【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.scala

import 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.MultilayerPerceptronClassifier

import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

import org.apache.spark.mllib.util.MLUtils

import 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。