AAS代码运行-第4章

[root@node1 aas]# ls
ch02  ch03  spark-1.2.-bin-hadoop2.  spark-1.2.-bin-hadoop2..tgz
[root@node1 aas]# cd spark-1.2.-bin-hadoop2.
[root@node1 spark-1.2.-bin-hadoop2.]# cd ..
[root@node1 aas]# mkdir ch04
[root@node1 aas]# cd ch04
[root@node1 ch04]# ls
[root@node1 ch04]# wget https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz
---- ::--  https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz
Resolving archive.ics.uci.edu... 128.195.10.249
Connecting to archive.ics.uci.edu|128.195.10.249|:... connected.
HTTP request sent, awaiting response...  OK
Length:  (11M) [application/x-gzip]
Saving to: ?.ovtype.data.gz?

%[===============================================================================================================================================================>] ,,  2.62M/s   in .2s    

-- :: (2.53 MB/s) - ?.ovtype.data.gz?.saved [/]

[root@node1 ch04]# wget https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.info
---- ::--  https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.info
Resolving archive.ics.uci.edu... 128.195.10.249
Connecting to archive.ics.uci.edu|128.195.10.249|:... connected.
HTTP request sent, awaiting response...  OK
Length:  (14K) [text/plain]
Saving to: ?.ovtype.info?

%[===============================================================================================================================================================>] ,      --.-K/s   in .001s  

-- :: (15.6 MB/s) - ?.ovtype.info?.saved [/]

[root@node1 ch04]# wget https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/old_covtype.info
---- ::--  https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/old_covtype.info
Resolving archive.ics.uci.edu... 128.195.10.249
Connecting to archive.ics.uci.edu|128.195.10.249|:... connected.
HTTP request sent, awaiting response...  OK
Length:  (.7K) [text/plain]
Saving to: ?.ld_covtype.info?

%[===============================================================================================================================================================>] ,       --.-K/s   in 0s      

-- :: (12.7 MB/s) - ?.ld_covtype.info?.saved [/]

将数据放到HDFS上

[root@node1 ch04]# ls
covtype.data.gz  covtype.info  old_covtype.info
[root@node1 ch04]# gunzip -d covtype.data.gz
[root@node1 ch04]# ll
total
-rw-r--r--  root root  Sep     covtype.data
-rw-r--r--  root root     Apr    covtype.info
-rw-r--r--  root root      Sep     old_covtype.info
[root@node1 ch04]# hdfs dfs -mkdir /user/root/covtype
[root@node1 ch04]# hdfs dfs -put * /user/root/covtype
[root@node1 ch04]# hdfs dfs -ls /user/root/covtype
Found  items
-rw-r--r--    root supergroup    -- : /user/root/covtype/covtype.data
-rw-r--r--    root supergroup       -- : /user/root/covtype/covtype.info
-rw-r--r--    root supergroup        -- : /user/root/covtype/old_covtype.info

启动spark-shell

[root@node1 ch04]# ../spark-1.2.-bin-hadoop2./bin/spark-shell --master yarn-client
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 1.2.
      /_/

Using Scala version 2.10. (OpenJDK -Bit Server VM, Java 1.7.0_09-icedtea)
Type in expressions to have them evaluated.
Type :help for more information.
// :: WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark context available as sc.

运行代码4.7节代码：

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

scala> import org.apache.spark.mllib.regression._
import org.apache.spark.mllib.regression._

scala> 

scala> val rawData = sc.textFile("hdfs:///user/ds/covtype.data" )
rawData: org.apache.spark.rdd.RDD[String] = hdfs:///user/ds/covtype.data MappedRDD[1] at textFile at <console>:18

scala> val data = rawData.map { line =>
     | val values = line.split(',' ).map(_. toDouble)
     |   val featureVector = Vectors.dense(values. init)
     |   val label = values.last -
     |   LabeledPoint(label, featureVector)
     | }
data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MappedRDD[] at map at <console>:

scala> val Array(trainData, cvData, testData) = data. randomSplit(Array(0.8, 0.1, 0.1))
trainData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = PartitionwiseSampledRDD[] at randomSplit at <console>:
cvData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = PartitionwiseSampledRDD[] at randomSplit at <console>:
testData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = PartitionwiseSampledRDD[] at randomSplit at <console>:

scala> trainData.cache()
res0: trainData.type = PartitionwiseSampledRDD[] at randomSplit at <console>:

scala> cvData.cache()
res1: cvData.type = PartitionwiseSampledRDD[] at randomSplit at <console>:

scala> testData.cache()
res2: testData.type = PartitionwiseSampledRDD[] at randomSplit at <console>:22

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

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

scala> import org.apache.spark.mllib.tree.model._
import org.apache.spark.mllib.tree.model._

scala> import org.apache.spark.rdd._
import org.apache.spark.rdd._

scala> def getMetrics(model: DecisionTreeModel, data: RDD[LabeledPoint]):
| MulticlassMetrics = {
| val predictionsAndLabels = data. map(example =>(model. predict(example. features), example. label))
| new MulticlassMetrics(predictionsAndLabels)
| }
getMetrics: (model: org.apache.spark.mllib.tree.model.DecisionTreeModel, data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint])org.apache.spark.mllib.evaluation.MulticlassMetrics

scala> val model = DecisionTree.trainClassifier(trainData, 7, Map[Int, Int](), "gini" , 4, 100)
model: org.apache.spark.mllib.tree.model.DecisionTreeModel = DecisionTreeModel classifier of depth 4 with 31 nodes

scala> val metrics = getMetrics(model, cvData)
metrics: org.apache.spark.mllib.evaluation.MulticlassMetrics = org.apache.spark.mllib.evaluation.MulticlassMetrics@5d574c23

scala> metrics. confusionMatrix
res6: org.apache.spark.mllib.linalg.Matrix =
15535.0 5345.0 21.0 0.0 0.0 0.0 392.0
6669.0 20855.0 688.0 0.0 5.0 0.0 47.0
0.0 610.0 2942.0 0.0 0.0 0.0 0.0
0.0 0.0 274.0 0.0 0.0 0.0 0.0
12.0 874.0 57.0 0.0 15.0 0.0 0.0
0.0 446.0 1318.0 0.0 0.0 0.0 0.0
1150.0 19.0 8.0 0.0 0.0 0.0 905.0

scala> metrics.precision
res7: Double = 0.6917696392665028

scala> metrics.precision
res7: Double = 0.6917696392665028

scala> (0 until 7).map(
| cat => (metrics.precision(cat), metrics.recall(cat))
| ).foreach(println)
(0.6648549174013524,0.729582491898746)
(0.7408788944545099,0.7378644211718086)
(0.554257724189902,0.8282657657657657)
(0.0,0.0)
(0.75,0.015657620041753653)
(0.0,0.0)
(0.6733630952380952,0.4346781940441883)

scala> import org.apache.spark.rdd._
import org.apache.spark.rdd._

scala> def classProbabilities(data: RDD[LabeledPoint]): Array[Double] = {
| val countsByCategory = data.map(_.label).countByValue()
| val counts = countsByCategory.toArray.sortBy(_. _1).map(_. _2)
| counts.map(_.toDouble / counts.sum)
| }
classProbabilities: (data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint])Array[Double]

scala> val trainPriorProbabilities = classProbabilities(trainData)
trainPriorProbabilities: Array[Double] = Array(0.3644680841907762, 0.48778063233452534, 0.06163475731247069, 0.004682046846288574, 0.0163893156379504, 0.029860958700732, 0.035184204977256786)

scala> val cvPriorProbabilities = classProbabilities(cvData)
cvPriorProbabilities: Array[Double] = Array(0.36594084589341264, 0.4857442384037672, 0.061044563218588345, 0.004708955608641105, 0.016464158660869265, 0.03031604997679894, 0.03578118823792256)

scala> trainPriorProbabilities.zip(cvPriorProbabilities).map {
| case (trainProb, cvProb) => trainProb * cvProb
| }.sum
res9: Double = 0.3765289404519721

scala> val evaluations =
| for (impurity <- Array("gini" , "entropy" );
| depth <- Array(1, 20);
| bins <- Array(10, 300))
| yield {
| val model = DecisionTree. trainClassifier(trainData, 7, Map[Int, Int](), impurity, depth, bins)
| val predictionsAndLabels = cvData. map(example =>(model. predict(example. features), example. label))
| val accuracy = new MulticlassMetrics(predictionsAndLabels). precision
| ((impurity, depth, bins), accuracy)
| }
evaluations: Array[((String, Int, Int), Double)] = Array(((gini,1,10),0.6319968377816351), ((gini,1,300),0.6323577431385017), ((gini,20,10),0.889253613350061), ((gini,20,300),0.9074191829790159), ((entropy,1,10),0.4857442384037672), ((entropy,1,300),0.4857442384037672), ((entropy,20,10),0.8946500077336862), ((entropy,20,300),0.9099455204770825))

scala> evaluations.sortBy(_. _2).reverse.foreach(println)
((entropy,20,300),0.9099455204770825)
((gini,20,300),0.9074191829790159)
((entropy,20,10),0.8946500077336862)
((gini,20,10),0.889253613350061)
((gini,1,300),0.6323577431385017)
((gini,1,10),0.6319968377816351)
((entropy,1,300),0.4857442384037672)
((entropy,1,10),0.4857442384037672)

scala> val data = rawData.map { line =>
| val values = line.split(',' ).map(_.toDouble)
| val wilderness = values.slice(10, 14).indexOf(1.0).toDouble
| val soil = values.slice(14, 54).indexOf(1.0).toDouble
| val featureVector =
| Vectors.dense(values.slice(0, 10) :+ wilderness :+ soil)
| val label = values.last - 1
| LabeledPoint(label, featureVector)
| }
data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MappedRDD[391] at map at <console>:47

scala>

scala> val evaluations =
| for (impurity <- Array("gini" , "entropy" );
| depth <- Array(10, 20, 30);
| bins <- Array(40, 300))
| yield {
| val model = DecisionTree. trainClassifier(trainData, 7, Map(10 -> 4, 11 -> 40),impurity, depth, bins)
| val trainAccuracy = getMetrics(model, trainData). precision
| val cvAccuracy = getMetrics(model, cvData). precision
| ((impurity, depth, bins), (trainAccuracy, cvAccuracy))
| }
evaluations: Array[((String, Int, Int), (Double, Double))] = Array(((gini,10,40),(0.7772542032989496,0.7730420884389984)), ((gini,10,300),(0.7849615065174265,0.7793665251688522)), ((gini,20,40),(0.9393033733975393,0.904480382215959)), ((gini,20,300),(0.9421715574260792,0.904480382215959)), ((gini,30,40),(0.9972329447406585,0.9341089934177738)), ((gini,30,300),(0.9974352022790551,0.9347964321927579)), ((entropy,10,40),(0.7768755083334409,0.7716672108890302)), ((entropy,10,300),(0.7715307452975122,0.7655318198222971)), ((entropy,20,40),(0.9487578374796128,0.9103407977726984)), ((entropy,20,300),(0.9484781196073622,0.9088971763452317)), ((entropy,30,40),(0.998582045555283,0.9374430714764467)), ((entropy,30,300),(0.9990833860493938,0.9413786584632307)))

scala> val forest = RandomForest. trainClassifier(
| trainData, 7, Map(10 -> 4, 11 -> 40), 20,
| "auto" , "entropy" , 30, 300)
forest: org.apache.spark.mllib.tree.model.RandomForestModel =
TreeEnsembleModel classifier with 20 trees