如何在RDD中使用PySpark的FP增长？

时间：2016-07-07 17:51:00

标签： python apache-spark machine-learning pyspark rdd

我想使用FP-growth来了解下面的RDD是否存在相关的关联规则。从documentation我尝试了以下内容：

sqlContext = SQLContext(sc)

spark_df = sqlContext.createDataFrame(pandas_df[['Category','Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address']])

spark_df.show(2)

+--------------+--------------------+---------+----------+--------------+------------------+
|      Category|            Descript|DayOfWeek|PdDistrict|    Resolution|           Address|
+--------------+--------------------+---------+----------+--------------+------------------+
|      WARRANTS|      WARRANT ARREST|Wednesday|  NORTHERN|ARREST, BOOKED|OAK ST / LAGUNA ST|
|OTHER OFFENSES|TRAFFIC VIOLATION...|Wednesday|  NORTHERN|ARREST, BOOKED|OAK ST / LAGUNA ST|
+--------------+--------------------+---------+----------+--------------+------------------+
only showing top 2 rows

from pyspark.mllib.fpm import FPGrowth

model = FPGrowth.train(spark_df.rdd, minSupport=0.2, numPartitions=10)

result = model.freqItemsets().collect()

for fi in result:

    print(fi)

然而，我遇到了这个例外：

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-7-fa62e885b01c> in <module>()
      4 #transactions = spark_df.map(lambda line: line.strip().split(' '))
      5 
----> 6 model = FPGrowth.train(spark_df.rdd, minSupport=0.2, numPartitions=10)
      7 
      8 result = model.freqItemsets().collect()

/Users/user/spark-1.6.2-bin-hadoop2.6/python/pyspark/mllib/fpm.py in train(cls, data, minSupport, numPartitions)
     75             parallel FP-growth (default: same as input data).
     76         """
---> 77         model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
     78         return FPGrowthModel(model)
     79 

/Users/user/spark-1.6.2-bin-hadoop2.6/python/pyspark/mllib/common.py in callMLlibFunc(name, *args)
    128     sc = SparkContext.getOrCreate()
    129     api = getattr(sc._jvm.PythonMLLibAPI(), name)
--> 130     return callJavaFunc(sc, api, *args)
    131 
    132 

/Users/user/spark-1.6.2-bin-hadoop2.6/python/pyspark/mllib/common.py in callJavaFunc(sc, func, *args)
    121     """ Call Java Function """
    122     args = [_py2java(sc, a) for a in args]
--> 123     return _java2py(sc, func(*args))
    124 
    125 

/Users/user/spark-1.6.2-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
    811         answer = self.gateway_client.send_command(command)
    812         return_value = get_return_value(
--> 813             answer, self.gateway_client, self.target_id, self.name)
    814 
    815         for temp_arg in temp_args:

/Users/user/spark-1.6.2-bin-hadoop2.6/python/pyspark/sql/utils.py in deco(*a, **kw)
     43     def deco(*a, **kw):
     44         try:
---> 45             return f(*a, **kw)
     46         except py4j.protocol.Py4JJavaError as e:
     47             s = e.java_exception.toString()

/Users/user/spark-1.6.2-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    306                 raise Py4JJavaError(
    307                     "An error occurred while calling {0}{1}{2}.\n".
--> 308                     format(target_id, ".", name), value)
    309             else:
    310                 raise Py4JError(

Py4JJavaError: An error occurred while calling o76.trainFPGrowthModel.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 3, localhost): net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.sql.types._create_row)
    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:1507)
    at org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1506)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1631)
    at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1157)
    at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1157)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
    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)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
    at org.apache.spark.rdd.RDD.count(RDD.scala:1157)
    at org.apache.spark.mllib.fpm.FPGrowth.run(FPGrowth.scala:114)
    at org.apache.spark.mllib.api.python.PythonMLLibAPI.trainFPGrowthModel(PythonMLLibAPI.scala:565)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)
Caused by: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.sql.types._create_row)
    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:1507)
    at org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1506)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1631)
    at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1157)
    at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1157)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more

因此，使用FP-Growth实施的正确方法是什么？。

1 个答案:

答案 0 :(得分：1)

这是错误的：transactions = spark_df.map(lambda line: line.strip().split(' '))。试一下这行：

>>> FPGrowth.train(
... spark_df.rdd.map(lambda x: list(set(x))),
... minSupport=0.2, numPartitions=10)

它应该提供解决方案。

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