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From Joseph Bradley <jos...@databricks.com>
Subject Re: net.razorvine.pickle.PickleException in Pyspark
Date Mon, 25 Apr 2016 22:59:33 GMT
Thanks for your work on this.  Can we continue discussing on the JIRA?

On Sun, Apr 24, 2016 at 9:39 AM, Caique Marques <caiquermarques95@gmail.com>
wrote:

> Hello, everyone!
>
> I'm trying to implement the association rules in Python. I got implement
> an association by a frequent element, works as expected (example can be
> seen here
> <https://github.com/mrcaique/spark/blob/master/examples/src/main/python/mllib/fpgrowth_example.py#L36-L40>).
>
>
> Now, my challenge is to implement by a custom RDD. I study the structure
> of Spark and how it implement Python functions of machine learning
> algorithms. The implementations can be seen in the fork
> <https://github.com/mrcaique/spark>.
>
> The example for a custom RDD for association rule can be seen here
> <https://github.com/mrcaique/spark/blob/master/examples/src/main/python/mllib/association_rules_example.py>,
> in the line 33 the output is:
>
> MapPartitionsRDD[10] at mapPartitions at PythonMLLibAPI.scala:1533
>
> It is ok. Testing the Scala example, the structure returned is a
> MapPartitions. But, when I try use a *foreach* in this collection:
>
> net.razorvine.pickle.PickleException: expected zero arguments for
> construction of ClassDict (for numpy.core.multiarray._reconstruct)
>     at
> net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
>     at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
>     at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
>     at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
>     at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
>     at
> org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1547)
>     at
> org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1546)
>     at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:396)
>     at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:396)
>     at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
>     at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:77)
>     at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:45)
>     at org.apache.spark.scheduler.Task.run(Task.scala:81)
>     at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>     at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>     at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>     at java.lang.Thread.run(Thread.java:745)
>
> What is this? What does mean? Any help or tip is welcome.
>
> Thanks,
> Caique.
>

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