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Posted to dev@spark.apache.org by Caique Marques <ca...@gmail.com> on 2016/04/24 18:39:43 UTC
net.razorvine.pickle.PickleException in Pyspark
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.
Re: net.razorvine.pickle.PickleException in Pyspark
Posted by Joseph Bradley <jo...@databricks.com>.
Thanks for your work on this. Can we continue discussing on the JIRA?
On Sun, Apr 24, 2016 at 9:39 AM, Caique Marques <ca...@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.
>