eclipse中运行spark机器学习代码
直接在eclipse运行，不需要hadoop，不需要搭建spark，只需要pom.xml中的依赖完整
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.LogisticRegressionWithSGD
import org.apache.spark.mllib.feature.HashingTF
import org.apache.spark.mllib.regression.LabeledPoint
object MLlib {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName(s"Book example: Scala").setMaster("local[2]")
val sc = new SparkContext(conf)
// Load 2 types of emails from text files: spam and ham (non-spam).
// Each line has text from one email.
val spam = sc.textFile("file:/Users/xxx/Documents/hadoopTools/scala/eclipse/Eclipse.app/Contents/MacOS/workspace/spark_ml/src/main/resources/files/spam.txt")
val ham = sc.textFile("file:/Users/xxx/Documents/hadoopTools/scala/eclipse/Eclipse.app/Contents/MacOS/workspace/spark_ml/src/main/resources/files/ham.txt")
// val abc=sc.parallelize(seq, 2)
// Create a HashingTF instance to map email text to vectors of 100 features.
val tf = new HashingTF(numFeatures = 100)
// Each email is split into words, and each word is mapped to one feature.
val spamFeatures = spam.map(email => tf.transform(email.split(" ")))
val hamFeatures = ham.map(email => tf.transform(email.split(" ")))
// Create LabeledPoint datasets for positive (spam) and negative (ham) examples.
val positiveExamples = spamFeatures.map(features => LabeledPoint(1, features))
val negativeExamples = hamFeatures.map(features => LabeledPoint(0, features))
val trainingData = positiveExamples ++ negativeExamples
trainingData.cache() // Cache data since Logistic Regression is an iterative algorithm.
// Create a Logistic Regression learner which uses the LBFGS optimizer.
val lrLearner = new LogisticRegressionWithSGD()
// Run the actual learning algorithm on the training data.
val model = lrLearner.run(trainingData)
// Test on a positive example (spam) and a negative one (ham).
// First apply the same HashingTF feature transformation used on the training data.
val posTestExample = tf.transform("O M G GET cheap stuff by sending money to ...".split(" "))
val negTestExample = tf.transform("Hi Dad, I started studying Spark the other ...".split(" "))
// Now use the learned model to predict spam/ham for new emails.
println(s"Prediction for positive test example: ${model.predict(posTestExample)}")
println(s"Prediction for negative test example: ${model.predict(negTestExample)}")
sc.stop()
}
}
sc.textFile里的参数是文件在本地的绝对路径。
setMaster("local[2]") 表示是本地运行，只使用两个核
HashingTF 用来从文档中创建词条目的频率特征向量，这里设置维度为100.
TF-IDF(Term frequency-inverse document frequency ) 是文本挖掘中一种广泛使用的特征向量化方法。TF-IDF反映了语料中单词对文档的重要程度。假设单词用t表示，文档用d表示，语料用D表示，那么文档频度DF(t, D)是包含单词t的文档数。如果我们只是使用词频度量重要性，就会很容易过分强调重负次数多但携带信息少的单词，例如：”a”, “the”以及”of”。如果某个单词在整个语料库中高频出现，意味着它没有携带专门针对某特殊文档的信息。逆文档频度(IDF)是单词携带信息量的数值度量。
pom.xml
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.yanan.spark_maven</groupId>
<artifactId>spark1.3.1</artifactId>
<version>0.0.1-SNAPSHOT</version>
<packaging>jar</packaging>
<name>spark_maven</name>
<url>http://maven.apache.org</url>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<jackson.version>1.9.13</jackson.version>
</properties>
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.10.4</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>1.3.1</version>
</dependency>
<!--<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.10</artifactId>
<version>1.3.1</version> </dependency> <dependency> <groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.10</artifactId> <version>1.3.1</version> </dependency>
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-bagel_2.10</artifactId>
<version>1.3.1</version> </dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-graphx_2.10</artifactId>
<version>1.3.1</version>
</dependency> -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-mllib_2.10</artifactId>
<version>1.3.1</version>
</dependency>
<!-- specify the version for json_truple <dependency> <groupId>org.codehaus.jackson</groupId>
<artifactId>jackson-core-asl</artifactId> <version>${jackson.version}</version>
</dependency> <dependency> <groupId>org.codehaus.jackson</groupId> <artifactId>jackson-mapper-asl</artifactId>
<version>${jackson.version}</version> </dependency> -->
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
<pluginRepositories>
<pluginRepository>
<id>scala-tools.org</id>
<name>Scala-tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</pluginRepository>
</pluginRepositories>
<repositories>
<repository>
<id>cloudera-repo-releases</id>
<url>https://repository.cloudera.com/artifactory/repo/</url>
</repository>
</repositories>
</project>
ham.txt
Dear Spark Learner, Thanks so much for attending the Spark Summit 2014! Check out videos of talks from the summit at ... Hi Mom, Apologies for being late about emailing and forgetting to send you the package. I hope you and bro have been ... Wow, hey Fred, just heard about the Spark petabyte sort. I think we need to take time to try it out immediately ... Hi Spark user list, This is my first question to this list, so thanks in advance for your help! I tried running ... Thanks Tom for your email. I need to refer you to Alice for this one. I haven't yet figured out that part either ... Good job yesterday! I was attending your talk, and really enjoyed it. I want to try out GraphX ... Summit demo got whoops from audience! Had to let you know. --Joe
spam.txt
Dear sir, I am a Prince in a far kingdom you have not heard of. I want to send you money via wire transfer so please ... Get Vi_agra real cheap! Send money right away to ... Oh my gosh you can be really strong too with these drugs found in the rainforest. Get them cheap right now ... YOUR COMPUTER HAS BEEN INFECTED! YOU MUST RESET YOUR PASSWORD. Reply to this email with your password and SSN ... THIS IS NOT A SCAM! Send money and get access to awesome stuff really cheap and never have to ...
Vi_agra 本来是去掉下划线的
参考：
http://www.fuqingchuan.com/2015/03/643.html