无法显示 pyspark 数据框，即使它很小（'.show()' 问题）
Can't show a pyspark dataframe, even it is small ('.show()' problem)
data ，这是输出 - DataFrame[features: vector, label: int]
我是如何得到 'data'
import pyspark.sql.functions as F
from pyspark.ml.feature import VectorAssembler
...
cols = [F.col(field[0]).cast('double') if field[1] == 'int' else F.col(field[0]) for field in cdr.dtypes]
cdr = cdr.select(cols)
cdr.printSchema()
train, test = cdr.randomSplit([0.8, 0.2])
catCols = [ x for (x,dataTypes) in train.dtypes if dataTypes == "string" ]
numCols = [ x for (x,dataTypes) in train.dtypes if (dataTypes == "double") & (x != ("flag_bad")) & (x != ("subs_no"))]
from pyspark.ml import Pipeline
assemblerInput = [x for x in numCols]
vector_assembler = VectorAssembler(inputCols = assemblerInput, outputCol = "vectorAssembler_features")
stages = []
stages += [vector_assembler]
pipeline = Pipeline().setStages(stages)
model = pipeline.fit(train)
pp_df = model.transform(test)
from mmlspark.lightgbm._LightGBMClassifier import _LightGBMClassifier
data = pp_df.select(
F.col("vectorAssembler_features").alias("features"),
F.col("flag_bad").alias("label"))
data = data.withColumn("label",data.label.cast(IntegerType()))
备注：
该列全部为数字
我尝试展示的数据
data = data.limit(5)
data.show(5)
这是输出
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-38-de129acebf9b> in <module>
----> 1 new.show(5)
/opt/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/lib/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
378 """
379 if isinstance(truncate, bool) and truncate:
--> 380 print(self._jdf.showString(n, 20, vertical))
381 else:
382 print(self._jdf.showString(n, int(truncate), vertical))
/opt/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/lib/spark/python/lib/py4j-0.10.7-src.zip/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:
/opt/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/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/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/lib/spark/python/lib/py4j-0.10.7-src.zip/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 o4536.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 20 in stage 1267.0 failed 4 times, most recent failure: Lost task 20.3 in stage 1267.0 (TID 137415, adaijktwrk07.adreach.co, executor 439): org.apache.spark.SparkException: Failed to execute user defined function($anonfun: (struct<arpu_sum_l1:double,arpu_sum_l2:double,arpu_sum_l3:double,arpu_sum_l4:double,arpu_sum_l5:double,arpu_sum_l6:double,sms_sum_l1:double,sms_sum_l2:double,sms_sum_l3:double,sms_sum_l4:double,sms_sum_l5:double,sms_sum_l6:double,voice_sum_l1:double,voice_sum_l2:double,voice_sum_l3:double,voice_sum_l4:double,voice_sum_l5:double,voice_sum_l6:double,data_sum_l1:double,data_sum_l2:double,data_sum_l3:double,data_sum_l4:double,data_sum_l5:double,data_sum_l6:double,vocrev_sum_l1:double,vocrev_sum_l2:double,vocrev_sum_l3:double,vocrev_sum_l4:double,vocrev_sum_l5:double,vocrev_sum_l6:double,smsrev_sum_l1:double,smsrev_sum_l2:double,smsrev_sum_l3:double,smsrev_sum_l4:double,smsrev_sum_l5:double,smsrev_sum_l6:double,datarev_sum_l1:double,datarev_sum_l2:double,datarev_sum_l3:double,datarev_sum_l4:double,datarev_sum_l5:double,datarev_sum_l6:double,vasrev_sum_l1:double,vasrev_sum_l2:double,vasrev_sum_l3:double,vasrev_sum_l4:double,vasrev_sum_l5:double,vasrev_sum_l6:double,totrev_sum_l1:double,totrev_sum_l2:double,totrev_sum_l3:double,totrev_sum_l4:double,totrev_sum_l5:double,totrev_sum_l6:double,voc_yield_sum_l1:double,voc_yield_sum_l2:double,voc_yield_sum_l3:double,voc_yield_sum_l4:double,voc_yield_sum_l5:double,voc_yield_sum_l6:double,sms_yield_sum_l1:double,sms_yield_sum_l2:double,sms_yield_sum_l3:double,sms_yield_sum_l4:double,sms_yield_sum_l5:double,sms_yield_sum_l6:double,data_yield_sum_l1:double,data_yield_sum_l2:double,data_yield_sum_l3:double,data_yield_sum_l4:double,data_yield_sum_l5:double,data_yield_sum_l6:double,arpu_cumsum_l2:double,arpu_cumsum_l3:double,arpu_cumsum_l4:double,arpu_cumsum_l5:double,arpu_cumsum_l6:double,sms_cumsum_l2:double,sms_cumsum_l3:double,sms_cumsum_l4:double,sms_cumsum_l5:double,sms_cumsum_l6:double,voice_cumsum_l2:double,voice_cumsum_l3:double,voice_cumsum_l4:double,voice_cumsum_l5:double,voice_cumsum_l6:double,data_cumsum_l2:double,data_cumsum_l3:double,data_cumsum_l4:double,data_cumsum_l5:double,data_cumsum_l6:double,vocrev_cumsum_l2:double,vocrev_cumsum_l3:double,vocrev_cumsum_l4:double,vocrev_cumsum_l5:double,vocrev_cumsum_l6:double,smsrev_cumsum_l2:double,smsrev_cumsum_l3:double,smsrev_cumsum_l4:double,smsrev_cumsum_l5:double,smsrev_cumsum_l6:double,datarev_cumsum_l2:double,datarev_cumsum_l3:double,datarev_cumsum_l4:double,datarev_cumsum_l5:double,datarev_cumsum_l6:double,vasrev_cumsum_l2:double,vasrev_cumsum_l3:double,vasrev_cumsum_l4:double,vasrev_cumsum_l5:double,vasrev_cumsum_l6:double,totrev_cumsum_l2:double,totrev_cumsum_l3:double,totrev_cumsum_l4:double,totrev_cumsum_l5:double,totrev_cumsum_l6:double,voc_yield_cumsum_l2:double,voc_yield_cumsum_l3:double,voc_yield_cumsum_l4:double,voc_yield_cumsum_l5:double,voc_yield_cumsum_l6:double,sms_yield_cumsum_l2:double,sms_yield_cumsum_l3:double,sms_yield_cumsum_l4:double,sms_yield_cumsum_l5:double,sms_yield_cumsum_l6:double,data_yield_cumsum_l2:double,data_yield_cumsum_l3:double,data_yield_cumsum_l4:double,data_yield_cumsum_l5:double,data_yield_cumsum_l6:double,pct_vocrev_cumsum_l2:double,pct_vocrev_cumsum_l3:double,pct_vocrev_cumsum_l4:double,pct_vocrev_cumsum_l5:double,pct_vocrev_cumsum_l6:double,pct_smsrev_cumsum_l2:double,pct_smsrev_cumsum_l3:double,pct_smsrev_cumsum_l4:double,pct_smsrev_cumsum_l5:double,pct_smsrev_cumsum_l6:double,pct_datarev_cumsum_l2:double,pct_datarev_cumsum_l3:double,pct_datarev_cumsum_l4:double,pct_datarev_cumsum_l5:double,pct_datarev_cumsum_l6:double,pct_vocrev_sum_l1:double,pct_vocrev_sum_l2:double,pct_vocrev_sum_l3:double,pct_vocrev_sum_l4:double,pct_vocrev_sum_l5:double,pct_vocrev_sum_l6:double,pct_smsrev_sum_l1:double,pct_smsrev_sum_l2:double,pct_smsrev_sum_l3:double,pct_smsrev_sum_l4:double,pct_smsrev_sum_l5:double,pct_smsrev_sum_l6:double,pct_datarev_sum_l1:double,pct_datarev_sum_l2:double,pct_datarev_sum_l3:double,pct_datarev_sum_l4:double,pct_datarev_sum_l5:double,pct_datarev_sum_l6:double,arpu_sum_l14d:double,sms_sum_l14d:double,voice_sum_l14d:double,data_sum_l14d:double,vocrev_sum_l14d:double,smsrev_sum_l14d:double,datarev_sum_l14d:double,vasrev_sum_l14d:double,totrev_sum_l14d:double,arpu_sum_l7d:double,sms_sum_l7d:double,voice_sum_l7d:double,data_sum_l7d:double,vocrev_sum_l7d:double,smsrev_sum_l7d:double,datarev_sum_l7d:double,vasrev_sum_l7d:double,totrev_sum_l7d:double,pct_vocrev_sum_l7d:double,pct_smsrev_sum_l7d:double,pct_datarev_sum_l7d:double,pct_vocrev_sum_l14d:double,pct_smsrev_sum_l14d:double,pct_datarev_sum_l14d:double,reload_sum_l1:double,reload_sum_l2:double,reload_sum_l3:double,reload_sum_l4:double,reload_sum_l5:double,reload_sum_l6:double,reload_min_l1:double,reload_min_l2:double,reload_min_l3:double,reload_min_l4:double,reload_min_l5:double,reload_min_l6:double,reload_max_l1:double,reload_max_l2:double,reload_max_l3:double,reload_max_l4:double,reload_max_l5:double,reload_max_l6:double,reload_avg_l1:double,reload_avg_l2:double,reload_avg_l3:double,reload_avg_l4:double,reload_avg_l5:double,reload_avg_l6:double,rembal_sum_l1:double,rembal_sum_l2:double,rembal_sum_l3:double,rembal_sum_l4:double,rembal_sum_l5:double,rembal_sum_l6:double,rembal_min_l1:double,rembal_min_l2:double,rembal_min_l3:double,rembal_min_l4:double,rembal_min_l5:double,rembal_min_l6:double,rembal_max_l1:double,rembal_max_l2:double,rembal_max_l3:double,rembal_max_l4:double,rembal_max_l5:double,rembal_max_l6:double,rembal_avg_l1:double,rembal_avg_l2:double,rembal_avg_l3:double,rembal_avg_l4:double,rembal_avg_l5:double,rembal_avg_l6:double,reload_cumsum_l2:double,reload_cumsum_l3:double,reload_cumsum_l4:double,reload_cumsum_l5:double,reload_cumsum_l6:double,rembal_cumsum_l2:double,rembal_cumsum_l3:double,rembal_cumsum_l4:double,rembal_cumsum_l5:double,rembal_cumsum_l6:double,pct_rembal_to_reload_l1:double,pct_rembal_to_reload_l2:double,pct_rembal_to_reload_l3:double,pct_rembal_to_reload_l4:double,pct_rembal_to_reload_l5:double,pct_rembal_to_reload_l6:double,packet_sum_l1:double,packet_sum_l2:double,packet_sum_l3:double,packet_sum_l4:double,packet_sum_l5:double,packet_sum_l6:double,packet_min_l1:double,packet_min_l2:double,packet_min_l3:double,packet_min_l4:double,packet_min_l5:double,packet_min_l6:double,packet_max_l1:double,packet_max_l2:double,packet_max_l3:double,packet_max_l4:double,packet_max_l5:double,packet_max_l6:double,packet_avg_l1:double,packet_avg_l2:double,packet_avg_l3:double,packet_avg_l4:double,packet_avg_l5:double,packet_avg_l6:double,packet_cumsum_l2:double,packet_cumsum_l3:double,packet_cumsum_l4:double,packet_cumsum_l5:double,packet_cumsum_l6:double,province_OTHERS:double,province_DKI JAKARTA:double,province_BANTEN:double,province_JAWA TIMUR:double,province_JAWA BARAT:double,province_JAWA TENGAH:double,province_SUMATERA UTARA:double,province_UNDEFINED:double,handset_brand_OTHERS:double,handset_brand_VIVO:double,handset_brand_OPPO:double,handset_brand_REDMI:double,handset_brand_APPLE:double,handset_brand_REALME:double,handset_brand_XIAOMI:double,handset_brand_ASUS:double,handset_brand_SAMSUNG:double,handset_os_OTHERS:double,handset_os_APPLE IOS:double,handset_os_ANDROID:double,handset_os_OTHER:double,handset_type_OTHERS:double,handset_type_SMARTPHONE:double,handset_type_FEATUREPHONE:double,payment_category_POST:double,payment_category_PRE:double,hpos_to_ios_0:double,hpos_to_ios_1:double,hpos_to_ios_UNDEFINED:double,hpos_from_ios_0:double,hpos_from_ios_1:double,hpos_from_ios_UNDEFINED:double,hptype_to_smart_0:double,hptype_to_smart_1:double,hptype_to_smart_UNDEFINED:double,hptype_from_smart_0:double,hptype_from_smart_1:double,hptype_from_smart_UNDEFINED:double,hpbrand_change_0:double,hpbrand_change_1:double,hpbrand_change_UNDEFINED:double>) => struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.ScalaUDF_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at scala.collection.Iterator$$anon.next(Iterator.scala:410)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage464.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:645)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
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:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1289)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Encountered null while assembling a row with handleInvalid = "keep". Consider
removing nulls from dataset or using handleInvalid = "keep" or "skip".
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:287)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:255)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at org.apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:255)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:144)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:143)
... 18 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1879)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1878)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:927)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050)
at org.apache.spark.util.EventLoop$$anon.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2088)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2107)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:375)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
at org.apache.spark.sql.Dataset$$anonfun$head.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$head.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun.apply(Dataset.scala:3370)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId.apply(SQLExecution.scala:80)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun: (struct<arpu_sum_l1:double,arpu_sum_l2:double,arpu_sum_l3:double,arpu_sum_l4:double,arpu_sum_l5:double,arpu_sum_l6:double,sms_sum_l1:double,sms_sum_l2:double,sms_sum_l3:double,sms_sum_l4:double,sms_sum_l5:double,sms_sum_l6:double,voice_sum_l1:double,voice_sum_l2:double,voice_sum_l3:double,voice_sum_l4:double,voice_sum_l5:double,voice_sum_l6:double,data_sum_l1:double,data_sum_l2:double,data_sum_l3:double,data_sum_l4:double,data_sum_l5:double,data_sum_l6:double,vocrev_sum_l1:double,vocrev_sum_l2:double,vocrev_sum_l3:double,vocrev_sum_l4:double,vocrev_sum_l5:double,vocrev_sum_l6:double,smsrev_sum_l1:double,smsrev_sum_l2:double,smsrev_sum_l3:double,smsrev_sum_l4:double,smsrev_sum_l5:double,smsrev_sum_l6:double,datarev_sum_l1:double,datarev_sum_l2:double,datarev_sum_l3:double,datarev_sum_l4:double,datarev_sum_l5:double,datarev_sum_l6:double,vasrev_sum_l1:double,vasrev_sum_l2:double,vasrev_sum_l3:double,vasrev_sum_l4:double,vasrev_sum_l5:double,vasrev_sum_l6:double,totrev_sum_l1:double,totrev_sum_l2:double,totrev_sum_l3:double,totrev_sum_l4:double,totrev_sum_l5:double,totrev_sum_l6:double,voc_yield_sum_l1:double,voc_yield_sum_l2:double,voc_yield_sum_l3:double,voc_yield_sum_l4:double,voc_yield_sum_l5:double,voc_yield_sum_l6:double,sms_yield_sum_l1:double,sms_yield_sum_l2:double,sms_yield_sum_l3:double,sms_yield_sum_l4:double,sms_yield_sum_l5:double,sms_yield_sum_l6:double,data_yield_sum_l1:double,data_yield_sum_l2:double,data_yield_sum_l3:double,data_yield_sum_l4:double,data_yield_sum_l5:double,data_yield_sum_l6:double,arpu_cumsum_l2:double,arpu_cumsum_l3:double,arpu_cumsum_l4:double,arpu_cumsum_l5:double,arpu_cumsum_l6:double,sms_cumsum_l2:double,sms_cumsum_l3:double,sms_cumsum_l4:double,sms_cumsum_l5:double,sms_cumsum_l6:double,voice_cumsum_l2:double,voice_cumsum_l3:double,voice_cumsum_l4:double,voice_cumsum_l5:double,voice_cumsum_l6:double,data_cumsum_l2:double,data_cumsum_l3:double,data_cumsum_l4:double,data_cumsum_l5:double,data_cumsum_l6:double,vocrev_cumsum_l2:double,vocrev_cumsum_l3:double,vocrev_cumsum_l4:double,vocrev_cumsum_l5:double,vocrev_cumsum_l6:double,smsrev_cumsum_l2:double,smsrev_cumsum_l3:double,smsrev_cumsum_l4:double,smsrev_cumsum_l5:double,smsrev_cumsum_l6:double,datarev_cumsum_l2:double,datarev_cumsum_l3:double,datarev_cumsum_l4:double,datarev_cumsum_l5:double,datarev_cumsum_l6:double,vasrev_cumsum_l2:double,vasrev_cumsum_l3:double,vasrev_cumsum_l4:double,vasrev_cumsum_l5:double,vasrev_cumsum_l6:double,totrev_cumsum_l2:double,totrev_cumsum_l3:double,totrev_cumsum_l4:double,totrev_cumsum_l5:double,totrev_cumsum_l6:double,voc_yield_cumsum_l2:double,voc_yield_cumsum_l3:double,voc_yield_cumsum_l4:double,voc_yield_cumsum_l5:double,voc_yield_cumsum_l6:double,sms_yield_cumsum_l2:double,sms_yield_cumsum_l3:double,sms_yield_cumsum_l4:double,sms_yield_cumsum_l5:double,sms_yield_cumsum_l6:double,data_yield_cumsum_l2:double,data_yield_cumsum_l3:double,data_yield_cumsum_l4:double,data_yield_cumsum_l5:double,data_yield_cumsum_l6:double,pct_vocrev_cumsum_l2:double,pct_vocrev_cumsum_l3:double,pct_vocrev_cumsum_l4:double,pct_vocrev_cumsum_l5:double,pct_vocrev_cumsum_l6:double,pct_smsrev_cumsum_l2:double,pct_smsrev_cumsum_l3:double,pct_smsrev_cumsum_l4:double,pct_smsrev_cumsum_l5:double,pct_smsrev_cumsum_l6:double,pct_datarev_cumsum_l2:double,pct_datarev_cumsum_l3:double,pct_datarev_cumsum_l4:double,pct_datarev_cumsum_l5:double,pct_datarev_cumsum_l6:double,pct_vocrev_sum_l1:double,pct_vocrev_sum_l2:double,pct_vocrev_sum_l3:double,pct_vocrev_sum_l4:double,pct_vocrev_sum_l5:double,pct_vocrev_sum_l6:double,pct_smsrev_sum_l1:double,pct_smsrev_sum_l2:double,pct_smsrev_sum_l3:double,pct_smsrev_sum_l4:double,pct_smsrev_sum_l5:double,pct_smsrev_sum_l6:double,pct_datarev_sum_l1:double,pct_datarev_sum_l2:double,pct_datarev_sum_l3:double,pct_datarev_sum_l4:double,pct_datarev_sum_l5:double,pct_datarev_sum_l6:double,arpu_sum_l14d:double,sms_sum_l14d:double,voice_sum_l14d:double,data_sum_l14d:double,vocrev_sum_l14d:double,smsrev_sum_l14d:double,datarev_sum_l14d:double,vasrev_sum_l14d:double,totrev_sum_l14d:double,arpu_sum_l7d:double,sms_sum_l7d:double,voice_sum_l7d:double,data_sum_l7d:double,vocrev_sum_l7d:double,smsrev_sum_l7d:double,datarev_sum_l7d:double,vasrev_sum_l7d:double,totrev_sum_l7d:double,pct_vocrev_sum_l7d:double,pct_smsrev_sum_l7d:double,pct_datarev_sum_l7d:double,pct_vocrev_sum_l14d:double,pct_smsrev_sum_l14d:double,pct_datarev_sum_l14d:double,reload_sum_l1:double,reload_sum_l2:double,reload_sum_l3:double,reload_sum_l4:double,reload_sum_l5:double,reload_sum_l6:double,reload_min_l1:double,reload_min_l2:double,reload_min_l3:double,reload_min_l4:double,reload_min_l5:double,reload_min_l6:double,reload_max_l1:double,reload_max_l2:double,reload_max_l3:double,reload_max_l4:double,reload_max_l5:double,reload_max_l6:double,reload_avg_l1:double,reload_avg_l2:double,reload_avg_l3:double,reload_avg_l4:double,reload_avg_l5:double,reload_avg_l6:double,rembal_sum_l1:double,rembal_sum_l2:double,rembal_sum_l3:double,rembal_sum_l4:double,rembal_sum_l5:double,rembal_sum_l6:double,rembal_min_l1:double,rembal_min_l2:double,rembal_min_l3:double,rembal_min_l4:double,rembal_min_l5:double,rembal_min_l6:double,rembal_max_l1:double,rembal_max_l2:double,rembal_max_l3:double,rembal_max_l4:double,rembal_max_l5:double,rembal_max_l6:double,rembal_avg_l1:double,rembal_avg_l2:double,rembal_avg_l3:double,rembal_avg_l4:double,rembal_avg_l5:double,rembal_avg_l6:double,reload_cumsum_l2:double,reload_cumsum_l3:double,reload_cumsum_l4:double,reload_cumsum_l5:double,reload_cumsum_l6:double,rembal_cumsum_l2:double,rembal_cumsum_l3:double,rembal_cumsum_l4:double,rembal_cumsum_l5:double,rembal_cumsum_l6:double,pct_rembal_to_reload_l1:double,pct_rembal_to_reload_l2:double,pct_rembal_to_reload_l3:double,pct_rembal_to_reload_l4:double,pct_rembal_to_reload_l5:double,pct_rembal_to_reload_l6:double,packet_sum_l1:double,packet_sum_l2:double,packet_sum_l3:double,packet_sum_l4:double,packet_sum_l5:double,packet_sum_l6:double,packet_min_l1:double,packet_min_l2:double,packet_min_l3:double,packet_min_l4:double,packet_min_l5:double,packet_min_l6:double,packet_max_l1:double,packet_max_l2:double,packet_max_l3:double,packet_max_l4:double,packet_max_l5:double,packet_max_l6:double,packet_avg_l1:double,packet_avg_l2:double,packet_avg_l3:double,packet_avg_l4:double,packet_avg_l5:double,packet_avg_l6:double,packet_cumsum_l2:double,packet_cumsum_l3:double,packet_cumsum_l4:double,packet_cumsum_l5:double,packet_cumsum_l6:double,province_OTHERS:double,province_DKI JAKARTA:double,province_BANTEN:double,province_JAWA TIMUR:double,province_JAWA BARAT:double,province_JAWA TENGAH:double,province_SUMATERA UTARA:double,province_UNDEFINED:double,handset_brand_OTHERS:double,handset_brand_VIVO:double,handset_brand_OPPO:double,handset_brand_REDMI:double,handset_brand_APPLE:double,handset_brand_REALME:double,handset_brand_XIAOMI:double,handset_brand_ASUS:double,handset_brand_SAMSUNG:double,handset_os_OTHERS:double,handset_os_APPLE IOS:double,handset_os_ANDROID:double,handset_os_OTHER:double,handset_type_OTHERS:double,handset_type_SMARTPHONE:double,handset_type_FEATUREPHONE:double,payment_category_POST:double,payment_category_PRE:double,hpos_to_ios_0:double,hpos_to_ios_1:double,hpos_to_ios_UNDEFINED:double,hpos_from_ios_0:double,hpos_from_ios_1:double,hpos_from_ios_UNDEFINED:double,hptype_to_smart_0:double,hptype_to_smart_1:double,hptype_to_smart_UNDEFINED:double,hptype_from_smart_0:double,hptype_from_smart_1:double,hptype_from_smart_UNDEFINED:double,hpbrand_change_0:double,hpbrand_change_1:double,hpbrand_change_UNDEFINED:double>) => struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.ScalaUDF_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at scala.collection.Iterator$$anon.next(Iterator.scala:410)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage464.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:645)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
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:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1289)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Caused by: org.apache.spark.SparkException: Encountered null while assembling a row with handleInvalid = "keep". Consider
removing nulls from dataset or using handleInvalid = "keep" or "skip".
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:287)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:255)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at org.apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:255)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:144)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:143)
... 18 more
这是 VectorAssembler 的文档。
https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html
您的堆栈跟踪提到，“在使用 handleInvalid =“keep”组装行时遇到空值。考虑
从数据集中删除空值或使用 handleInvalid = "keep" or "skip"."
因此请尝试使用 handleInvalid="keep" 或 handleInvalid="skip" 设置 VectorAssembler 构造函数
vector_assembler = VectorAssembler(inputCols = assemblerInput, outputCol = "vectorAssembler_features", handleInvalid="keep")
或
vector_assembler = VectorAssembler(inputCols = assemblerInput, outputCol = "vectorAssembler_features", handleInvalid="skip")
data ，这是输出 - DataFrame[features: vector, label: int]
我是如何得到 'data'
import pyspark.sql.functions as F
from pyspark.ml.feature import VectorAssembler
...
cols = [F.col(field[0]).cast('double') if field[1] == 'int' else F.col(field[0]) for field in cdr.dtypes]
cdr = cdr.select(cols)
cdr.printSchema()
train, test = cdr.randomSplit([0.8, 0.2])
catCols = [ x for (x,dataTypes) in train.dtypes if dataTypes == "string" ]
numCols = [ x for (x,dataTypes) in train.dtypes if (dataTypes == "double") & (x != ("flag_bad")) & (x != ("subs_no"))]
from pyspark.ml import Pipeline
assemblerInput = [x for x in numCols]
vector_assembler = VectorAssembler(inputCols = assemblerInput, outputCol = "vectorAssembler_features")
stages = []
stages += [vector_assembler]
pipeline = Pipeline().setStages(stages)
model = pipeline.fit(train)
pp_df = model.transform(test)
from mmlspark.lightgbm._LightGBMClassifier import _LightGBMClassifier
data = pp_df.select(
F.col("vectorAssembler_features").alias("features"),
F.col("flag_bad").alias("label"))
data = data.withColumn("label",data.label.cast(IntegerType()))
备注： 该列全部为数字
我尝试展示的数据
data = data.limit(5)
data.show(5)
这是输出
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-38-de129acebf9b> in <module>
----> 1 new.show(5)
/opt/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/lib/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
378 """
379 if isinstance(truncate, bool) and truncate:
--> 380 print(self._jdf.showString(n, 20, vertical))
381 else:
382 print(self._jdf.showString(n, int(truncate), vertical))
/opt/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/lib/spark/python/lib/py4j-0.10.7-src.zip/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:
/opt/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/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/cloudera/parcels/CDH-7.1.3-1.cdh7.1.3.p0.4992530/lib/spark/python/lib/py4j-0.10.7-src.zip/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 o4536.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 20 in stage 1267.0 failed 4 times, most recent failure: Lost task 20.3 in stage 1267.0 (TID 137415, adaijktwrk07.adreach.co, executor 439): org.apache.spark.SparkException: Failed to execute user defined function($anonfun: (struct<arpu_sum_l1:double,arpu_sum_l2:double,arpu_sum_l3:double,arpu_sum_l4:double,arpu_sum_l5:double,arpu_sum_l6:double,sms_sum_l1:double,sms_sum_l2:double,sms_sum_l3:double,sms_sum_l4:double,sms_sum_l5:double,sms_sum_l6:double,voice_sum_l1:double,voice_sum_l2:double,voice_sum_l3:double,voice_sum_l4:double,voice_sum_l5:double,voice_sum_l6:double,data_sum_l1:double,data_sum_l2:double,data_sum_l3:double,data_sum_l4:double,data_sum_l5:double,data_sum_l6:double,vocrev_sum_l1:double,vocrev_sum_l2:double,vocrev_sum_l3:double,vocrev_sum_l4:double,vocrev_sum_l5:double,vocrev_sum_l6:double,smsrev_sum_l1:double,smsrev_sum_l2:double,smsrev_sum_l3:double,smsrev_sum_l4:double,smsrev_sum_l5:double,smsrev_sum_l6:double,datarev_sum_l1:double,datarev_sum_l2:double,datarev_sum_l3:double,datarev_sum_l4:double,datarev_sum_l5:double,datarev_sum_l6:double,vasrev_sum_l1:double,vasrev_sum_l2:double,vasrev_sum_l3:double,vasrev_sum_l4:double,vasrev_sum_l5:double,vasrev_sum_l6:double,totrev_sum_l1:double,totrev_sum_l2:double,totrev_sum_l3:double,totrev_sum_l4:double,totrev_sum_l5:double,totrev_sum_l6:double,voc_yield_sum_l1:double,voc_yield_sum_l2:double,voc_yield_sum_l3:double,voc_yield_sum_l4:double,voc_yield_sum_l5:double,voc_yield_sum_l6:double,sms_yield_sum_l1:double,sms_yield_sum_l2:double,sms_yield_sum_l3:double,sms_yield_sum_l4:double,sms_yield_sum_l5:double,sms_yield_sum_l6:double,data_yield_sum_l1:double,data_yield_sum_l2:double,data_yield_sum_l3:double,data_yield_sum_l4:double,data_yield_sum_l5:double,data_yield_sum_l6:double,arpu_cumsum_l2:double,arpu_cumsum_l3:double,arpu_cumsum_l4:double,arpu_cumsum_l5:double,arpu_cumsum_l6:double,sms_cumsum_l2:double,sms_cumsum_l3:double,sms_cumsum_l4:double,sms_cumsum_l5:double,sms_cumsum_l6:double,voice_cumsum_l2:double,voice_cumsum_l3:double,voice_cumsum_l4:double,voice_cumsum_l5:double,voice_cumsum_l6:double,data_cumsum_l2:double,data_cumsum_l3:double,data_cumsum_l4:double,data_cumsum_l5:double,data_cumsum_l6:double,vocrev_cumsum_l2:double,vocrev_cumsum_l3:double,vocrev_cumsum_l4:double,vocrev_cumsum_l5:double,vocrev_cumsum_l6:double,smsrev_cumsum_l2:double,smsrev_cumsum_l3:double,smsrev_cumsum_l4:double,smsrev_cumsum_l5:double,smsrev_cumsum_l6:double,datarev_cumsum_l2:double,datarev_cumsum_l3:double,datarev_cumsum_l4:double,datarev_cumsum_l5:double,datarev_cumsum_l6:double,vasrev_cumsum_l2:double,vasrev_cumsum_l3:double,vasrev_cumsum_l4:double,vasrev_cumsum_l5:double,vasrev_cumsum_l6:double,totrev_cumsum_l2:double,totrev_cumsum_l3:double,totrev_cumsum_l4:double,totrev_cumsum_l5:double,totrev_cumsum_l6:double,voc_yield_cumsum_l2:double,voc_yield_cumsum_l3:double,voc_yield_cumsum_l4:double,voc_yield_cumsum_l5:double,voc_yield_cumsum_l6:double,sms_yield_cumsum_l2:double,sms_yield_cumsum_l3:double,sms_yield_cumsum_l4:double,sms_yield_cumsum_l5:double,sms_yield_cumsum_l6:double,data_yield_cumsum_l2:double,data_yield_cumsum_l3:double,data_yield_cumsum_l4:double,data_yield_cumsum_l5:double,data_yield_cumsum_l6:double,pct_vocrev_cumsum_l2:double,pct_vocrev_cumsum_l3:double,pct_vocrev_cumsum_l4:double,pct_vocrev_cumsum_l5:double,pct_vocrev_cumsum_l6:double,pct_smsrev_cumsum_l2:double,pct_smsrev_cumsum_l3:double,pct_smsrev_cumsum_l4:double,pct_smsrev_cumsum_l5:double,pct_smsrev_cumsum_l6:double,pct_datarev_cumsum_l2:double,pct_datarev_cumsum_l3:double,pct_datarev_cumsum_l4:double,pct_datarev_cumsum_l5:double,pct_datarev_cumsum_l6:double,pct_vocrev_sum_l1:double,pct_vocrev_sum_l2:double,pct_vocrev_sum_l3:double,pct_vocrev_sum_l4:double,pct_vocrev_sum_l5:double,pct_vocrev_sum_l6:double,pct_smsrev_sum_l1:double,pct_smsrev_sum_l2:double,pct_smsrev_sum_l3:double,pct_smsrev_sum_l4:double,pct_smsrev_sum_l5:double,pct_smsrev_sum_l6:double,pct_datarev_sum_l1:double,pct_datarev_sum_l2:double,pct_datarev_sum_l3:double,pct_datarev_sum_l4:double,pct_datarev_sum_l5:double,pct_datarev_sum_l6:double,arpu_sum_l14d:double,sms_sum_l14d:double,voice_sum_l14d:double,data_sum_l14d:double,vocrev_sum_l14d:double,smsrev_sum_l14d:double,datarev_sum_l14d:double,vasrev_sum_l14d:double,totrev_sum_l14d:double,arpu_sum_l7d:double,sms_sum_l7d:double,voice_sum_l7d:double,data_sum_l7d:double,vocrev_sum_l7d:double,smsrev_sum_l7d:double,datarev_sum_l7d:double,vasrev_sum_l7d:double,totrev_sum_l7d:double,pct_vocrev_sum_l7d:double,pct_smsrev_sum_l7d:double,pct_datarev_sum_l7d:double,pct_vocrev_sum_l14d:double,pct_smsrev_sum_l14d:double,pct_datarev_sum_l14d:double,reload_sum_l1:double,reload_sum_l2:double,reload_sum_l3:double,reload_sum_l4:double,reload_sum_l5:double,reload_sum_l6:double,reload_min_l1:double,reload_min_l2:double,reload_min_l3:double,reload_min_l4:double,reload_min_l5:double,reload_min_l6:double,reload_max_l1:double,reload_max_l2:double,reload_max_l3:double,reload_max_l4:double,reload_max_l5:double,reload_max_l6:double,reload_avg_l1:double,reload_avg_l2:double,reload_avg_l3:double,reload_avg_l4:double,reload_avg_l5:double,reload_avg_l6:double,rembal_sum_l1:double,rembal_sum_l2:double,rembal_sum_l3:double,rembal_sum_l4:double,rembal_sum_l5:double,rembal_sum_l6:double,rembal_min_l1:double,rembal_min_l2:double,rembal_min_l3:double,rembal_min_l4:double,rembal_min_l5:double,rembal_min_l6:double,rembal_max_l1:double,rembal_max_l2:double,rembal_max_l3:double,rembal_max_l4:double,rembal_max_l5:double,rembal_max_l6:double,rembal_avg_l1:double,rembal_avg_l2:double,rembal_avg_l3:double,rembal_avg_l4:double,rembal_avg_l5:double,rembal_avg_l6:double,reload_cumsum_l2:double,reload_cumsum_l3:double,reload_cumsum_l4:double,reload_cumsum_l5:double,reload_cumsum_l6:double,rembal_cumsum_l2:double,rembal_cumsum_l3:double,rembal_cumsum_l4:double,rembal_cumsum_l5:double,rembal_cumsum_l6:double,pct_rembal_to_reload_l1:double,pct_rembal_to_reload_l2:double,pct_rembal_to_reload_l3:double,pct_rembal_to_reload_l4:double,pct_rembal_to_reload_l5:double,pct_rembal_to_reload_l6:double,packet_sum_l1:double,packet_sum_l2:double,packet_sum_l3:double,packet_sum_l4:double,packet_sum_l5:double,packet_sum_l6:double,packet_min_l1:double,packet_min_l2:double,packet_min_l3:double,packet_min_l4:double,packet_min_l5:double,packet_min_l6:double,packet_max_l1:double,packet_max_l2:double,packet_max_l3:double,packet_max_l4:double,packet_max_l5:double,packet_max_l6:double,packet_avg_l1:double,packet_avg_l2:double,packet_avg_l3:double,packet_avg_l4:double,packet_avg_l5:double,packet_avg_l6:double,packet_cumsum_l2:double,packet_cumsum_l3:double,packet_cumsum_l4:double,packet_cumsum_l5:double,packet_cumsum_l6:double,province_OTHERS:double,province_DKI JAKARTA:double,province_BANTEN:double,province_JAWA TIMUR:double,province_JAWA BARAT:double,province_JAWA TENGAH:double,province_SUMATERA UTARA:double,province_UNDEFINED:double,handset_brand_OTHERS:double,handset_brand_VIVO:double,handset_brand_OPPO:double,handset_brand_REDMI:double,handset_brand_APPLE:double,handset_brand_REALME:double,handset_brand_XIAOMI:double,handset_brand_ASUS:double,handset_brand_SAMSUNG:double,handset_os_OTHERS:double,handset_os_APPLE IOS:double,handset_os_ANDROID:double,handset_os_OTHER:double,handset_type_OTHERS:double,handset_type_SMARTPHONE:double,handset_type_FEATUREPHONE:double,payment_category_POST:double,payment_category_PRE:double,hpos_to_ios_0:double,hpos_to_ios_1:double,hpos_to_ios_UNDEFINED:double,hpos_from_ios_0:double,hpos_from_ios_1:double,hpos_from_ios_UNDEFINED:double,hptype_to_smart_0:double,hptype_to_smart_1:double,hptype_to_smart_UNDEFINED:double,hptype_from_smart_0:double,hptype_from_smart_1:double,hptype_from_smart_UNDEFINED:double,hpbrand_change_0:double,hpbrand_change_1:double,hpbrand_change_UNDEFINED:double>) => struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.ScalaUDF_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at scala.collection.Iterator$$anon.next(Iterator.scala:410)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage464.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:645)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1289)
at org.apache.spark.executor.Executor$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)
Caused by: org.apache.spark.SparkException: Encountered null while assembling a row with handleInvalid = "keep". Consider
removing nulls from dataset or using handleInvalid = "keep" or "skip".
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:287)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:255)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at org.apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:255)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:144)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:143)
... 18 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1879)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1878)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:927)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050)
at org.apache.spark.util.EventLoop$$anon.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2088)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2107)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:375)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
at org.apache.spark.sql.Dataset$$anonfun$head.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$head.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun.apply(Dataset.scala:3370)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId.apply(SQLExecution.scala:80)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
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.SparkException: Failed to execute user defined function($anonfun: (struct<arpu_sum_l1:double,arpu_sum_l2:double,arpu_sum_l3:double,arpu_sum_l4:double,arpu_sum_l5:double,arpu_sum_l6:double,sms_sum_l1:double,sms_sum_l2:double,sms_sum_l3:double,sms_sum_l4:double,sms_sum_l5:double,sms_sum_l6:double,voice_sum_l1:double,voice_sum_l2:double,voice_sum_l3:double,voice_sum_l4:double,voice_sum_l5:double,voice_sum_l6:double,data_sum_l1:double,data_sum_l2:double,data_sum_l3:double,data_sum_l4:double,data_sum_l5:double,data_sum_l6:double,vocrev_sum_l1:double,vocrev_sum_l2:double,vocrev_sum_l3:double,vocrev_sum_l4:double,vocrev_sum_l5:double,vocrev_sum_l6:double,smsrev_sum_l1:double,smsrev_sum_l2:double,smsrev_sum_l3:double,smsrev_sum_l4:double,smsrev_sum_l5:double,smsrev_sum_l6:double,datarev_sum_l1:double,datarev_sum_l2:double,datarev_sum_l3:double,datarev_sum_l4:double,datarev_sum_l5:double,datarev_sum_l6:double,vasrev_sum_l1:double,vasrev_sum_l2:double,vasrev_sum_l3:double,vasrev_sum_l4:double,vasrev_sum_l5:double,vasrev_sum_l6:double,totrev_sum_l1:double,totrev_sum_l2:double,totrev_sum_l3:double,totrev_sum_l4:double,totrev_sum_l5:double,totrev_sum_l6:double,voc_yield_sum_l1:double,voc_yield_sum_l2:double,voc_yield_sum_l3:double,voc_yield_sum_l4:double,voc_yield_sum_l5:double,voc_yield_sum_l6:double,sms_yield_sum_l1:double,sms_yield_sum_l2:double,sms_yield_sum_l3:double,sms_yield_sum_l4:double,sms_yield_sum_l5:double,sms_yield_sum_l6:double,data_yield_sum_l1:double,data_yield_sum_l2:double,data_yield_sum_l3:double,data_yield_sum_l4:double,data_yield_sum_l5:double,data_yield_sum_l6:double,arpu_cumsum_l2:double,arpu_cumsum_l3:double,arpu_cumsum_l4:double,arpu_cumsum_l5:double,arpu_cumsum_l6:double,sms_cumsum_l2:double,sms_cumsum_l3:double,sms_cumsum_l4:double,sms_cumsum_l5:double,sms_cumsum_l6:double,voice_cumsum_l2:double,voice_cumsum_l3:double,voice_cumsum_l4:double,voice_cumsum_l5:double,voice_cumsum_l6:double,data_cumsum_l2:double,data_cumsum_l3:double,data_cumsum_l4:double,data_cumsum_l5:double,data_cumsum_l6:double,vocrev_cumsum_l2:double,vocrev_cumsum_l3:double,vocrev_cumsum_l4:double,vocrev_cumsum_l5:double,vocrev_cumsum_l6:double,smsrev_cumsum_l2:double,smsrev_cumsum_l3:double,smsrev_cumsum_l4:double,smsrev_cumsum_l5:double,smsrev_cumsum_l6:double,datarev_cumsum_l2:double,datarev_cumsum_l3:double,datarev_cumsum_l4:double,datarev_cumsum_l5:double,datarev_cumsum_l6:double,vasrev_cumsum_l2:double,vasrev_cumsum_l3:double,vasrev_cumsum_l4:double,vasrev_cumsum_l5:double,vasrev_cumsum_l6:double,totrev_cumsum_l2:double,totrev_cumsum_l3:double,totrev_cumsum_l4:double,totrev_cumsum_l5:double,totrev_cumsum_l6:double,voc_yield_cumsum_l2:double,voc_yield_cumsum_l3:double,voc_yield_cumsum_l4:double,voc_yield_cumsum_l5:double,voc_yield_cumsum_l6:double,sms_yield_cumsum_l2:double,sms_yield_cumsum_l3:double,sms_yield_cumsum_l4:double,sms_yield_cumsum_l5:double,sms_yield_cumsum_l6:double,data_yield_cumsum_l2:double,data_yield_cumsum_l3:double,data_yield_cumsum_l4:double,data_yield_cumsum_l5:double,data_yield_cumsum_l6:double,pct_vocrev_cumsum_l2:double,pct_vocrev_cumsum_l3:double,pct_vocrev_cumsum_l4:double,pct_vocrev_cumsum_l5:double,pct_vocrev_cumsum_l6:double,pct_smsrev_cumsum_l2:double,pct_smsrev_cumsum_l3:double,pct_smsrev_cumsum_l4:double,pct_smsrev_cumsum_l5:double,pct_smsrev_cumsum_l6:double,pct_datarev_cumsum_l2:double,pct_datarev_cumsum_l3:double,pct_datarev_cumsum_l4:double,pct_datarev_cumsum_l5:double,pct_datarev_cumsum_l6:double,pct_vocrev_sum_l1:double,pct_vocrev_sum_l2:double,pct_vocrev_sum_l3:double,pct_vocrev_sum_l4:double,pct_vocrev_sum_l5:double,pct_vocrev_sum_l6:double,pct_smsrev_sum_l1:double,pct_smsrev_sum_l2:double,pct_smsrev_sum_l3:double,pct_smsrev_sum_l4:double,pct_smsrev_sum_l5:double,pct_smsrev_sum_l6:double,pct_datarev_sum_l1:double,pct_datarev_sum_l2:double,pct_datarev_sum_l3:double,pct_datarev_sum_l4:double,pct_datarev_sum_l5:double,pct_datarev_sum_l6:double,arpu_sum_l14d:double,sms_sum_l14d:double,voice_sum_l14d:double,data_sum_l14d:double,vocrev_sum_l14d:double,smsrev_sum_l14d:double,datarev_sum_l14d:double,vasrev_sum_l14d:double,totrev_sum_l14d:double,arpu_sum_l7d:double,sms_sum_l7d:double,voice_sum_l7d:double,data_sum_l7d:double,vocrev_sum_l7d:double,smsrev_sum_l7d:double,datarev_sum_l7d:double,vasrev_sum_l7d:double,totrev_sum_l7d:double,pct_vocrev_sum_l7d:double,pct_smsrev_sum_l7d:double,pct_datarev_sum_l7d:double,pct_vocrev_sum_l14d:double,pct_smsrev_sum_l14d:double,pct_datarev_sum_l14d:double,reload_sum_l1:double,reload_sum_l2:double,reload_sum_l3:double,reload_sum_l4:double,reload_sum_l5:double,reload_sum_l6:double,reload_min_l1:double,reload_min_l2:double,reload_min_l3:double,reload_min_l4:double,reload_min_l5:double,reload_min_l6:double,reload_max_l1:double,reload_max_l2:double,reload_max_l3:double,reload_max_l4:double,reload_max_l5:double,reload_max_l6:double,reload_avg_l1:double,reload_avg_l2:double,reload_avg_l3:double,reload_avg_l4:double,reload_avg_l5:double,reload_avg_l6:double,rembal_sum_l1:double,rembal_sum_l2:double,rembal_sum_l3:double,rembal_sum_l4:double,rembal_sum_l5:double,rembal_sum_l6:double,rembal_min_l1:double,rembal_min_l2:double,rembal_min_l3:double,rembal_min_l4:double,rembal_min_l5:double,rembal_min_l6:double,rembal_max_l1:double,rembal_max_l2:double,rembal_max_l3:double,rembal_max_l4:double,rembal_max_l5:double,rembal_max_l6:double,rembal_avg_l1:double,rembal_avg_l2:double,rembal_avg_l3:double,rembal_avg_l4:double,rembal_avg_l5:double,rembal_avg_l6:double,reload_cumsum_l2:double,reload_cumsum_l3:double,reload_cumsum_l4:double,reload_cumsum_l5:double,reload_cumsum_l6:double,rembal_cumsum_l2:double,rembal_cumsum_l3:double,rembal_cumsum_l4:double,rembal_cumsum_l5:double,rembal_cumsum_l6:double,pct_rembal_to_reload_l1:double,pct_rembal_to_reload_l2:double,pct_rembal_to_reload_l3:double,pct_rembal_to_reload_l4:double,pct_rembal_to_reload_l5:double,pct_rembal_to_reload_l6:double,packet_sum_l1:double,packet_sum_l2:double,packet_sum_l3:double,packet_sum_l4:double,packet_sum_l5:double,packet_sum_l6:double,packet_min_l1:double,packet_min_l2:double,packet_min_l3:double,packet_min_l4:double,packet_min_l5:double,packet_min_l6:double,packet_max_l1:double,packet_max_l2:double,packet_max_l3:double,packet_max_l4:double,packet_max_l5:double,packet_max_l6:double,packet_avg_l1:double,packet_avg_l2:double,packet_avg_l3:double,packet_avg_l4:double,packet_avg_l5:double,packet_avg_l6:double,packet_cumsum_l2:double,packet_cumsum_l3:double,packet_cumsum_l4:double,packet_cumsum_l5:double,packet_cumsum_l6:double,province_OTHERS:double,province_DKI JAKARTA:double,province_BANTEN:double,province_JAWA TIMUR:double,province_JAWA BARAT:double,province_JAWA TENGAH:double,province_SUMATERA UTARA:double,province_UNDEFINED:double,handset_brand_OTHERS:double,handset_brand_VIVO:double,handset_brand_OPPO:double,handset_brand_REDMI:double,handset_brand_APPLE:double,handset_brand_REALME:double,handset_brand_XIAOMI:double,handset_brand_ASUS:double,handset_brand_SAMSUNG:double,handset_os_OTHERS:double,handset_os_APPLE IOS:double,handset_os_ANDROID:double,handset_os_OTHER:double,handset_type_OTHERS:double,handset_type_SMARTPHONE:double,handset_type_FEATUREPHONE:double,payment_category_POST:double,payment_category_PRE:double,hpos_to_ios_0:double,hpos_to_ios_1:double,hpos_to_ios_UNDEFINED:double,hpos_from_ios_0:double,hpos_from_ios_1:double,hpos_from_ios_UNDEFINED:double,hptype_to_smart_0:double,hptype_to_smart_1:double,hptype_to_smart_UNDEFINED:double,hptype_from_smart_0:double,hptype_from_smart_1:double,hptype_from_smart_UNDEFINED:double,hpbrand_change_0:double,hpbrand_change_1:double,hpbrand_change_UNDEFINED:double>) => struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.ScalaUDF_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at scala.collection.Iterator$$anon.next(Iterator.scala:410)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage464.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:645)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1289)
at org.apache.spark.executor.Executor$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
Caused by: org.apache.spark.SparkException: Encountered null while assembling a row with handleInvalid = "keep". Consider
removing nulls from dataset or using handleInvalid = "keep" or "skip".
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:287)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble.apply(VectorAssembler.scala:255)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at org.apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:255)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:144)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun.apply(VectorAssembler.scala:143)
... 18 more
这是 VectorAssembler 的文档。 https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html
您的堆栈跟踪提到，“在使用 handleInvalid =“keep”组装行时遇到空值。考虑 从数据集中删除空值或使用 handleInvalid = "keep" or "skip"."
因此请尝试使用 handleInvalid="keep" 或 handleInvalid="skip" 设置 VectorAssembler 构造函数
vector_assembler = VectorAssembler(inputCols = assemblerInput, outputCol = "vectorAssembler_features", handleInvalid="keep")
或
vector_assembler = VectorAssembler(inputCols = assemblerInput, outputCol = "vectorAssembler_features", handleInvalid="skip")