Spark MLlib 示例代码阅读

阅读前提：有一定的机器学习基础， 本文重点面向的是应用，至于机器学习的相关复杂理论和优化理论，还是多多看论文，初学者推荐Ng的公开课

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* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.{LogisticRegressionWithLBFGS, SVMWithSGD}
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}

/**
* An example app for binary classification. Run with
* {{{
* bin/run-example org.apache.spark.examples.mllib.BinaryClassification
* }}}
* A synthetic dataset is located at `data/mllib/sample_binary_classification_data.txt`.
* If you use it as a template to create your own app, please use `spark-submit` to submit your app.
*/
object BinaryClassification {

object Algorithm extends Enumeration {
type Algorithm = Value
val SVM, LR = Value
}

object RegType extends Enumeration {
type RegType = Value
val L1, L2 = Value
}

import Algorithm._
import RegType._

case class Params(
input: String = null,
numIterations: Int = 100,  迭代次数 submit时可以传递进来
stepSize: Double = 1.0,     步长
algorithm: Algorithm = LR,   默认的二分算法是逻辑回归
regType: RegType = L2,  默认的正则化规则是L2 ，
regParam: Double = 0.01) extends AbstractParams[Params]  L1 L2 正则化参数

def main(args: Array[String]) {    
val defaultParams = Params()

val parser = new OptionParser[Params]("BinaryClassification") {
head("BinaryClassification: an example app for binary classification.")
opt[Int]("numIterations") 
.text("number of iterations")
.action((x, c) => c.copy(numIterations = x))
opt[Double]("stepSize")
.text("initial step size (ignored by logistic regression), " +
s"default: ${defaultParams.stepSize}")
.action((x, c) => c.copy(stepSize = x))
opt[String]("algorithm")
.text(s"algorithm (${Algorithm.values.mkString(",")}), " +
s"default: ${defaultParams.algorithm}")
.action((x, c) => c.copy(algorithm = Algorithm.withName(x)))
opt[String]("regType")
.text(s"regularization type (${RegType.values.mkString(",")}), " +
s"default: ${defaultParams.regType}")
.action((x, c) => c.copy(regType = RegType.withName(x)))
opt[Double]("regParam")
.text(s"regularization parameter, default: ${defaultParams.regParam}")
arg[String]("<input>")
.required()
.text("input paths to labeled examples in LIBSVM format")
.action((x, c) => c.copy(input = x))
note(
"""
|For example, the following command runs this app on a synthetic dataset:
|
| bin/spark-submit --class org.apache.spark.examples.mllib.BinaryClassification \
| examples/target/scala-*/spark-examples-*.jar \
| --algorithm LR --regType L2 --regParam 1.0 \
| data/mllib/sample_binary_classification_data.txt
""".stripMargin)
}

parser.parse(args, defaultParams).map { params =>                                      ////params 是参数列表 保存了线性回归或者svm的各种参数
run(params)
} getOrElse {
sys.exit(1)
}
}

def run(params: Params) {
val conf = new SparkConf().setAppName(s"BinaryClassification with $params") 创建sparkConf 
val sc = new SparkContext(conf)  创建sc sparkcontext

Logger.getRootLogger.setLevel(Level.WARN)

val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()   //input 是我们的样本文件的路径

val splits = examples.randomSplit(Array(0.8, 0.2))  将输入文本进行随机切割 80%的文件为训练文本 20%的文件为 测试文本
val training = splits(0).cache()  训练数据
val test = splits(1).cache()  测试数据

val numTraining = training.count()
val numTest = test.count()
println(s"Training: $numTraining, test: $numTest.")

examples.unpersist(blocking = false)

val updater = params.regType match {   根据输入选择 是L1正则化还是L2正则化
case L1 => new L1Updater()
case L2 => new SquaredL2Updater()
}

val model = params.algorithm match {  根据输入参数选择是 逻辑回归还是 SVM
case LR =>
val algorithm = new LogisticRegressionWithLBFGS()
algorithm.optimizer
.setNumIterations(params.numIterations) 参数设置
.setUpdater(updater)         参数设置
.setRegParam(params.regParam)  参数设置
algorithm.run(training).clearThreshold()        开始train
case SVM =>
val algorithm = new SVMWithSGD()
algorithm.optimizer
.setNumIterations(params.numIterations)
.setStepSize(params.stepSize)
.setUpdater(updater)
.setRegParam(params.regParam)
algorithm.run(training).clearThreshold()     开始train
}

val prediction = model.predict(test.map(_.features))  开始测试
val predictionAndLabel = prediction.zip(test.map(_.label))

val metrics = new BinaryClassificationMetrics(predictionAndLabel)

println(s"Test areaUnderPR = ${metrics.areaUnderPR()}.")
println(s"Test areaUnderROC = ${metrics.areaUnderROC()}.")

sc.stop()
}
}