Apache spark apachespark回归算法中的回归拟合错误

我在ApacheSpark上进行回归工作。每次我都会犯同样的错误。我解决不了。 模型=线性回归（featuresCol='features'，labelCol='label'）。拟合（df）

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Py4JJavaError回溯（最近一次调用）
在里面
---->1型号=左后配合（df）
C:\ProgramData\Anaconda3\lib\site packages\pyspark\ml\base.py（self、dataset、params）
130返回自复制（参数）.\u拟合（数据集）
131其他：
-->132返回自拟合（数据集）
133其他：
134 raise VALUERROR（“参数必须是参数映射或参数映射的列表/元组，”
C:\ProgramData\Anaconda3\lib\site packages\pyspark\ml\wrapper.py in\u fit（self，dataset）
293
294定义拟合（自我，数据集）：
-->295 java_model=self._fit_java（数据集）
296 model=self.\u创建\u模型（java\u模型）
297返回自身值。\u复制值（型号）
java中的C:\ProgramData\Anaconda3\lib\site packages\pyspark\ml\wrapper.py（self，dataset）
290         """
291 self._transfer_params_to_java（）
-->292返回self.\u java.\u obj.fit（数据集.\u jdf）
293
294定义拟合（自我，数据集）：
C:\ProgramData\Anaconda3\lib\site packages\py4j\java\u gateway.py in\uuuuu调用（self，*args）
1255 answer=self.gateway\u client.send\u命令（command）
1256返回值=获取返回值(
->1257应答，self.gateway_客户端，self.target_id，self.name）
1258
1259对于临时参数中的临时参数：
C:\ProgramData\Anaconda3\lib\site packages\pyspark\sql\utils.py in deco（*a，**kw）
61 def装饰（*a，**千瓦）：
62尝试：
--->63返回f（*a，**kw）
64除py4j.protocol.Py4JJavaError外的其他错误为e:
65 s=e.java_exception.toString（）
C:\ProgramData\Anaconda3\lib\site packages\py4j\protocol.py在get\u return\u值中（应答、网关\u客户端、目标\u id、名称）
326 raise Py4JJavaError(
327“调用{0}{1}{2}时出错。\n”。
-->328格式（目标id，“.”，名称），值）
329其他：
330升起Py4JError(
Py4JJavaError:调用o762.fit时出错。
：org.apache.SparkException:Job因阶段失败而中止：阶段66.0中的任务3失败1次，最近的失败：阶段66.0中的任务3.0丢失（TID 133，localhost，executor driver）：scala.MatchError:[null，1.0，[34.0147.0249.02006.0,7.0,30.0,38.0,2.0]（属于org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema类）
在org.apache.spark.ml.regression.LinearRegression.$anonfun$train$2（LinearRegression.scala:325）
在scala.collection.Iterator$$anon$10.next（Iterator.scala:459）
位于scala.collection.Iterator.foreach（Iterator.scala:941）
位于scala.collection.Iterator.foreach$（Iterator.scala:941）
位于scala.collection.AbstractIterator.foreach（迭代器.scala:1429）
位于scala.collection.TraversableOnce.foldLeft（TraversableOnce.scala:160）
位于scala.collection.TraversableOnce.foldLeft$（TraversableOnce.scala:158）
位于scala.collection.AbstractIterator.foldLeft（Iterator.scala:1429）
位于scala.collection.TraversableOnce.aggregate（TraversableOnce.scala:217）
位于scala.collection.TraversableOnce.aggregate$（TraversableOnce.scala:217）
位于scala.collection.AbstractIterator.aggregate（Iterator.scala:1429）
在org.apache.spark.rdd.rdd.$anonfun$treeaggreegate$3（rdd.scala:1145）
在org.apache.spark.rdd.rdd.$anonfun$treeaggreegate$5（rdd.scala:1146）
在org.apache.spark.rdd.rdd.$anonfun$mapPartitions$2（rdd.scala:801）
位于org.apache.spark.rdd.rdd.$anonfun$mapPartitions$2$adapted（rdd.scala:801）
位于org.apache.spark.rdd.MapPartitionsRDD.compute（MapPartitionsRDD.scala:52）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:324）
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:288）
位于org.apache.spark.rdd.MapPartitionsRDD.compute（MapPartitionsRDD.scala:52）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:324）
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:288）
在org.apache.spark.scheduler.ShuffleMapTask.runTask（ShuffleMapTask.scala:99）上
在org.apache.spark.scheduler.ShuffleMapTask.runTask（ShuffleMapTask.scala:55）上
位于org.apache.spark.scheduler.Task.run（Task.scala:121）
在org.apache.spark.executor.executor$TaskRunner.$annfun$run$3（executor.scala:411）
位于org.apache.spark.util.Utils$.tryWithSafeFinally（Utils.scala:1360）
位于org.apache.spark.executor.executor$TaskRunner.run（executor.scala:414）
位于java.util.concurrent.ThreadPoolExecutor.runWorker（ThreadPoolExecutor.java:1149）
位于java.util.concurrent.ThreadPoolExecutor$Worker.run（ThreadPoolExecutor.java:624）
运行（Thread.java:748）
驱动程序堆栈跟踪：
位于org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages（DAGScheduler.scala:1889）
位于org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2（DAGScheduler.scala:1877）
位于org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted（DAGScheduler.scala:1876）
位于scala.collection.mutable.resizeblearray.foreach（resizeblearray.scala:62）
位于scala.collection.mutable.resizeblearray.foreach$（resizeblearray.scala:55）
位于scala.collection.mutable.ArrayBuffer.foreach（ArrayBuffer.scala:49）
位于org.apache.spark.scheduler.DAGScheduler.abortStage（DAGScheduler.scala:1876）
位于org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1（DAGScheduler.scala:926）
位于org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted（DAGScheduler.scala:926）
位于scala.Option.foreach（Option.scala:274）
位于org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed（DAGScheduler.scala:926）
位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive（DAGScheduler.scala:21
---------------------------------------------------------------------------
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