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Posted to issues@spark.apache.org by "L. C. Hsieh (Jira)" <ji...@apache.org> on 2020/09/05 17:07:01 UTC
[jira] [Commented] (SPARK-32787) requirement failed: The columns of
A don't match the number of elements of x. A: 14, x: 10
[ https://issues.apache.org/jira/browse/SPARK-32787?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17191100#comment-17191100 ]
L. C. Hsieh commented on SPARK-32787:
-------------------------------------
Do you check the feature length of {{OneHotEncoderEstimator}} and {{FeatureHasher}} is the same?
> requirement failed: The columns of A don't match the number of elements of x. A: 14, x: 10
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-32787
> URL: https://issues.apache.org/jira/browse/SPARK-32787
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.4.0
> Environment: Mac OS
> spark2.4.0
> Reporter: mzz
> Priority: Major
>
> i use sparkML to train a gender model, but always report the following error when verifying the test data.When i use *FeatureHasher* ,the procedure is normal,but when i use one-hot,it
> will report a error:"requirement failed: The columns of A don't match the number of elements of x. A: 14, x: 10"
> my key code:
> {code:java}
> // hash feature
> // val hasher = new FeatureHasher()
> // .setInputCols("nickname_index", "title_index")
> // .setOutputCol("feature")
> // println("特征Hasher编码：")
> // val tranning = hasher.transform(train_index)
> // tranning.show(10)
> // val testing = hasher.transform(test_index)
> // one-hot
> val encoder = new OneHotEncoderEstimator()
> .setInputCols(Array("nickname_index", "title_index"))
> .setOutputCols(Array("nickname_indexs", "title_indexs"))
> val tranning = encoder.fit(train_index).transform(train_index)
> val testing = encoder.fit(test_index).transform(test_index)
> // my LR model
> val lr = new LogisticRegression()
> .setMaxIter(10000)
> .setRegParam(0.01)
> .setElasticNetParam(0) // 0:L2, 1:L1
> .setFeaturesCol("nickname_indexs")
> .setFeaturesCol("title_indexs")
> .setLabelCol("label")
> .setPredictionCol("sex_predict") //预测列
> val model_lr = lr.fit(tranning)
> // println(s"每个特征对应系数: ${model_lr.coefficients} 截距: ${model_lr.intercept}")
> //TODO.. 测试数据验证
> val predictions = model_lr.transform(testing)
> predictions.select("label", "sex_predict", "probability").show(100, false)
> {code}
> error:
> {code:java}
> 18:04:16,763 ERROR org.apache.spark.scheduler.TaskSetManager - Task 0 in stage 35.0 failed 1 times; aborting job
> Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 35.0 failed 1 times, most recent failure: Lost task 0.0 in stage 35.0 (TID 35, localhost, executor driver): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (struct<type:tinyint,size:int,indices:array<int>,values:array<double>>) => struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: java.lang.IllegalArgumentException: requirement failed: The columns of A don't match the number of elements of x. A: 14, x: 10
> at scala.Predef$.require(Predef.scala:224)
> at org.apache.spark.ml.linalg.BLAS$.gemv(BLAS.scala:539)
> at org.apache.spark.ml.classification.LogisticRegressionModel$$anonfun$34.apply(LogisticRegression.scala:1007)
> at org.apache.spark.ml.classification.LogisticRegressionModel$$anonfun$34.apply(LogisticRegression.scala:1005)
> at org.apache.spark.ml.classification.LogisticRegressionModel.predictRaw(LogisticRegression.scala:1155)
> at org.apache.spark.ml.classification.LogisticRegressionModel.predictRaw(LogisticRegression.scala:930)
> at org.apache.spark.ml.classification.ProbabilisticClassificationModel$$anonfun$1.apply(ProbabilisticClassifier.scala:117)
> at org.apache.spark.ml.classification.ProbabilisticClassificationModel$$anonfun$1.apply(ProbabilisticClassifier.scala:116)
> ... 21 more
> Driver stacktrace:
> at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1887)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1875)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1874)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1874)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
> at scala.Option.foreach(Option.scala:257)
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2108)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2057)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2046)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
> at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
> at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3384)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
> at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3365)
> at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
> at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3364)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2545)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2759)
> at org.apache.spark.sql.Dataset.getRows(Dataset.scala:255)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:292)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:748)
> at com.qm.sparkapp.lr.LRUserBookOneHotSexModel$.main(LRUserBookOneHotSexModel.scala:185)
> at com.qm.sparkapp.lr.LRUserBookOneHotSexModel.main(LRUserBookOneHotSexModel.scala)
> Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (struct<type:tinyint,size:int,indices:array<int>,values:array<double>>) => struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: java.lang.IllegalArgumentException: requirement failed: The columns of A don't match the number of elements of x. A: 14, x: 10
> at scala.Predef$.require(Predef.scala:224)
> at org.apache.spark.ml.linalg.BLAS$.gemv(BLAS.scala:539)
> at org.apache.spark.ml.classification.LogisticRegressionModel$$anonfun$34.apply(LogisticRegression.scala:1007)
> at org.apache.spark.ml.classification.LogisticRegressionModel$$anonfun$34.apply(LogisticRegression.scala:1005)
> at org.apache.spark.ml.classification.LogisticRegressionModel.predictRaw(LogisticRegression.scala:1155)
> at org.apache.spark.ml.classification.LogisticRegressionModel.predictRaw(LogisticRegression.scala:930)
> at org.apache.spark.ml.classification.ProbabilisticClassificationModel$$anonfun$1.apply(ProbabilisticClassifier.scala:117)
> at org.apache.spark.ml.classification.ProbabilisticClassificationModel$$anonfun$1.apply(ProbabilisticClassifier.scala:116)
> ... 21 more
> {code}
> help me ,thx.
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