ML-spark 使用ml步骤

2019年5月11日 226次阅读 来源: yunpiao

title: spark 使用ml步骤
date: 2017-9-28 13:21:16
tags: [spark,机器学习]

使用大数据工具进行数据预测

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification._
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorAssembler}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}
 
 
object ClassificationPipeline {
 def main(args: Array[String]) {
 if (args.length < 1){
 println("Usage:ClassificationPipeline inputDataFile")
 sys.exit(1)
 }
 val conf = new SparkConf().setAppName("Classification with ML Pipeline")
 val sc = new SparkContext(conf)
 val sqlCtx = new SQLContext(sc)

Step 1

读取原始数据

  • 3.6216,8.6661,-2.8073,-0.44699,0
  • 4.5459,8.1674,-2.4586,-1.4621,0
  • 3.866,-2.6383,1.9242,0.10645,0
  • 3.4566,9.5228,-4.0112,-3.5944,0
  • 0.32924,-4.4552,4.5718,-0.9888,0
  • … …
    */
 val parsedRDD = sc.textFile(args(0)).map(_.split(",")).map(eachRow => {
 val a = eachRow.map(x => x.toDouble)
 (a(0),a(1),a(2),a(3),a(4))
 })
 val df = sqlCtx.createDataFrame(parsedRDD).toDF(
 "f0","f1","f2","f3","label").cache()

Step 2

为了容易使用机器学习算法 设置lable index 从0开始

val labelIndexer = new StringIndexer()
 .setInputCol("label")
 .setOutputCol("indexedLabel")
 .fit(df)

Step 3

定义特征列


val vectorAssembler = new VectorAssembler()
.setInputCols(Array("f0","f1","f2","f3"))
.setOutputCol("featureVector")

Step 4

创建随机森林分类器

val rfClassifier = new RandomForestClassifier()
 .setLabelCol("indexedLabel")
 .setFeaturesCol("featureVector")
 .setNumTrees(5)

Step 5

转换lable列 到原始数据

val labelConverter = new IndexToString()
 .setInputCol("prediction")
 .setOutputCol("predictedLabel")
 .setLabels(labelIndexer.labels)

Step 6

拆分数据

val Array(trainingData, testData) = df.randomSplit(Array(0.8, 0.2))`

Step 7

创建 ML pipeline .

val pipeline = new Pipeline().setStages(Array(labelIndexer,vectorAssembler,rfClassifier,labelConverter))
 val model = pipeline.fit(trainingData)

Step 8

设置填充数据预测

val predictionResultDF = model.transform(testData)`

Step 9

选择标签行

predictionResultDF.select("f0","f1","f2","f3","label","predictedLabel").show(20)`

Step 10

输出准确率

val evaluator = new MulticlassClassificationEvaluator()
 .setLabelCol("label")
 .setPredictionCol("prediction")
 .setMetricName("precision")
 val predictionAccuracy = evaluator.evaluate(predictionResultDF)
 println("Testing Error = " + (1.0 - predictionAccuracy))

Step 11

保存模型

val randomForestModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
 println("Trained Random Forest Model is:\n" + randomForestModel.toDebugString)
 }
}```
    原文作者：yunpiao
    原文地址: https://www.jianshu.com/p/ec9336327fc0#comments
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
点赞