LDA}
import org.apache.spark.SparkConf
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.sql.SparkSession

import scala.util.Random

object cluster_kmeans {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local").setAppName("iris")
    val spark = SparkSession.builder().config(conf).getOrCreate()

    val file = spark.read.format("csv").load("iris.data")
    file.show()

    import spark.implicits._
    val random = new Random()
    val data = file.map(row => {
      val label = row.getString(4) match {
        case "Iris-setosa" => 0
        case "Iris-versicolor" => 1
        case "Iris-virginica" => 2
      }

      (row.getString(0).toDouble,
        row.getString(1).toDouble,
        row.getString(2).toDouble,
        row.getString(3).toDouble,
        label,
        random.nextDouble())
    }).toDF("_c0", "_c1", "_c2", "_c3", "label", "rand").sort("rand")
    val assembler = new VectorAssembler()
      .setInputCols(Array("_c0", "_c1", "_c2", "_c3"))
      .setOutputCol("features")

    val dataset = assembler.transform(data)
    val Array(train, test) = dataset.randomSplit(Array(0.8, 0.2))
    train.show()

    val kmeans = new KMeans().setFeaturesCol("features").setK(3).setMaxIter(20)
    val model = kmeans.fit(train)
    model.transform(train).show()

  }
}

3.LDA算法

package sparktest

import org.apache.spark.ml.clustering.{KMeans, LDA}
import org.apache.spark.SparkConf
//import org.apache.spark.ml.evaluation.ClusteringEvaluator
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.sql.SparkSession

import scala.util.Random

object cluster_lda {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local").setAppName("iris")
    val spark = SparkSession.builder().config(conf).getOrCreate()

    val file = spark.read.format("csv").load("iris.data")
    file.show()

    import spark.implicits._
    val random = new Random()
    val data = file.map(row => {
      val label = row.getString(4) match {
        case "Iris-setosa" => 0
        case "Iris-versicolor" => 1
        case "Iris-virginica" => 2
      }

      (row.getString(0).toDouble,
        row.getString(1).toDouble,
        row.getString(2).toDouble,
        row.getString(3).toDouble,
        label,
        random.nextDouble())
    }).toDF("_c0", "_c1", "_c2", "_c3", "label", "rand").sort("rand")
    val assembler = new VectorAssembler()
      .setInputCols(Array("_c0", "_c1", "_c2", "_c3"))
      .setOutputCol("features")

    val dataset = assembler.transform(data)
    val Array(train, test) = dataset.randomSplit(Array(0.8, 0.2))
    train.show()


    val lda = new LDA().setFeaturesCol("features").setK(3).setMaxIter(40)
    val model = lda.fit(train)
    val prediction = model.transform(train)
    //prediction.show()
    val ll = model.logLikelihood(train)
    val lp = model.logPerplexity(train)

    // Describe topics.
    val topics = model.describeTopics(3)
    prediction.select("label","topicDistribution").show(false)
    println("The topics described by their top-weighted terms:")
    topics.show(false)
    println(s"The lower bound on the log likelihood of the entire corpus: $ll")
    println(s"The upper bound on perplexity: $lp")
  }
}