Hi Li,
I tried out your code and sample data in both local mode and Spark
Standalone and it ran correctly with output that looks good. Sorry, I
don't have a YARN cluster setup right now, so maybe the error you are
seeing is specific to that. Btw, I am running the latest Spark code from
the master branch. Hope that helps some!
Bryan
On Mon, Jan 4, 2016 at 8:42 PM, Li Li <fancye...@gmail.com> wrote:
> 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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>
