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.