[Topic-models] Exception when I am using Stanford Topic Modeling Toolbox v.0.4 for doing Labeled LDA

Daniel Ramage dramage at cs.stanford.edu
Sun Jul 15 15:37:01 EDT 2012


Hi Zhang Wen,

This usually happens when you have a stale .cache file - unfortunately the
error message isn't particularly useful.  Try deleting *cache* in the run
folder and running again.

Feel free to email me directly if you have more trouble.  (And sorry if you
sent me an email and I missed it!)

dan

On Sat, Jul 14, 2012 at 12:40 PM, Zhang Wen <zhangwen8277 at gmail.com> wrote:

>  Hi ,
>
> Recently, I got an exception when I am using the Standford TMT(version
> 0.4) for doing Labeled LDA. The stack trace of exception is :
>
> java.lang.ArrayIndexOutOfBoundsException: -1
>         at scalanlp.stage.text.TermCounts$class.getDF(TermFilters.scala:64)
>         at
> scalanlp.stage.text.TermCounts$$anon$2.getDF(TermFilters.scala:84)
>         at
> scalanlp.stage.text.TermMinimumDocumentCountFilter$$anonfun$apply$4$$anonfun$apply$5$$anonfun$apply$6.apply(TermFilters.scala:172)
>         at
> scalanlp.stage.text.TermMinimumDocumentCountFilter$$anonfun$apply$4$$anonfun$apply$5$$anonfun$apply$6.apply(TermFilters.scala:172)
>         at
> scala.collection.TraversableLike$$anonfun$filter$1.apply(TraversableLike.scala:213)
>         at
> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:34)
>         at
> scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:33)
>         at
> scala.collection.TraversableLike$class.filter(TraversableLike.scala:212)
>         at
> scala.collection.mutable.WrappedArray.filter(WrappedArray.scala:33)
>         at
> scalanlp.stage.text.TermMinimumDocumentCountFilter$$anonfun$apply$4$$anonfun$apply$5.apply(TermFilters.scala:172)
>         at
> scalanlp.stage.text.TermMinimumDocumentCountFilter$$anonfun$apply$4$$anonfun$apply$5.apply(TermFilters.scala:172)
>         at scalanlp.stage.Item.map(Item.scala:32)
>         at
> scalanlp.stage.text.TermMinimumDocumentCountFilter$$anonfun$apply$4.apply(TermFilters.scala:172)
>         at
> scalanlp.stage.text.TermMinimumDocumentCountFilter$$anonfun$apply$4.apply(TermFilters.scala:172)
>         at scala.collection.Iterator$$anon$19.next(Iterator.scala:335)
>         at
> edu.stanford.nlp.tmt.data.Dataset$QueuedIterator.enqueue(Dataset.scala:80)
>         at
> edu.stanford.nlp.tmt.data.Dataset$$anon$1$$anon$2.prepare(Dataset.scala:150)
>         at
> edu.stanford.nlp.tmt.data.Dataset$$anon$1$$anon$2.<init>(Dataset.scala:131)
>         at
> edu.stanford.nlp.tmt.data.Dataset$$anon$1.iterator(Dataset.scala:123)
>         at
> edu.stanford.nlp.tmt.model.llda.LabeledLDADataset$class.iterator(LabeledLDADataset.scala:51)
>         at
> edu.stanford.nlp.tmt.model.llda.LabeledLDADataset$$anon$1.iterator(LabeledLDADataset.scala:66)
>         at
> edu.stanford.nlp.tmt.learn.ThreadedModeler.addData(ThreadedModeler.scala:87)
>         at
> edu.stanford.nlp.tmt.learn.Modeler$class.train(Modeler.scala:108)
>         at
> edu.stanford.nlp.tmt.learn.ThreadedModeler.train(ThreadedModeler.scala:35)
>         at
> edu.stanford.nlp.tmt.stage.package$.TrainCVB0LabeledLDA(package.scala:83)
>         at Main$$anon$1.<init>(cross-quest-10-lda-learn.scala:52)
>         at Main$.main(cross-quest-10-lda-learn.scala:1)
>         at Main.main(cross-quest-10-lda-learn.scala)
>         at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>         at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
>         at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
>         at java.lang.reflect.Method.invoke(Unknown Source)
>         at
> scala.tools.nsc.util.ScalaClassLoader$$anonfun$run$1.apply(ScalaClassLoader.scala:78)
>         at
> scala.tools.nsc.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:24)
>         at
> scala.tools.nsc.util.ScalaClassLoader$URLClassLoader.asContext(ScalaClassLoader.scala:88)
>         at
> scala.tools.nsc.util.ScalaClassLoader$class.run(ScalaClassLoader.scala:78)
>         at
> scala.tools.nsc.util.ScalaClassLoader$URLClassLoader.run(ScalaClassLoader.scala:101)
>         at scala.tools.nsc.ObjectRunner$.run(ObjectRunner.scala:33)
>         at scala.tools.nsc.ObjectRunner$.runAndCatch(ObjectRunner.scala:40)
>         at
> scala.tools.nsc.ScriptRunner.scala$tools$nsc$ScriptRunner$$runCompiled(ScriptRunner.scala:171)
>         at
> scala.tools.nsc.ScriptRunner$$anonfun$runScript$1.apply(ScriptRunner.scala:188)
>         at
> scala.tools.nsc.ScriptRunner$$anonfun$runScript$1.apply(ScriptRunner.scala:188)
>         at
> scala.tools.nsc.ScriptRunner$$anonfun$withCompiledScript$1.apply$mcZ$sp(ScriptRunner.scala:157)
>         at
> scala.tools.nsc.ScriptRunner$$anonfun$withCompiledScript$1.apply(ScriptRunner.scala:131)
>         at
> scala.tools.nsc.ScriptRunner$$anonfun$withCompiledScript$1.apply(ScriptRunner.scala:131)
>         at
> scala.tools.nsc.util.package$.waitingForThreads(package.scala:26)
>         at
> scala.tools.nsc.ScriptRunner.withCompiledScript(ScriptRunner.scala:130)
>         at scala.tools.nsc.ScriptRunner.runScript(ScriptRunner.scala:188)
>         at
> scala.tools.nsc.ScriptRunner.runScriptAndCatch(ScriptRunner.scala:201)
>         at
> scala.tools.nsc.MainGenericRunner.runTarget$1(MainGenericRunner.scala:58)
>         at
> scala.tools.nsc.MainGenericRunner.process(MainGenericRunner.scala:80)
>         at
> scala.tools.nsc.MainGenericRunner$.main(MainGenericRunner.scala:89)
>         at scala.tools.nsc.MainGenericRunner.main(MainGenericRunner.scala)
>         at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>         at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
>         at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
>         at java.lang.reflect.Method.invoke(Unknown Source)
>         at edu.stanford.nlp.tmt.TMTMain$.main(TMTMain.scala:57)
>         at edu.stanford.nlp.tmt.TMTMain.main(TMTMain.scala)
>
> The main operations I did are:
>
>    1. I got the top terms by doing the LDA on a document.
>     2. Create a CSV file that contains three columns. The first column is
>    the sequence number, the second column are the top terms which are
>    separated by space. The third the column is the document.
>    3. Modify the example (
>    http://nlp.stanford.edu/software/tmt/tmt-0.4/examples/example-6-llda-learn.scala)
>    a little bit in order to adapt to my CSV file. Changed the indexes of
>    column actually.
>     4. Doing the Labeled LDA on the generated CSV file.
>
>
> Can anyone please help me with this issue?  Thanks!
>
> Best regards,
> Zhang Wen
>
>
>
>
>
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>
>
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