用于构造ClassDict的预期零参数(用于pyspark.ml.linalg.SparseVector) [英] expected zero arguments for construction of ClassDict (for pyspark.ml.linalg.SparseVector)
本文介绍了用于构造ClassDict的预期零参数(用于pyspark.ml.linalg.SparseVector)的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在创建一个LDA模型.
I am working to create a LDA model.
到目前为止，这是我所做的事情-创建了一个unigram，并根据
Here is what I have done so far- created a unigram and converted the dataframe to RDD based on this post.
这是代码:
countVectors = CountVectorizer(inputCol="unigrams", outputCol="features", vocabSize=3, minDF=2.0)
model = countVectors.fit(res)
result = model.transform(res)
result.show(5, truncate=False)
这是数据集
+------------------------------------------------------------------------+---+-------------------+
|unigrams |id |features |
+------------------------------------------------------------------------+---+-------------------+
|[born, furyth, leaguenemesi, rise, (the, leaguenemesi, rise, seri, book]|0 |(3,[0,1],[1.0,1.0])|
|[hous, raven, (the, nightfal, chronicl, book] |1 |(3,[0,1],[1.0,1.0])|
|[law, 101everyth, need, know, american, law, fourth, edit] |2 |(3,[],[]) |
|[hot, summer, night] |3 |(3,[],[]) |
|[wet, bundlemega, collect, sex, stori, (30, book, box, set)] |4 |(3,[0],[1.0]) |
+------------------------------------------------------------------------+---+-------------------+
根据上面的基本数据，我根据我关注的databrick帖子创建了MLLib所需的rdd.
From the above basic data, I created following rdd that MLLib required based on the databrick's post that I am following.
from pyspark.mllib.linalg import Vector, Vectors
rdd_convert = result.rdd
corpus = rdd_convert.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
corpus.take(4)
上面的代码生成以下数据:
This above code generates following data:
[[0,
Row(unigrams=['born', 'furyth', 'leaguenemesi', 'rise', '(the', 'leaguenemesi', 'rise', 'seri', 'book'], id=0, features=SparseVector(3, {0: 1.0, 1: 1.0}))],
[1,
Row(unigrams=['hous', 'raven', '(the', 'nightfal', 'chronicl', 'book'], id=1, features=SparseVector(3, {0: 1.0, 1: 1.0}))],
[2,
Row(unigrams=['law', '101everyth', 'need', 'know', 'american', 'law', 'fourth', 'edit'], id=2, features=SparseVector(3, {}))],
[3,
Row(unigrams=['hot', 'summer', 'night'], id=3, features=SparseVector(3, {}))]]
现在我想在RDD上使用LDA
Now I wanted to use LDA on RDD
from pyspark.mllib.clustering import LDA, LDAModel
# Cluster the documents into three topics using LDA
from pyspark.mllib.linalg import Vectors
type(corpus)
rdd = spark.sparkContext.parallelize(corpus.collect())
type(rdd)
如果我运行ldaModel = LDA.train(rdd)，则会出现以下错误:
If I run ldaModel = LDA.train(rdd), I get following error:
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-33-2abff4618359> in <module>()
----> 1 ldaModel = LDA.train(rdd)
~/Documents/spark/spark-2.2.1-bin-hadoop2.7/python/pyspark/mllib/clustering.py in train(cls, rdd, k, maxIterations, docConcentration, topicConcentration, seed, checkpointInterval, optimizer)
1037 model = callMLlibFunc("trainLDAModel", rdd, k, maxIterations,
1038 docConcentration, topicConcentration, seed,
-> 1039 checkpointInterval, optimizer)
1040 return LDAModel(model)
1041
~/Documents/spark/spark-2.2.1-bin-hadoop2.7/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
~/Documents/spark/spark-2.2.1-bin-hadoop2.7/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
~/Documents/spark/spark-2.2.1-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py in __call__(self, *args)
1131 answer = self.gateway_client.send_command(command)
1132 return_value = get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name)
1134
1135 for temp_arg in temp_args:
~/Documents/spark/spark-2.2.1-bin-hadoop2.7/python/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
~/Documents/spark/spark-2.2.1-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling o401.trainLDAModel.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 26.0 failed 1 times, most recent failure: Lost task 0.0 in stage 26.0 (TID 81, localhost, executor driver): net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.ml.linalg.SparseVector)
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.SerDeBase$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1353)
at org.apache.spark.mllib.api.python.SerDeBase$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1352)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:389)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at scala.collection.AbstractIterator.to(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1354)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.take(RDD.scala:1327)
at org.apache.spark.mllib.clustering.EMLDAOptimizer.initialize(LDAOptimizer.scala:166)
at org.apache.spark.mllib.clustering.EMLDAOptimizer.initialize(LDAOptimizer.scala:80)
at org.apache.spark.mllib.clustering.LDA.run(LDA.scala:331)
at org.apache.spark.mllib.api.python.PythonMLLibAPI.trainLDAModel(PythonMLLibAPI.scala:552)
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:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.ml.linalg.SparseVector)
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.SerDeBase$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1353)
at org.apache.spark.mllib.api.python.SerDeBase$$anonfun$pythonToJava$1$$anonfun$apply$2.apply(PythonMLLibAPI.scala:1352)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:389)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at scala.collection.AbstractIterator.to(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
我试图通过这种方式解决 但没有用.任何解决此问题的帮助将不胜感激
I tried to solve this way but did not work. Any help to solve this will be kindly appreciated
推荐答案
如果使用Spark 2.2，则应使用 pyspark.ml.clustering.LDA 而不是 mllib 之一:
If you use Spark 2.2 you should use pyspark.ml.clustering.LDA not mllib one:
from pyspark.ml.clustering import LDA
LDA().fit(result)
但是，如果您想使 mllib 变体起作用，则正确的格式为 [label，pyspark.mllib.linalg.Vector] :
However if you want to make mllib variant to work you the right format is [label, pyspark.mllib.linalg.Vector]:
from pyspark.mllib.linalg import Vectors as MLlibVectors
from pyspark.mllib.clustering import LDA as MLlibLDA
MLlibLDA.train(
result.select("id", "features").rdd.mapValues(MLlibVectors.fromML).map(list)
)
这篇关于用于构造ClassDict的预期零参数(用于pyspark.ml.linalg.SparseVector)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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