Spark机器学习读书笔记-CH05

Posted 2020-06-12

5.2.从数据中提取合适的特征

[[email protected] ch05]# sed 1d train.tsv > train_noheader.tsv
[[email protected] ch05]# ll
total 42920
-rw-r--r-- 1 root root 21972457 Jan 31 15:03 train_noheader.tsv
-rw-r--r-- 1 root root 21972916 Jan 31 15:00 train.tsv
[[email protected] ch05]# hdfs dfs -mkdir /user/root/studio/MachineLearningWithSpark/ch05
[[email protected] ch05]# hdfs dfs -put train_noheader.tsv /user/root/studio/MachineLearningWithSpark/ch05

[[email protected] ch05]# spark-shell --master yarn

scala> val rawData = sc.textFile("/user/root/studio/MachineLearningWithSpark/ch05/train_noheader.tsv")
rawData: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[1] at textFile at <console>:27

scala> val records = rawData.map(line => line.split("\t"))
records: org.apache.spark.rdd.RDD[Array[String]] = MapPartitionsRDD[2] at map at <console>:29

scala> records.first()
res1: Array[String] = Array("http://www.bloomberg.com/news/2010-12-23/ibm-predicts-holographic-calls-air-breathing-batteries-by-2015.html", "4042", "{""title"":""IBM Sees Holographic Calls Air Breathing Batteries ibm sees holographic calls, air-breathing batteries"",""body"":""A sign stands outside the International Business Machines Corp IBM Almaden Research Center campus in San Jose California Photographer Tony Avelar Bloomberg Buildings stand at the International Business Machines Corp IBM Almaden Research Center campus in the Santa Teresa Hills of San Jose California Photographer Tony Avelar Bloomberg By 2015 your mobile phone will project a 3 D image of anyone who calls and your laptop will be powered by kinetic energy At least that s what International Business Machines Corp sees ...
scala> import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.regression.LabeledPoint

scala> import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.Vectors

scala> val data = records.map{ r =>
| val trimmed = r.map(_.replaceAll("\"",""))
| val label = trimmed(r.size - 1).toInt
| val features = trimmed.slice(4, r.size - 1).map(d => if (d == "?") 0.0 else d.toDouble)
| LabeledPoint(label, Vectors.dense(features))
| }
data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[3] at map at <console>:33

 

5.3.训练分类模型

scala> import org.apache.spark.mllib.classification.LogisticRegressionWithSGD
import org.apache.spark.mllib.classification.LogisticRegressionWithSGD

scala> import org.apache.spark.mllib.classification.SVMWithSGD
import org.apache.spark.mllib.classification.SVMWithSGD

scala> import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.classification.NaiveBayes

scala> import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.tree.DecisionTree

scala> import org.apache.spark.mllib.tree.configuration.Algo
import org.apache.spark.mllib.tree.configuration.Algo

scala> import org.apache.spark.mllib.tree.impurity.Entropy
import org.apache.spark.mllib.tree.impurity.Entropy

scala> val numIterations = 10
numIterations: Int = 10

scala> val maxTreeDepth = 5
maxTreeDepth: Int = 5

scala> val lrModel = LogisticRegressionWithSGD.train(data, numIterations)
lrModel: org.apache.spark.mllib.classification.LogisticRegressionModel = org.apache.spark.mllib.classification.LogisticRegressionModel: intercept = 0.0, numFeatures = 22, numClasses = 2, threshold = 0.5

scala> val svmModel = SVMWithSGD.train(data, numIterations)
svmModel: org.apache.spark.mllib.classification.SVMModel = org.apache.spark.mllib.classification.SVMModel: intercept = 0.0, numFeatures = 22, numClasses = 2, threshold = 0.0

scala> val nbModel = NaiveBayes.train(nbData)
nbModel: org.apache.spark.mllib.classification.NaiveBayesModel = [email protected]

scala> val dtModel = DecisionTree.train(data, Algo.Classification, Entropy, maxTreeDepth)
dtModel: org.apache.spark.mllib.tree.model.DecisionTreeModel = DecisionTreeModel classifier of depth 5 with 61 nodes

 

5.4使用分类模型

scala> val dataPoint = data.first
dataPoint: org.apache.spark.mllib.regression.LabeledPoint = (0.0,[0.789131,2.055555556,0.676470588,0.205882353,0.047058824,0.023529412,0.443783175,0.0,0.0,0.09077381,0.0,0.245831182,0.003883495,1.0,1.0,24.0,0.0,5424.0,170.0,8.0,0.152941176,0.079129575])

scala> val prediction = lrModel.predict(dataPoint.features)
prediction: Double = 1.0

scala> val trueLabel = dataPoint.label
trueLabel: Double = 0.0

scala> val predictions = lrModel.predict(data.map(lp => lp.features))
predictions: org.apache.spark.rdd.RDD[Double] = MapPartitionsRDD[99] at mapPartitions at GeneralizedLinearAlgorithm.scala:69

scala> predictions.take(5)
res3: Array[Double] = Array(1.0, 1.0, 1.0, 1.0, 1.0)

 

5.5.评估分类模型的性能

scala> val lrTotalCorrect = data.map { point =>
| if (lrModel.predict(point.features) == point.label) 1 else 0
| }.sum
lrTotalCorrect: Double = 3806.0

scala> val lrAccuracy = lrTotalCorrect / data.count
lrAccuracy: Double = 0.5146720757268425

scala> val svmTotalCorrect = data.map { point =>
| if (svmModel.predict(point.features) == point.label) 1 else 0
| }.sum
svmTotalCorrect: Double = 3806.0

scala> val svmAccuracy = svmTotalCorrect / data.count
svmAccuracy: Double = 0.5146720757268425

scala> val nbTotalCorrect = nbData.map { point =>
| if (nbModel.predict(point.features) == point.label) 1 else 0
| }.sum
nbTotalCorrect: Double = 4292.0

scala> val nbAccuracy = nbTotalCorrect / data.count
nbAccuracy: Double = 0.5803921568627451

 

scala> val dtTotalCorrect = data.map { point =>
| val score = dtModel.predict(point.features)
| val predicted = if (score > 0.5) 1 else 0
| if (predicted == point.label) 1 else 0
| }.sum
dtTotalCorrect: Double = 4794.0

scala> val dtAccuracy = dtTotalCorrect / data.count
dtAccuracy: Double = 0.6482758620689655

 

 

scala> import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics

scala> val metrics = Seq(lrModel, svmModel).map { model =>
| val scoreAndLabels = data.map { point => (model.predict(point.features), point.label) }
| val metrics = new BinaryClassificationMetrics(scoreAndLabels)
| (model.getClass.getSimpleName, metrics.areaUnderPR, metrics.areaUnderROC)
| }
metrics: Seq[(String, Double, Double)] = List((LogisticRegressionModel,0.7567586293858841,0.5014181143280931), (SVMModel,0.7567586293858841,0.5014181143280931))

scala> val nbMetrics = Seq(nbModel).map { model =>
| val scoreAndLabels = nbData.map { point =>
| val score = model.predict(point.features)
| (if (score > 0.5) 1.0 else 0.0, point.label)
| }
| val metrics = new BinaryClassificationMetrics(scoreAndLabels)
| (model.getClass.getSimpleName, metrics.areaUnderPR, metrics.areaUnderROC)
| }
nbMetrics: Seq[(String, Double, Double)] = List((NaiveBayesModel,0.6808510815151734,0.5835585110136261))

scala> val dtMetrics = Seq(dtModel).map { model =>
| val scoreAndLabels = data.map { point =>
| val score = model.predict(point.features)
| (if (score > 0.5) 1.0 else 0.0, point.label)
| }
| val metrics = new BinaryClassificationMetrics(scoreAndLabels)
| (model.getClass.getSimpleName, metrics.areaUnderPR, metrics.areaUnderROC)
| }
dtMetrics: Seq[(String, Double, Double)] = List((DecisionTreeModel,0.7430805993331199,0.6488371887050935))

scala> val allMetrics = metrics ++ nbMetrics ++ dtMetrics
allMetrics: Seq[(String, Double, Double)] = List((LogisticRegressionModel,0.7567586293858841,0.5014181143280931), (SVMModel,0.7567586293858841,0.5014181143280931), (NaiveBayesModel,0.6808510815151734,0.5835585110136261), (DecisionTreeModel,0.7430805993331199,0.6488371887050935))

scala> allMetrics.foreach { case (m, pr, roc) =>
| println(f"$m, Area under PR: ${pr * 100.0}%2.4f%%, Area under ROC: ${roc * 100.0}%2.4f%%")
| }
LogisticRegressionModel, Area under PR: 75.6759%, Area under ROC: 50.1418%
SVMModel, Area under PR: 75.6759%, Area under ROC: 50.1418%
NaiveBayesModel, Area under PR: 68.0851%, Area under ROC: 58.3559%
DecisionTreeModel, Area under PR: 74.3081%, Area under ROC: 64.8837%