更多代码请见：https://github.com/xubo245/SparkLearning
spark-1.5.2
1解释
使用spark 官网推荐的算法,使用了隐式转换
2.代码：
package apache.spark.mllib.learning.recommendimport java.text.SimpleDateFormat
import java.util.Dateimport org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}
import org.apache.spark.{SparkConf, SparkContext}/**
* Created by xubo on 2016/5/16.
*/
object ALSImplicitFromSpark {
def main(args: Array[String]) {
val conf = new SparkConf().setMaster("local").setAppName(this.getClass().getSimpleName().filter(!_.equals('$')))
//println(this.getClass().getSimpleName().filter(!_.equals('$')))
//设置环境变量
val sc = new SparkContext(conf)// Load and parse the data
//val data = https://www.it610.com/article/sc.textFile("data/mllib/als/test.data")
val data = https://www.it610.com/article/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 = 10val alpha = 0.01
val lambda = 0.01
val model = ALS.trainImplicit(ratings, rank, numIterations, lambda, alpha)
//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, "myModelPath" + iString)
val sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
}
}
3.结果：
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 apache.spark.mllib.learning.recommend.ALSImplicitFromSpark
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]
2016-05-16 22:46:20 WARNNativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2016-05-16 22:46:22 WARNMetricsSystem:71 - Using default name DAGScheduler for source because spark.app.id is not set.
2016-05-16 22:46:25 WARN:139 - Your hostname, xubo-PC resolves to a loopback/non-reachable address: fe80:0:0:0:200:5efe:ca26:541d%20, but we couldn't find any external IP address!
2016-05-16 22:46:27 WARNBLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
2016-05-16 22:46:27 WARNBLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
2016-05-16 22:46:27 WARNLAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK
2016-05-16 22:46:27 WARNLAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefLAPACK
Mean Squared Error = 8.011502126568448
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.
2016-05-16 22:46:36 WARNParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-16 22:46:36 WARNMatrixFactorizationModel:71 - User factor does not have a partitioner. Prediction on individual records could be slow.
2016-05-16 22:46:36 WARNMatrixFactorizationModel:71 - User factor is not cached. Prediction could be slow.
2016-05-16 22:46:36 WARNParquetRecordReader:193 - Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
2016-05-16 22:46:36 WARNMatrixFactorizationModel:71 - Product factor does not have a partitioner. Prediction on individual records could be slow.
2016-05-16 22:46:36 WARNMatrixFactorizationModel:71 - Product factor is not cached. Prediction could be slow.Process finished with exit code 0
【Spark中组件Mllib的学习6之ALS测试（apache spark 含隐式转换）】参考
【1】http://spark.apache.org/docs/1.5.2/mllib-guide.html
【2】http://spark.apache.org/docs/1.5.2/mllib-collaborative-filtering.html#collaborative-filtering
【3】https://github.com/xubo245/SparkLearning
