spark2.1.1创建Pipeline

Pipeline 为流程，是Spark创建机器学习的一个流程控制的类
下面直接贴出创建的代码，以及整个流程 

第一种：

import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.ml.linalg.Vector

/**
  * Created by xiaopengpeng on 2016/12/20.
  */
class Popeline_ {

}
object Popeline_{
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .appName("popeline")
      .master("local[*]")
      .config("spark.sql.warehouse.dir", "warehouse/dir")
      .getOrCreate()
    //创建原始数据
    val training = spark.createDataFrame(Seq((0L,"a b c d e spark",1.0),
      (1L, "b d", 0.0),
      (2L, "spark f g h", 1.0),
      (3L, "hadoop mapreduce", 0.0)
    )).toDF("id","text","label")
    //创建分词
    val tokenizer = new Tokenizer().setInputCol("text").setOutputCol("words")
    //创建hashingTF
    val hashingTF = new HashingTF().setNumFeatures(1000).setInputCol(tokenizer.getOutputCol).setOutputCol("features")
    //创建模型
    val lr = new LogisticRegression().setMaxIter(10).setRegParam(0.01)
    //创建流程
    val pipeline = new Pipeline().setStages(Array(tokenizer,hashingTF,lr))
    //进行模型训练
    val model = pipeline.fit(training)
    //把模型存储到磁盘上
    model.write.overwrite().save("result/model/popeline")
    //把没有训练的模型存储到磁盘上
    pipeline.write.overwrite().save("result/unmodel/poeline")
    //从磁盘上读取
    val sameModel = PipelineModel.load("result/model/popeline")
    //创建测试数据
    val test = spark.createDataFrame(Seq((4L, "spark i j k"),
      (5L, "l m n"),
      (6L, "mapreduce spark"),
      (7L, "apache hadoop")
    )).toDF("id","text")
    //测试的输出
    model.transform(test).select("id","text","probability","prediction").collect()
      .foreach{case Row(id:Long,text:String,prob:Vector,prediction:Double) => println(s"($id,$text) --> prob=$prob, prediction = $prediction")}
    spark.stop()
  }
}

第二种：

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.sql.{Row, SparkSession}

/**
  * Created by xiaopengpeng on 2016/12/20.
  */
class Popeline_2 {

}
object Popeline_2{
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .appName("流程")
      .master("local[*]")
      .config("spark.sql.warehouse.dir", "warehouse/dir")
      .getOrCreate()

    val training = spark.createDataFrame(Seq((1.0,Vectors.dense(0.0,1.1,0.1),
      (0.0, Vectors.dense(2.0, 1.0, -1.0)),
      (0.0, Vectors.dense(2.0, 1.3, 1.0)),
      (1.0, Vectors.dense(0.0, 1.2, -0.5))
      ))).toDF("label","features")

    val lr = new LogisticRegression()
    println("LogisticRegression parameters:
"+lr.explainParams()+"
")

    lr.setMaxIter(10).setRegParam(0.01)
    val model1 = lr.fit(training)
    println("Model 1 was fit using parameters: "+model1.parent.extractParamMap())
    val paramMap = ParamMap(lr.maxIter -> 20)
      .put(lr.maxIter -> 30)//这个会覆盖上一个
      .put(lr.regParam -> 0.1 ,lr.threshold -> 0.55)

    val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability")  //改变输出列名
    val paramMapCombined = paramMap++paramMap2
    val model2 = lr.fit(training,paramMapCombined)
    println("Model 2 was fit using parameters: "+model2.parent.extractParamMap)

    val test = spark.createDataFrame(Seq((1.0,Vectors.dense(-1.0,1.5,1.3)),
      (0.0, Vectors.dense(3.0, 2.0, -0.1)),
      (1.0, Vectors.dense(0.0, 2.2, -1.5))
    )).toDF("label","features")

    model2.transform(test)
      .select("features","label","myProbability","prediction")
      .collect()
      .foreach{case Row(features:Vector,lable:Double,prob:Vector,prediction:Double) => println(s"($features,$lable) ->prob=$prob,prediction=$prediction")}

  }
}