Pyspark py4j PickleException：“构造ClassDict的预期零参数”

Question

这个问题针对熟悉py4j的人 - 可以帮助解决酸洗错误。我正在尝试向pyspark添加一个方法PythonMLLibAPI，它接受一个namedtuple的RDD，做一些工作，并以RDD的形式返回一个结果。

此方法以PYthonMLLibAPI.trainALSModel（）方法为模型，其类似的现有相关部分为：

  def trainALSModel(
    ratingsJRDD: JavaRDD[Rating],
    .. )

用于建模新代码的现有 python Rating类是：

class Rating(namedtuple("Rating", ["user", "product", "rating"])):
    def __reduce__(self):
        return Rating, (int(self.user), int(self.product), float(self.rating))

这是尝试所以这里是相关的类：

新 python类pyspark.mllib.clustering.MatrixEntry：

from collections import namedtuple
class MatrixEntry(namedtuple("MatrixEntry", ["x","y","weight"])):
    def __reduce__(self):
        return MatrixEntry, (long(self.x), long(self.y), float(self.weight))

新的方法 foobarRDD 在PythonMLLibAPI中：

  def foobarRdd(
    data: JavaRDD[MatrixEntry]): RDD[FooBarResult] = {
    val rdd = data.rdd.map { d => FooBarResult(d.i, d.j, d.value, d.i * 100 + d.j * 10 + d.value)}
    rdd
  }

现在让我们尝试一下：

from pyspark.mllib.clustering import MatrixEntry

def convert_to_MatrixEntry(tuple):
  return MatrixEntry(*tuple)

from pyspark.mllib.clustering import *
pic = PowerIterationClusteringModel(2)
tups = [(1,2,3),(4,5,6),(12,13,14),(15,7,8),(16,17,16.5)]
trdd = sc.parallelize(map(convert_to_MatrixEntry,tups))

# print out the RDD on python side just for validation
print "%s" %(repr(trdd.collect()))

from pyspark.mllib.common import callMLlibFunc
pic = callMLlibFunc("foobar", trdd)

结果的相关部分：

[(1,2)=3.0, (4,5)=6.0, (12,13)=14.0, (15,7)=8.0, (16,17)=16.5]

表示输入rdd为'whole'。然而，酸洗不开心：

5/04/27 21:15:44 ERROR Executor: Exception in task 6.0 in stage 1.0 (TID 14)
net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict
(for pyspark.mllib.clustering.MatrixEntry)
    at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
    at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:617)
    at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:170)
    at net.razorvine.pickle.Unpickler.load(Unpickler.java:84)
    at net.razorvine.pickle.Unpickler.loads(Unpickler.java:97)
    at org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(PythonMLLibAPI.scala:1167)
    at org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(PythonMLLibAPI.scala:1166)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
    at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1523)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1523)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
    at org.apache.spark.scheduler.Task.run(Task.scala:64)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:212)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:724)

下面是python调用堆栈跟踪的可视化：

Answer 1

我遇到与使用MLlib时相同的错误，结果发现我在其中一个函数中返回了错误的数据类型。它现在在返回值的简单转换后工作。这可能不是您正在寻求的答案，但它至少可以提示您遵循的方向。

Answer 2

有同样的问题，好几次。 numpy类型没有隐式转换为 pyspark.sql.types 。

进行简单的显式转换到本机类型系统。 就我而言，它是：

float(vector_a.dot(vector_b)

Answer 3

我使用Spark版本＆gt; = 2.0收到此错误。

Spark正在将MLlib功能转换为更新的ML命名空间。 因此，有两种类型的SparseVector： ml.linalg.SparseVector和mllib.linalg.SparseVector

有些MLlib函数仍然期望较早的mllib种

from pyspark.ml.linalg import Vectors
# convert ML vector to older MLlib vector
old_vec = Vectors.fromML(new_vec)

HTH