pysparkword2vecapache-spark-mllibmlp

PySpark ArrayIndexOutOfBoundsException error during model fit: How can I diagnose and fix the issue?


I am working on a PySpark project where I'm trying to fit a MultilayerPerceptronClassifier model to my text data using the fit method.I am using the Word2ve model provided bu Mllib to extract features . However, I keep running into an ArrayIndexOutOfBoundsException error when I try to run the fit method. Specifically, the error message says :

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-1-d46ecefd3281> in <module>
     41 # Use Word2Vec to generate word embeddings
     42 word2Vec_pipeline = Pipeline(stages=[tokenizer, word2Vec, labelIndexer, mlp])
---> 43 word2Vec_model = mlp.fit(trainingData_word2Vec)
     44 word2Vec_predictions = word2Vec_model.transform(testData_word2Vec)
     45 

~\Documents\spark-3.3.1-bin-hadoop3\python\pyspark\ml\base.py in fit(self, dataset, params)
    203                 return self.copy(params)._fit(dataset)
    204             else:
--> 205                 return self._fit(dataset)
    206         else:
    207             raise TypeError(

~\Documents\spark-3.3.1-bin-hadoop3\python\pyspark\ml\wrapper.py in _fit(self, dataset)
    381 
    382     def _fit(self, dataset: DataFrame) -> JM:
--> 383         java_model = self._fit_java(dataset)
    384         model = self._create_model(java_model)
    385         return self._copyValues(model)

~\Documents\spark-3.3.1-bin-hadoop3\python\pyspark\ml\wrapper.py in _fit_java(self, dataset)
    378 
    379         self._transfer_params_to_java()
--> 380         return self._java_obj.fit(dataset._jdf)
    381 
    382     def _fit(self, dataset: DataFrame) -> JM:

~\anaconda3\lib\site-packages\py4j\java_gateway.py in __call__(self, *args)
   1319 
   1320         answer = self.gateway_client.send_command(command)
-> 1321         return_value = get_return_value(
   1322             answer, self.gateway_client, self.target_id, self.name)
   1323 

~\Documents\spark-3.3.1-bin-hadoop3\python\pyspark\sql\utils.py in deco(*a, **kw)
    188     def deco(*a: Any, **kw: Any) -> Any:
    189         try:
--> 190             return f(*a, **kw)
    191         except Py4JJavaError as e:
    192             converted = convert_exception(e.java_exception)

~\anaconda3\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
    324             value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
    325             if answer[1] == REFERENCE_TYPE:
--> 326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
    328                     format(target_id, ".", name), value)

Py4JJavaError: An error occurred while calling o181.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 15.0 failed 1 times, most recent failure: Lost task 0.0 in stage 15.0 (TID 39) (DESKTOP-HILGIEG executor driver): java.lang.ArrayIndexOutOfBoundsException
    at java.lang.System.arraycopy(Native Method)
    at org.apache.spark.ml.ann.DataStacker.$anonfun$stack$4(Layer.scala:665)
    at org.apache.spark.ml.ann.DataStacker.$anonfun$stack$4$adapted(Layer.scala:664)
    at scala.collection.immutable.List.foreach(List.scala:431)
    at org.apache.spark.ml.ann.DataStacker.$anonfun$stack$3(Layer.scala:664)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
    at org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:224)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:302)
    at org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1518)
    at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1445)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1509)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1332)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:376)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:327)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:136)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2672)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2608)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2607)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2607)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1182)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1182)
    at scala.Option.foreach(Option.scala:407)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1182)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2860)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2802)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2791)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:952)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2228)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2249)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2268)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2293)
    at org.apache.spark.rdd.RDD.count(RDD.scala:1274)
    at org.apache.spark.mllib.optimization.LBFGS$.runLBFGS(LBFGS.scala:195)
    at org.apache.spark.mllib.optimization.LBFGS.optimizeWithLossReturned(LBFGS.scala:154)
    at org.apache.spark.ml.ann.FeedForwardTrainer.train(Layer.scala:855)
    at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.$anonfun$train$1(MultilayerPerceptronClassifier.scala:228)
    at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)
    at scala.util.Try$.apply(Try.scala:213)
    at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)
    at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:184)
    at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:93)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:151)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:115)
    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:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
    at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ArrayIndexOutOfBoundsException
    at java.lang.System.arraycopy(Native Method)
    at org.apache.spark.ml.ann.DataStacker.$anonfun$stack$4(Layer.scala:665)
    at org.apache.spark.ml.ann.DataStacker.$anonfun$stack$4$adapted(Layer.scala:664)
    at scala.collection.immutable.List.foreach(List.scala:431)
    at org.apache.spark.ml.ann.DataStacker.$anonfun$stack$3(Layer.scala:664)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
    at org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:224)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:302)
    at org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1518)
    at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1445)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1509)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1332)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:376)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:327)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:136)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more

I'm not sure what's causing this error, but it seems like it might be related to how I'm handling my data or how I'm using PySpark. I've checked my data and my code, but I can't seem to find any null values . Can anyone provide some insight into what might be going wrong here and how I can fix it?

I traid to fit the MultilayerPerceptronClassifier of Mllib using features extracted by Word2vec and i keep getting java.lang.ArrayIndexOutOfBoundsException. Although I have checked the null values and I have none.


Solution

  • If you are encountering an ArrayIndexOutOfBoundsException error in PySpark when trying to fit a machine learning model using the fit method, one possible solution is to add a normalizer to your data before calling the model.

    The ArrayIndexOutOfBoundsException error can occur when your data is not normalized or scaled appropriately, which can cause the model to try to access array indices that are out of bounds. i fixed this error by adding a normalizer in my data using PySpark's Normalizer class.