我想使用FP-growth来确定下面的RDD中是否存在相关的关联规则。我根据文档尝试了以下方法：
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|+--------------+--------------------+---------+----------+--------------+------------------+仅显示前两行from pyspark.mllib.fpm import FPGrowthmodel = FPGrowth.train(spark_df.rdd, minSupport=0.2, numPartitions=10)result = model.freqItemsets().collect()for fi in result: print(fi)然而，我遇到了以下异常：
---------------------------------------------------------------------------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，正确使用FP-Growth实现的方法是什么？
回答：
这是错误的：transactions = spark_df.map(lambda line: line.strip().split(' '))。删除此行并尝试：
>>> FPGrowth.train(... spark_df.rdd.map(lambda x: list(set(x))),... minSupport=0.2, numPartitions=10)这应该能提供一个解决方案。
