Regression fit error in Apache Spark regression algorithm - apache-spark

Question

I work on regression on Apache Spark. And I get everytime same error. I can't solve it.
model = LinearRegression(featuresCol = 'features', labelCol='label').fit(df)
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-80-3156cdf2ca47> in <module>
----> 1 model = lr.fit(df)
C:\ProgramData\Anaconda3\lib\site-packages\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, "
C:\ProgramData\Anaconda3\lib\site-packages\pyspark\ml\wrapper.py in _fit(self, dataset)
293
294 def _fit(self, dataset):
--> 295 java_model = self._fit_java(dataset)
296 model = self._create_model(java_model)
297 return self._copyValues(model)
C:\ProgramData\Anaconda3\lib\site-packages\pyspark\ml\wrapper.py in _fit_java(self, dataset)
290 """
291 self._transfer_params_to_java()
--> 292 return self._java_obj.fit(dataset._jdf)
293
294 def _fit(self, dataset):
C:\ProgramData\Anaconda3\lib\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:
C:\ProgramData\Anaconda3\lib\site-packages\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()
C:\ProgramData\Anaconda3\lib\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 o762.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 66.0 failed 1 times, most recent failure: Lost task 3.0 in stage 66.0 (TID 133, localhost, executor driver): scala.MatchError: [null,1.0,[34.0,147.0,249.0,2006.0,7.0,30.0,38.0,2.0]] (of class org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema)
at org.apache.spark.ml.regression.LinearRegression.$anonfun$train$2(LinearRegression.scala:325)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.TraversableOnce.foldLeft(TraversableOnce.scala:160)
at scala.collection.TraversableOnce.foldLeft$(TraversableOnce.scala:158)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1429)
at scala.collection.TraversableOnce.aggregate(TraversableOnce.scala:217)
at scala.collection.TraversableOnce.aggregate$(TraversableOnce.scala:217)
at scala.collection.AbstractIterator.aggregate(Iterator.scala:1429)
at org.apache.spark.rdd.RDD.$anonfun$treeAggregate$3(RDD.scala:1145)
at org.apache.spark.rdd.RDD.$anonfun$treeAggregate$5(RDD.scala:1146)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:801)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:801)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
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.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:411)
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.failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1876)
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:1876)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2158)
at org.apache.spark.rdd.RDD.$anonfun$fold$1(RDD.scala:1098)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.fold(RDD.scala:1092)
at org.apache.spark.rdd.RDD.$anonfun$treeAggregate$1(RDD.scala:1161)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1137)
at org.apache.spark.ml.optim.WeightedLeastSquares.fit(WeightedLeastSquares.scala:105)
at org.apache.spark.ml.regression.LinearRegression.$anonfun$train$1(LinearRegression.scala:345)
at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:183)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:183)
at org.apache.spark.ml.regression.LinearRegression.train(LinearRegression.scala:319)
at org.apache.spark.ml.regression.LinearRegression.train(LinearRegression.scala:176)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:118)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: scala.MatchError: [null,1.0,[34.0,147.0,249.0,2006.0,7.0,30.0,38.0,2.0]] (of class org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema)
at org.apache.spark.ml.regression.LinearRegression.$anonfun$train$2(LinearRegression.scala:325)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.TraversableOnce.foldLeft(TraversableOnce.scala:160)
at scala.collection.TraversableOnce.foldLeft$(TraversableOnce.scala:158)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1429)
at scala.collection.TraversableOnce.aggregate(TraversableOnce.scala:217)
at scala.collection.TraversableOnce.aggregate$(TraversableOnce.scala:217)
at scala.collection.AbstractIterator.aggregate(Iterator.scala:1429)
at org.apache.spark.rdd.RDD.$anonfun$treeAggregate$3(RDD.scala:1145)
at org.apache.spark.rdd.RDD.$anonfun$treeAggregate$5(RDD.scala:1146)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:801)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:801)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
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.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:411)
at org.apache.spark.util.Utils$.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
What is the reason? df is dataframe. How can I solve it? I tried it in my computer and databricks, the result is same. My dataframe is features=[8 numbers], label= one number like [2.0,92.0,1.0,2002.0,9.0,37.0,2.0,2.0] and 99.4