Chapter 7: Spark exception in def eval(labelpred)

[marko7460]

This exception is thrown in the last step of logistic_regression.ipynb Code:

def eval(labelpred):
    cancel = labelpred.filter(lambda label, pred: pred < 0.7)
    nocancel = labelpred.filter(lambda label, pred: pred >= 0.7)
    corr_cancel = cancel.filter(lambda label, pred: label == int(pred >= 0.7)).count()
    corr_nocancel = nocancel.filter(lambda label, pred: label == int(pred >= 0.7)).count()

    cancel_denom = cancel.count()
    nocancel_denom = nocancel.count()
    if cancel_denom == 0:
        cancel_denom = 1
    if nocancel_denom == 0:
        nocancel_denom = 1
    return {'total_cancel': cancel.count(), \
            'correct_cancel': float(corr_cancel)/cancel_denom, \
            'total_noncancel': nocancel.count(), \
            'correct_noncancel': float(corr_nocancel)/nocancel_denom \
           }

# Evaluate model
lrmodel.clearThreshold() # so it returns probabilities
labelpred = examples.map(lambda p: (p.label, lrmodel.predict(p.features)))
print('All flights:')
#print(eval(labelpred))

# keep only those examples near the decision threshold
print('Flights near decision threshold:')
labelpred = labelpred.filter(lambda label, pred: pred > 0.65 and pred < 0.75)
print(eval(labelpred))

Exception:

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-73-b7f891377acf> in <module>
     26 print('Flights near decision threshold:')
     27 labelpred = labelpred.filter(lambda label, pred: pred > 0.65 and pred < 0.75)
---> 28 print(eval(labelpred))

<ipython-input-73-b7f891377acf> in eval(labelpred)
      2     cancel = labelpred.filter(lambda label, pred: pred < 0.7)
      3     nocancel = labelpred.filter(lambda label, pred: pred >= 0.7)
----> 4     corr_cancel = cancel.filter(lambda label, pred: label == int(pred >= 0.7)).count()
      5     corr_nocancel = nocancel.filter(lambda label, pred: label == int(pred >= 0.7)).count()
      6 

/usr/lib/spark/python/pyspark/rdd.py in count(self)
   1053         3
   1054         """
-> 1055         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
   1056 
   1057     def stats(self):

/usr/lib/spark/python/pyspark/rdd.py in sum(self)
   1044         6.0
   1045         """
-> 1046         return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
   1047 
   1048     def count(self):

/usr/lib/spark/python/pyspark/rdd.py in fold(self, zeroValue, op)
    915         # zeroValue provided to each partition is unique from the one provided
    916         # to the final reduce call
--> 917         vals = self.mapPartitions(func).collect()
    918         return reduce(op, vals, zeroValue)
    919 

/usr/lib/spark/python/pyspark/rdd.py in collect(self)
    814         """
    815         with SCCallSiteSync(self.context) as css:
--> 816             sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    817         return list(_load_from_socket(sock_info, self._jrdd_deserializer))
    818 

/opt/conda/anaconda/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/usr/lib/spark/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()

/opt/conda/anaconda/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 78.0 failed 1 times, most recent failure: Lost task 0.0 in stage 78.0 (TID 101, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/usr/lib/spark/python/pyspark/rdd.py", line 2499, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "/usr/lib/spark/python/pyspark/rdd.py", line 2499, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "/usr/lib/spark/python/pyspark/rdd.py", line 2499, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "/usr/lib/spark/python/pyspark/rdd.py", line 352, in func
    return f(iterator)
  File "/usr/lib/spark/python/pyspark/rdd.py", line 1055, in <lambda>
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "/usr/lib/spark/python/pyspark/rdd.py", line 1055, in <genexpr>
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
    return f(*args, **kwargs)
TypeError: <lambda>() missing 1 required positional argument: 'pred'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:592)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator$class.foreach(Iterator.scala:891)
    at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
    at scala.collection.generic.Growable$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 org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
    at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
    at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$15.apply(RDD.scala:990)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$15.apply(RDD.scala:990)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$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$$failJobAndIndependentStages(DAGScheduler.scala:1926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1914)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1913)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2147)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2096)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2085)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:990)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:385)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:989)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    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.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/usr/lib/spark/python/pyspark/rdd.py", line 2499, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "/usr/lib/spark/python/pyspark/rdd.py", line 2499, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "/usr/lib/spark/python/pyspark/rdd.py", line 2499, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "/usr/lib/spark/python/pyspark/rdd.py", line 352, in func
    return f(iterator)
  File "/usr/lib/spark/python/pyspark/rdd.py", line 1055, in <lambda>
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "/usr/lib/spark/python/pyspark/rdd.py", line 1055, in <genexpr>
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
    return f(*args, **kwargs)
TypeError: <lambda>() missing 1 required positional argument: 'pred'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:592)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator$class.foreach(Iterator.scala:891)
    at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
    at scala.collection.generic.Growable$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 org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
    at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
    at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$15.apply(RDD.scala:990)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$15.apply(RDD.scala:990)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more

Up to this point I was able to train the model and execute all other steps in the notebook. It's only this step that is failing.

[SothanaV]

in my case edit lambda function like this

def eval(labelpred):
    ''' 
        data = (label, pred)
            data[0] = label
            data[1] = pred
    '''
    cancel = labelpred.filter(lambda data: data[1] < 0.7)
    nocancel = labelpred.filter(lambda data: data[1] >= 0.7)
    corr_cancel = cancel.filter(lambda data: data[0] == int(data[1] >= 0.7)).count()
    corr_nocancel = nocancel.filter(lambda data: data[0] == int(data[1] >= 0.7)).count()

    cancel_denom = cancel.count()
    nocancel_denom = nocancel.count()
    if cancel_denom == 0:
        cancel_denom = 1
    if nocancel_denom == 0:
        nocancel_denom = 1
    return {'total_cancel': cancel.count(), \
            'correct_cancel': float(corr_cancel)/cancel_denom, \
            'total_noncancel': nocancel.count(), \
            'correct_noncancel': float(corr_nocancel)/nocancel_denom \
           }

[lakshmanok]

thanks! merged your PR.