Spark中元件Mllib的學習4之examples中的MovieLensALS修改本地執行
阿新 • • 發佈：2019-02-20
環境：
spark-1.5.2
在idea中自己定義的project中local模式執行，需要注意幾個地方：
1.檔案匯入：
MovieLensALS.scala
和AbstractParams.scala
其中繼承了MovieLensALSAbstractParams，如果不匯入AbstractParams會報錯
2.maven 的pom匯入包
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" - 配置問題
idea報錯，執行沒問題，用idea改倒是跑不了
(org.apache.spark.mllib.recommendation.MatrixFactorizationModel, org.apache.spark.rdd.RDD[(Int, Array[Array[Float]])], org.apache.spark.rdd.RDD[(Int, Array[Array[Float]])]) required: org.apache.spark.mllib.recommendation.MatrixFactorizationModel val rmse = computeRmse(model, test, params.implicitPrefs) ^另外師兄對ALS改了，包是2015.12.2，改了之後會輸出詳細的hangc。。。。
沒改過的是2015.11.4
4.執行結果：
（1）第一種引數配置
D:\1win7\java\jdk\bin\java -Didea.launcher.port=7537 "-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.MovieLensALS file/data/mllib/input/sample_movielens_data.txt
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 22:25:17 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2016-05-16 22:25:23 WARN MetricsSystem:71 - Using default name DAGScheduler for source because spark.app.id is not set.
2016-05-16 22:25:28 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!
Got 1501 ratings from 30 users on 100 movies.
Training: 1186, test: 315.
2016-05-16 22:25:34 WARN BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
2016-05-16 22:25:34 WARN BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
2016-05-16 22:25:34 WARN LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK
2016-05-16 22:25:34 WARN LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefLAPACK
Test RMSE = 1.4182063007342702.
2016-05-16 22:25:38 WARN QueuedThreadPool:145 - 4 threads could not be stopped
Process finished with exit code 0
（2） 第二種引數配置：
--rank 5 --numIterations 20 --lambda 1.0 --kryo file/data/mllib/input/sample_movielens_data.txt結果：
D:\1win7\java\jdk\bin\java -Didea.launcher.port=7532 "-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.MovieLensALS --rank 5 --numIterations 20 --lambda 1.0 --kryo file/data/mllib/input/sample_movielens_data.txt
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 22:30:14 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2016-05-16 22:30:16 WARN MetricsSystem:71 - Using default name DAGScheduler for source because spark.app.id is not set.
2016-05-16 22:30:19 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!
Got 1501 ratings from 30 users on 100 movies.
Training: 1194, test: 307.
2016-05-16 22:30:24 WARN BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
2016-05-16 22:30:24 WARN BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
2016-05-16 22:30:24 WARN LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK
2016-05-16 22:30:24 WARN LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefLAPACK
Test RMSE = 1.4115509902774126.
Process finished with exit code 0
5.原始檔：
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// scalastyle:off println
package apache.spark.mllib.learning.recommend
import scala.collection.mutable
import org.apache.log4j.{Level, Logger}
import scopt.OptionParser
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.SparkContext._
import org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}
import org.apache.spark.rdd.RDD
/**
* An example app for ALS on MovieLens data (http://grouplens.org/datasets/movielens/).
* Run with
* {{{
* bin/run-example org.apache.spark.examples.mllib.MovieLensALS
* }}}
* A synthetic dataset in MovieLens format can be found at `data/mllib/sample_movielens_data.txt`.
* If you use it as a template to create your own app, please use `spark-submit` to submit your app.
*/
object MovieLensALS {
case class Params(
input: String = null,
kryo: Boolean = false,
numIterations: Int = 20,
lambda: Double = 1.0,
rank: Int = 10,
numUserBlocks: Int = -1,
numProductBlocks: Int = -1,
implicitPrefs: Boolean = false) extends AbstractParams[Params]
def main(args: Array[String]) {
val defaultParams = Params()
val parser = new OptionParser[Params]("MovieLensALS") {
head("MovieLensALS: an example app for ALS on MovieLens data.")
opt[Int]("rank")
.text(s"rank, default: ${defaultParams.rank}")
.action((x, c) => c.copy(rank = x))
opt[Int]("numIterations")
.text(s"number of iterations, default: ${defaultParams.numIterations}")
.action((x, c) => c.copy(numIterations = x))
opt[Double]("lambda")
.text(s"lambda (smoothing constant), default: ${defaultParams.lambda}")
.action((x, c) => c.copy(lambda = x))
opt[Unit]("kryo")
.text("use Kryo serialization")
.action((_, c) => c.copy(kryo = true))
opt[Int]("numUserBlocks")
.text(s"number of user blocks, default: ${defaultParams.numUserBlocks} (auto)")
.action((x, c) => c.copy(numUserBlocks = x))
opt[Int]("numProductBlocks")
.text(s"number of product blocks, default: ${defaultParams.numProductBlocks} (auto)")
.action((x, c) => c.copy(numProductBlocks = x))
opt[Unit]("implicitPrefs")
.text("use implicit preference")
.action((_, c) => c.copy(implicitPrefs = true))
arg[String]("<input>")
.required()
.text("input paths to a MovieLens dataset of ratings")
.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.MovieLensALS \
| examples/target/scala-*/spark-examples-*.jar \
| --rank 5 --numIterations 20 --lambda 1.0 --kryo \
| data/mllib/sample_movielens_data.txt
""".stripMargin)
}
parser.parse(args, defaultParams).map { params =>
run(params)
} getOrElse {
System.exit(1)
}
}
def run(params: Params) {
val conf = new SparkConf().setAppName(s"MovieLensALS with $params").setMaster("local[4]")
if (params.kryo) {
conf.registerKryoClasses(Array(classOf[mutable.BitSet], classOf[Rating]))
.set("spark.kryoserializer.buffer", "8m")
}
val sc = new SparkContext(conf)
Logger.getRootLogger.setLevel(Level.WARN)
val implicitPrefs = params.implicitPrefs
val ratings = sc.textFile(params.input).map { line =>
val fields = line.split("::")
if (implicitPrefs) {
/*
* MovieLens ratings are on a scale of 1-5:
* 5: Must see
* 4: Will enjoy
* 3: It's okay
* 2: Fairly bad
* 1: Awful
* So we should not recommend a movie if the predicted rating is less than 3.
* To map ratings to confidence scores, we use
* 5 -> 2.5, 4 -> 1.5, 3 -> 0.5, 2 -> -0.5, 1 -> -1.5. This mappings means unobserved
* entries are generally between It's okay and Fairly bad.
* The semantics of 0 in this expanded world of non-positive weights
* are "the same as never having interacted at all".
*/
Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble - 2.5)
} else {
Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
}
}.cache()
val numRatings = ratings.count()
val numUsers = ratings.map(_.user).distinct().count()
val numMovies = ratings.map(_.product).distinct().count()
println(s"Got $numRatings ratings from $numUsers users on $numMovies movies.")
val splits = ratings.randomSplit(Array(0.8, 0.2))
val training = splits(0).cache()
val test = if (params.implicitPrefs) {
/*
* 0 means "don't know" and positive values mean "confident that the prediction should be 1".
* Negative values means "confident that the prediction should be 0".
* We have in this case used some kind of weighted RMSE. The weight is the absolute value of
* the confidence. The error is the difference between prediction and either 1 or 0,
* depending on whether r is positive or negative.
*/
splits(1).map(x => Rating(x.user, x.product, if (x.rating > 0) 1.0 else 0.0))
} else {
splits(1)
}.cache()
val numTraining = training.count()
val numTest = test.count()
println(s"Training: $numTraining, test: $numTest.")
ratings.unpersist(blocking = false)
val model = new ALS()
.setRank(params.rank)
.setIterations(params.numIterations)
.setLambda(params.lambda)
.setImplicitPrefs(params.implicitPrefs)
.setUserBlocks(params.numUserBlocks)
.setProductBlocks(params.numProductBlocks)
.run(training)
val rmse = computeRmse(model, test, params.implicitPrefs)
println(s"Test RMSE = $rmse.")
sc.stop()
}
/** Compute RMSE (Root Mean Squared Error). */
def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean)
: Double = {
def mapPredictedRating(r: Double): Double = {
if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r
}
val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product)))
val predictionsAndRatings = predictions.map{ x =>
((x.user, x.product), mapPredictedRating(x.rating))
}.join(data.map(x => ((x.user, x.product), x.rating))).values
math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 - x._2)).mean())
}
}
// scalastyle:on println