【Windows】【Scala + Spark】【Eclipse】单机开发环境搭建 - 及示例程序

Java7 –系统变量

JAVA_HOME -> C:\ProgramFiles\Java\jdk1.7.0_71

CLASSPATH ->%JAVA_HOME%\lib\dt.jar;%JAVA_HOME%\lib\tools.jar;

PATH -> %JAVA_HOME%\bin;

 

Scala2.10.x –系统变量

SCALA_HOME -> C:\Program Files\scala

PATH -> %SCALA_HOME%\bin;

 

Scala IDE for Eclipse

File -> New -> Scala Project-> Project name:SparkMLlibTest-> Next -> Finish

src -> New -> Package -> name:com.sparkmltest.scala -> Finish

Scala Library container -> Build Path-> Configure Build Path… -> Libraries -> Edit -> Fixed ScalaLibrary container:2.10.x -> Finish -> OK

//For the Scala API, Spark1.5.2 usesScala 2.10.x

SparkMLlibTest -> New -> Folder-> name:lib -> Finish

将Spark-assembly-1.5.1-hadoop2.6.0.jar架包复制到文件夹lib下。

Spark-assembly-1.5.1-hadoop2.6.0.jar-> Build Path -> Add to Build Path

 

//example1 基于Spark的，读取一个本地文件并打印。

com.sparkmltest.scala -> New ->Scala Object/Scala Class -> Name:SimpleTest-> Finish

package com.sparkmltest.scalaimport org.apache.spark.{SparkConf,SparkContext} object SimpleTest {  def main(args:Array[String]):Unit = {    val conf = newSparkConf().setAppName("SimpleTest").setMaster("local[*]")    val sc = newSparkContext(conf)    val sqlContext = neworg.apache.spark.sql.SQLContext(sc)       val rdd = sc.textFile("D:/Scala workspace/simplefile.txt")    rdd.foreach(x =>println(x))  }}

//example2 Spark.ml Multilayer perceptron classifier –Spark机器学习多层感知器分类器，代码来自Spark官网，数据来自Spark源码自带默认数据集。

package com.sparkmltest.scala import org.apache.spark.{SparkConf,SparkContext}import org.apache.spark.ml.classification.MultilayerPerceptronClassifierimport org.apache.spark.ml.evaluation.MulticlassClassificationEvaluatorimport org.apache.spark.mllib.util.MLUtilsimport org.apache.spark.sql.Row object MultilayersPerceptronTest {  def main(args: Array[String]): Unit= {    val conf = new SparkConf().setAppName("SimpleTest").setMaster("local[*]")    val sc = new SparkContext(conf)    val sqlContext = new org.apache.spark.sql.SQLContext(sc)       importsqlContext.implicits._    //Load training data    val data = MLUtils.loadLibSVMFile(sc, "D:/Scalaworkspace/spark-1.5.1-bin-hadoop2.6/data/mllib/sample_multiclass_classification_data.txt").toDF()// Split thedata into train and test    val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)    val train = splits(0)    val test = splits(1)// specifylayers for the neural network:// inputlayer of size 4 (features), two intermediate of size 5 and 4 and output of size3 (classes)    val layers = Array[Int](4, 5, 4, 3)// create thetrainer and set its parameters    val trainer = new MultilayerPerceptronClassifier()      .setLayers(layers)      .setBlockSize(128)      .setSeed(1234L)      .setMaxIter(100)// train themodel    val model = trainer.fit(train)// computeprecision on the test set    val result = model.transform(test)    val predictionAndLabels = result.select("prediction", "label")    val evaluator = new MulticlassClassificationEvaluator()      .setMetricName("precision")    println("Precision:" + evaluator.evaluate(predictionAndLabels))  }}

//光标在MulticlassClassificationEvaluator处按F3，通过设置可以关联源码文件夹(Folder)以便阅读。