PySparkのFP成長とRDDの使い方は？

FP成長を使用して、以下のRDDの関連付けルールがあるかどうかを知りたいと思います。 documentationから私は次のことを試してみました：PySparkのFP成長とRDDの使い方は？

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)

しかし、私はこの例外を得た：

--------------------------------------------------------------------------- 
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

したがって、FP-成長実装を使用する正しい方法は何ですか？。

出典

2016-07-07 john doe

これは間違っています：transactions = spark_df.map(lambda line: line.strip().split(' '))です。この行を試してみてください：

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

解決策を提供する必要があります。

出典

2016-07-07 17:59:21

私はそれについてもわかりません。 –

助けてくれてありがとう...私はフォーマットについて疑問を抱いていた。 freqItemset（items = ['NONE']、freq = 526790）FreqItemset（items = ['ARREST、BOOKED']、freq = 206403） '。どのように私は関連ルールを抽出する必要がありますか... ...それはsparkで可能ですか？ドキュメントでは、関連ルール部分のためのPythonサポートは見ませんでした。 –

私は分かりません。私はそれがJavaで可能だと思うhttps://spark.apache.org/docs/latest/mllib-frequent-pattern-mining.html#association-rules –

PySparkのFP成長とRDDの使い方は？

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