LogesticRegression fit() function is throwing this error

Question

i'm following datacamp pyspark tutorial series and on chapter 04 Model tuning and selection in fitting the model, I'm getting this error when i execute these line

best_lr = lr.fit(training)

Error

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-102-88042cb88c20> in <module>()
----> 1 best_lr = lr.fit(training)

/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/ml/base.py in fit(self, dataset, params)
    130                 return self.copy(params)._fit(dataset)
    131             else:
--> 132                 return self._fit(dataset)
    133         else:
    134             raise ValueError("Params must be either a param map or a list/tuple of param maps, "

/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/ml/wrapper.py in _fit(self, dataset)
    286 
    287     def _fit(self, dataset):
--> 288         java_model = self._fit_java(dataset)
    289         model = self._create_model(java_model)
    290         return self._copyValues(model)

/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/ml/wrapper.py in _fit_java(self, dataset)
    283         """
    284         self._transfer_params_to_java()
--> 285         return self._java_obj.fit(dataset._jdf)
    286 
    287     def _fit(self, dataset):

/usr/hdp/current/spark2-client/python/lib/py4j-0.10.6-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1158         answer = self.gateway_client.send_command(command)
   1159         return_value = get_return_value(
-> 1160             answer, self.gateway_client, self.target_id, self.name)
   1161 
   1162         for temp_arg in temp_args:

/usr/hdp/current/spark2-client/python/lib/pyspark.zip/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()

/usr/hdp/current/spark2-client/python/lib/py4j-0.10.6-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    318                 raise Py4JJavaError(
    319                     "An error occurred while calling {0}{1}{2}.\n".
--> 320                     format(target_id, ".", name), value)
    321             else:
    322                 raise Py4JError(

Py4JJavaError: An error occurred while calling o596.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 60.0 failed 1 times, most recent failure: Lost task 2.0 in stage 60.0 (TID 86, localhost, executor driver): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$3: (struct<month_double_VectorAssembler_42f79ae7f99735f04859:double,air_time_double_VectorAssembler_42f79ae7f99735f04859:double,carrier_fact:vector,dest_fact:vector,plane_age_double_VectorAssembler_42f79ae7f99735f04859:double>) => vector)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.sort_addToSorter$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:216)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1092)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1083)
    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1018)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1083)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:809)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Values to assemble cannot be null.
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:163)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:146)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
    at org.apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:146)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:99)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:98)
    ... 24 more

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2131)
    at org.apache.spark.rdd.RDD$$anonfun$fold$1.apply(RDD.scala:1092)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.fold(RDD.scala:1086)
    at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1155)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1131)
    at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:518)
    at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:488)
    at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:278)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:118)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$3: (struct<month_double_VectorAssembler_42f79ae7f99735f04859:double,air_time_double_VectorAssembler_42f79ae7f99735f04859:double,carrier_fact:vector,dest_fact:vector,plane_age_double_VectorAssembler_42f79ae7f99735f04859:double>) => vector)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.sort_addToSorter$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:216)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1092)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1083)
    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1018)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1083)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:809)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more
Caused by: org.apache.spark.SparkException: Values to assemble cannot be null.
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:163)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:146)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
    at org.apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:146)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:99)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:98)
    ... 24 more

Tools

I'm using online pyspark cluter with Cloudxlabs.com(trail version)

Possible duplicate of [SparkException: Values to assemble cannot be null](https://stackoverflow.com/questions/41362295/sparkexception-values-to-assemble-cannot-be-null) — 10465355, Dec 04 '18 at 11:16

score 1 · Answer 1 · answered Dec 04 '18 at 10:40

1

May be there are some NULL values in the data set. You'll have to take care of those first.

As explained by the error "Values to assemble cannot be null."

answered Dec 04 '18 at 10:40

Mi8Guy

37
9

how can i remove those null values? – runningmark Dec 04 '18 at 10:41
@runningmark you can refer to [this link](https://www.kaggle.com/pavansanagapati/simple-tutorial-on-how-to-handle-missing-data) – Mi8Guy Dec 04 '18 at 10:47

score 0 · Answer 2 · answered Dec 05 '18 at 10:22

0

Apart from removing or imputing missing values, they can be replaced with mean, median values.
Second option is using xgboost for regression which will automatically handle missing values.

answered Dec 05 '18 at 10:22

DJ6968

109
1
2

score 0 · Answer 3 · answered Dec 06 '18 at 08:43

 df = pd.DataFrame({'Last_Name': ['Smith', None, 'Brown'], 
                   'First_Name': ['John', 'Mike', 'Bill'],
                   'Age': [35, 45, None]})


print(df)
  Last_Name First_Name   Age
0     Smith       John  35.0
1      None       Mike  45.0
2     Brown       Bill   NaN

df2 = df.dropna()

print(df2)
  Last_Name First_Name   Age
0     Smith       John  35.0

Also xgboost can be applied as below:
https://www.datacamp.com/community/tutorials/xgboost-in-python

LogesticRegression fit() function is throwing this error

3 Answers3