1解釋
研究ALS的準確率

2.程式碼：

package org.apache.spark.mllib.learning.recommend

import java.text.SimpleDateFormat
import java.util.Date

import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 

import org.apache.spark.mllib.recommendation.Rating

/**
  * Created by xubo on 2016/5/16.
  */
object ALSFromSpark {
  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster("local").setAppName(this.getClass().getSimpleName().filter(!_.equals('$')))
    //    println(this.getClass().getSimpleName
 
().filter(!_.equals('$')))
    //設定環境變數
    val sc = new SparkContext(conf)

    // Load and parse the data
    //    val data = sc.textFile("data/mllib/als/test.data")
    val data = sc.textFile("file/data/mllib/input/test.data")

    val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
      Rating(user.toInt
 
, item.toInt, rate.toDouble)
    })

    // Build the recommendation model using ALS
    val rank = 10
    val numIterations = 10
    val model = ALS.train(ratings, rank, numIterations, 0.01)

    // Evaluate the model on rating data
    val usersProducts = ratings.map { case Rating(user, product, rate) =>
      (user, product)
    }
    val predictions =
      model.predict(usersProducts).map { case Rating(user, product, rate) =>
        ((user, product), rate)
      }
    val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
      ((user, product), rate)
    }.join(predictions)
    val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
      val err = (r1 - r2)
      err * err
    }.mean()
    println("Mean Squared Error = " + MSE)

    // Save and load model
    val iString = new SimpleDateFormat("yyyyMMddHHmmssSSS").format(new Date())
    model.save(sc, "file/data/mllib/output/als/myModelPath" + iString)
    val sameModel = MatrixFactorizationModel.load(sc, "file/data/mllib/output/als/myModelPath" + iString)

    println("user 2 ,top 1")
    var rs = sameModel.recommendProducts(2, 1)

    rs.foreach(println)
    println("user 2 ,top 2")
    rs = sameModel.recommendProducts(2, 2)

    rs.foreach(println)
    println("user 2 ,top 3")
    rs = sameModel.recommendProducts(2, 3)

    rs.foreach(println)
    println("user 2 ,top 4")
    rs = sameModel.recommendProducts(2, 4)

    rs.foreach(println)
    println("user 2 ,top 5")
    rs = sameModel.recommendProducts(2, 5)
    rs.foreach(println)

    println(sameModel.predict(2, 1))
    println(sameModel.predict(2, 2))
    println(sameModel.predict(2, 3))
    println(sameModel.predict(2, 4))
    //    println(sameModel.predict(2,5))//error


  }
}

3.結果：

D:\1win7\java\jdk\bin\java -Didea.launcher.port=7533 "-Didea.launcher.bin.path=D:\1win7\idea\IntelliJ IDEA Community Edition 15.0.4\bin" -Dfile.encoding=UTF-8 -classpath "D:\all\idea\SparkLearning\target\classes;D:\1win7\java\jdk\jre\lib\charsets.jar;D:\1win7\java\jdk\jre\lib\deploy.jar;D:\1win7\java\jdk\jre\lib\ext\access-bridge-64.jar;D:\1win7\java\jdk\jre\lib\ext\dnsns.jar;D:\1win7\java\jdk\jre\lib\ext\jaccess.jar;D:\1win7\java\jdk\jre\lib\ext\localedata.jar;D:\1win7\java\jdk\jre\lib\ext\sunec.jar;D:\1win7\java\jdk\jre\lib\ext\sunjce_provider.jar;D:\1win7\java\jdk\jre\lib\ext\sunmscapi.jar;D:\1win7\java\jdk\jre\lib\ext\zipfs.jar;D:\1win7\java\jdk\jre\lib\javaws.jar;D:\1win7\java\jdk\jre\lib\jce.jar;D:\1win7\java\jdk\jre\lib\jfr.jar;D:\1win7\java\jdk\jre\lib\jfxrt.jar;D:\1win7\java\jdk\jre\lib\jsse.jar;D:\1win7\java\jdk\jre\lib\management-agent.jar;D:\1win7\java\jdk\jre\lib\plugin.jar;D:\1win7\java\jdk\jre\lib\resources.jar;D:\1win7\java\jdk\jre\lib\rt.jar;D:\1win7\scala;D:\1win7\scala\lib;D:\1win7\java\otherJar\spark-assembly-1.5.2-hadoop2.6.0.jar;D:\1win7\java\otherJar\adam-apis_2.10-0.18.3-SNAPSHOT.jar;D:\1win7\java\otherJar\adam-cli_2.10-0.18.3-SNAPSHOT.jar;D:\1win7\java\otherJar\adam-core_2.10-0.18.3-SNAPSHOT.jar;D:\1win7\java\otherJar\SparkCSV\com.databricks_spark-csv_2.10-1.4.0.jar;D:\1win7\java\otherJar\SparkCSV\com.univocity_univocity-parsers-1.5.1.jar;D:\1win7\java\otherJar\SparkCSV\org.apache.commons_commons-csv-1.1.jar;D:\1win7\java\otherJar\SparkAvro\spark-avro_2.10-2.0.1.jar;D:\1win7\java\otherJar\SparkAvro\spark-avro_2.10-2.0.1-javadoc.jar;D:\1win7\java\otherJar\SparkAvro\spark-avro_2.10-2.0.1-sources.jar;D:\1win7\java\otherJar\avro\spark-avro_2.10-2.0.2-SNAPSHOT.jar;D:\1win7\java\otherJar\tachyon\tachyon-assemblies-0.7.1-jar-with-dependencies.jar;D:\1win7\scala\lib\scala-actors-migration.jar;D:\1win7\scala\lib\scala-actors.jar;D:\1win7\scala\lib\scala-library.jar;D:\1win7\scala\lib\scala-reflect.jar;D:\1win7\scala\lib\scala-swing.jar;C:\Users\xubo\.m2\repository\com\github\scopt\scopt_2.10\3.2.0\scopt_2.10-3.2.0.jar;C:\Users\xubo\.m2\repository\org\scala-lang\scala-library\2.10.3\scala-library-2.10.3.jar;D:\1win7\idea\IntelliJ IDEA Community Edition 15.0.4\lib\idea_rt.jar" com.intellij.rt.execution.application.AppMain org.apache.spark.mllib.learning.recommend.ALSFromSpark
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/D:/1win7/java/otherJar/spark-assembly-1.5.2-hadoop2.6.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/D:/1win7/java/otherJar/adam-cli_2.10-0.18.3-SNAPSHOT.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/D:/1win7/java/otherJar/tachyon/tachyon-assemblies-0.7.1-jar-with-dependencies.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
2016-05-17 20:31:54 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2016-05-17 20:31:56 WARN  MetricsSystem:71 - Using default name DAGScheduler for source because spark.app.id is not set.
2016-05-17 20:31:59 WARN  :139 - Your hostname, xubo-PC resolves to a loopback/non-reachable address: fe80:0:0:0:200:5efe:d356:9f8e%20, but we couldn't find any external IP address!
2016-05-17 20:32:02 WARN  BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
2016-05-17 20:32:02 WARN  BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
2016-05-17 20:32:02 WARN  LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK
2016-05-17 20:32:02 WARN  LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefLAPACK
Mean Squared Error = 2.084528769598244E-5
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:10 WARN  MatrixFactorizationModel:71 - User factor does not have a partitioner. Prediction on individual records could be slow.
2016-05-17 20:32:10 WARN  MatrixFactorizationModel:71 - User factor is not cached. Prediction could be slow.
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
user 2 ,top 1
2016-05-17 20:32:10 WARN  MatrixFactorizationModel:71 - Product factor does not have a partitioner. Prediction on individual records could be slow.
2016-05-17 20:32:10 WARN  MatrixFactorizationModel:71 - Product factor is not cached. Prediction could be slow.
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
Rating(2,3,4.9920036874935985)
user 2 ,top 2
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
Rating(2,3,4.9920036874935985)
Rating(2,1,4.24074985241211)
user 2 ,top 3
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
Rating(2,3,4.9920036874935985)
Rating(2,1,4.24074985241211)
Rating(2,2,1.0009153672703281)
user 2 ,top 4
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
Rating(2,3,4.9920036874935985)
Rating(2,1,4.24074985241211)
Rating(2,2,1.0009153672703281)
Rating(2,4,1.0009153672703281)
user 2 ,top 5
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
Rating(2,3,4.9920036874935985)
Rating(2,1,4.24074985241211)
Rating(2,2,1.0009153672703281)
Rating(2,4,1.0009153672703281)
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
4.24074985241211
2016-05-17 20:32:10 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:11 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
1.0009153672703281
2016-05-17 20:32:11 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:11 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
4.9920036874935985
2016-05-17 20:32:11 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-17 20:32:11 WARN  ParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
1.0009153672703281

Process finished with exit code 0

1,1,5.0
1,2,1.0
1,3,5.0
1,4,1.0
2,2,1.0
2,3,5.0
2,4,1.0
3,1,1.0
3,2,5.0
3,3,1.0
3,4,5.0
4,1,1.0
4,2,5.0
4,3,1.0
4,4,5.0

分析，實際test.data刪除了2，1，5.0
預測為2，1，4.24074985241211，所以準確率還好，不算很高
2，2；2，3，2，4預測準確率都很高，原來訓練資料中有，所以可以理解

另外計算MSE時用訓練資料來當測試資料，結果為：Mean Squared Error = 2.084528769598244E-5，值很小，說明ALS擬合不錯
但是實際操作中訓練資料和測試資料最好分開