anyone could help? the problem is very easy to reproduce. What's wrong? On Wed, Dec 30, 2015 at 8:59 PM, Li Li <fancye...@gmail.com> wrote: > I use a small data and reproduce the problem. > But I don't know my codes are correct or not because I am not familiar > with spark. > So I first post my codes here. If it's correct, then I will post the data. > one line of my data like: > > { "time":"08-09-17","cmtUrl":"2094361" > ,"rvId":"rev_10000020","webpageUrl":"http://www.dianping.com/shop/2094361","word_vec":[0,1,2,3,4,5,6,2,7,8,9 > > ,10,11,12,13,14,15,16,8,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,32,35,36,37,38,15,39,40,41,42,5,43,44,17,45,46,42,47,26,48,49]} > > it's a json file which contains webpageUrl and word_vec which is the > encoded words. > The first step is to prase the input rdd to a rdd of VectorUrl. > BTW, if public VectorUrl call(String s) return null, is it ok? > Then follow the example Index documents with unique IDs > Then I create a rdd to map id to url so after lda training, I can find > the url of the document. Then save this rdd to hdfs. > Then create corpus rdd and train > > The exception stack is > > 15/12/30 20:45:42 ERROR yarn.ApplicationMaster: User class threw > exception: java.lang.IndexOutOfBoundsException: (454,0) not in > [-58,58) x [-100,100) > java.lang.IndexOutOfBoundsException: (454,0) not in [-58,58) x [-100,100) > at breeze.linalg.DenseMatrix$mcD$sp.update$mcD$sp(DenseMatrix.scala:112) > at > org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:534) > at > org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:531) > at > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) > at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) > at > org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix$lzycompute(LDAModel.scala:531) > at > org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix(LDAModel.scala:523) > at com.mobvoi.knowledgegraph.textmining.lda.ReviewLDA.main(ReviewLDA.java:89) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) > at > org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:525) > > > ==========here is my codes============== > > SparkConf conf = new SparkConf().setAppName(ReviewLDA.class.getName()); > > JavaSparkContext sc = new JavaSparkContext(conf); > > > // Load and parse the data > > JavaRDD<String> data = sc.textFile(inputDir + "/*"); > > JavaRDD<VectorUrl> parsedData = data.map(new Function<String, > VectorUrl>() { > > public VectorUrl call(String s) { > > JsonParser parser = new JsonParser(); > > JsonObject jo = parser.parse(s).getAsJsonObject(); > > if (!jo.has("word_vec") || !jo.has("webpageUrl")) { > > return null; > > } > > JsonArray word_vec = jo.get("word_vec").getAsJsonArray(); > > String url = jo.get("webpageUrl").getAsString(); > > double[] values = new double[word_vec.size()]; > > for (int i = 0; i < values.length; i++) > > values[i] = word_vec.get(i).getAsInt(); > > return new VectorUrl(Vectors.dense(values), url); > > } > > }); > > > > // Index documents with unique IDs > > JavaPairRDD<Long, VectorUrl> id2doc = > JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map( > > new Function<Tuple2<VectorUrl, Long>, Tuple2<Long, VectorUrl>>() { > > public Tuple2<Long, VectorUrl> call(Tuple2<VectorUrl, Long> doc_id) > { > > return doc_id.swap(); > > } > > })); > > JavaPairRDD<Long, String> id2Url = JavaPairRDD.fromJavaRDD(id2doc > > .map(new Function<Tuple2<Long, VectorUrl>, Tuple2<Long, String>>() { > > @Override > > public Tuple2<Long, String> call(Tuple2<Long, VectorUrl> > id2doc) throws Exception { > > return new Tuple2(id2doc._1, id2doc._2.url); > > } > > })); > > id2Url.saveAsTextFile(id2UrlPath); > > JavaPairRDD<Long, Vector> corpus = JavaPairRDD.fromJavaRDD(id2doc > > .map(new Function<Tuple2<Long, VectorUrl>, Tuple2<Long, Vector>>() { > > @Override > > public Tuple2<Long, Vector> call(Tuple2<Long, VectorUrl> > id2doc) throws Exception { > > return new Tuple2(id2doc._1, id2doc._2.vec); > > } > > })); > > corpus.cache(); > > > // Cluster the documents into three topics using LDA > > DistributedLDAModel ldaModel = (DistributedLDAModel) new > LDA().setMaxIterations(iterNumber) > > .setK(topicNumber).run(corpus); > > On Wed, Dec 30, 2015 at 3:34 PM, Li Li <fancye...@gmail.com> wrote: >> I will use a portion of data and try. will the hdfs block affect >> spark?(if so, it's hard to reproduce) >> >> On Wed, Dec 30, 2015 at 3:22 AM, Joseph Bradley <jos...@databricks.com> >> wrote: >>> Hi Li, >>> >>> I'm wondering if you're running into the same bug reported here: >>> https://issues.apache.org/jira/browse/SPARK-12488 >>> >>> I haven't figured out yet what is causing it. Do you have a small corpus >>> which reproduces this error, and which you can share on the JIRA? If so, >>> that would help a lot in debugging this failure. >>> >>> Thanks! >>> Joseph >>> >>> On Sun, Dec 27, 2015 at 7:26 PM, Li Li <fancye...@gmail.com> wrote: >>>> >>>> I ran my lda example in a yarn 2.6.2 cluster with spark 1.5.2. >>>> it throws exception in line: Matrix topics = ldaModel.topicsMatrix(); >>>> But in yarn job history ui, it's successful. What's wrong with it? >>>> I submit job with >>>> .bin/spark-submit --class Myclass \ >>>> --master yarn-client \ >>>> --num-executors 2 \ >>>> --driver-memory 4g \ >>>> --executor-memory 4g \ >>>> --executor-cores 1 \ >>>> >>>> >>>> My codes: >>>> >>>> corpus.cache(); >>>> >>>> >>>> // Cluster the documents into three topics using LDA >>>> >>>> DistributedLDAModel ldaModel = (DistributedLDAModel) new >>>> >>>> LDA().setOptimizer("em").setMaxIterations(iterNumber).setK(topicNumber).run(corpus); >>>> >>>> >>>> // Output topics. Each is a distribution over words (matching word >>>> count vectors) >>>> >>>> System.out.println("Learned topics (as distributions over vocab of >>>> " + ldaModel.vocabSize() >>>> >>>> + " words):"); >>>> >>>> //Line81, exception here: Matrix topics = ldaModel.topicsMatrix(); >>>> >>>> for (int topic = 0; topic < topicNumber; topic++) { >>>> >>>> System.out.print("Topic " + topic + ":"); >>>> >>>> for (int word = 0; word < ldaModel.vocabSize(); word++) { >>>> >>>> System.out.print(" " + topics.apply(word, topic)); >>>> >>>> } >>>> >>>> System.out.println(); >>>> >>>> } >>>> >>>> >>>> ldaModel.save(sc.sc(), modelPath); >>>> >>>> >>>> Exception in thread "main" java.lang.IndexOutOfBoundsException: >>>> (1025,0) not in [-58,58) x [-100,100) >>>> >>>> at >>>> breeze.linalg.DenseMatrix$mcD$sp.update$mcD$sp(DenseMatrix.scala:112) >>>> >>>> at >>>> org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:534) >>>> >>>> at >>>> org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:531) >>>> >>>> at >>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) >>>> >>>> at >>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) >>>> >>>> at >>>> org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix$lzycompute(LDAModel.scala:531) >>>> >>>> at >>>> org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix(LDAModel.scala:523) >>>> >>>> at >>>> com.mobvoi.knowledgegraph.textmining.lda.ReviewLDA.main(ReviewLDA.java:81) >>>> >>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>> >>>> at >>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>>> >>>> at >>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>> >>>> at java.lang.reflect.Method.invoke(Method.java:606) >>>> >>>> at >>>> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:674) >>>> >>>> at >>>> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180) >>>> >>>> at >>>> org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205) >>>> >>>> at >>>> org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120) >>>> >>>> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) >>>> >>>> 15/12/23 00:01:16 INFO spark.SparkContext: Invoking stop() from shutdown >>>> hook >>>> >>>> --------------------------------------------------------------------- >>>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org >>>> For additional commands, e-mail: dev-h...@spark.apache.org >>>> >>>
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