我想使用FP-growth来了解下面的RDD是否有相关的关联规则。来自 documentation我尝试了以下方法:
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|
+--------------+--------------------+---------+----------+--------------+------------------+
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)
但是，我遇到了这个异常:
---------------------------------------------------------------------------
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
那么，使用 FP-Growth 实现的正确方法是什么？
最佳答案
这是错误的:transactions = spark_df.map(lambda line: line.strip().split(' '))。尝试删除此行:
>>> FPGrowth.train(
... spark_df.rdd.map(lambda x: list(set(x))),
... minSupport=0.2, numPartitions=10)
它应该提供一个解决方案。
关于python - 如何将 PySpark 的 FP-growth 与 RDD 结合使用？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/38252121/