apache-spark - spark.ml StringIndexer 在 fit() 上抛出“Unseen label”

Question

我正在准备一个玩具spark.ml示例。Spark version 1.6.0，运行在Oracle JDK version 1.8.0_65pyspark、ipython notebook 之上。

首先，它与 Spark、ML、StringIndexer 几乎没有任何关系：处理看不见的标签。在将管道拟合到数据集时引发异常，而不是对其进行转换。并且在这里抑制异常可能不是解决方案，因为我担心在这种情况下数据集会变得非常糟糕。

我的数据集大约有 800Mb 未压缩，因此可能很难重现（较小的子集似乎可以避开这个问题）。

数据集如下所示：

+--------------------+-----------+-----+-------+-----+--------------------+
|                 url|         ip|   rs|   lang|label|                 txt|
+--------------------+-----------+-----+-------+-----+--------------------+
|http://3d-detmold...|217.160.215|378.0|     de|  0.0|homwillkommskip c...|
|   http://3davto.ru/| 188.225.16|891.0|     id|  1.0|оформить заказ пе...|
| http://404.szm.com/|  85.248.42| 58.0|     cs|  0.0|kliknite tu alebo...|
|  http://404.xls.hu/| 212.52.166|168.0|     hu|  0.0|honlapkészítés404...|
|http://a--m--a--t...|    66.6.43|462.0|     en|  0.0|back top archiv r...|
|http://a-wrf.ru/c...|  78.108.80|126.0|unknown|  1.0|                    |
|http://a-wrf.ru/s...|  78.108.80|214.0|     ru|  1.0|установк фаркопна...|
+--------------------+-----------+-----+-------+-----+--------------------+

被预测的值为label。整个管道适用于它：

from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler, StringIndexer, OneHotEncoder, Tokenizer, HashingTF
from pyspark.ml.classification import LogisticRegression

train, test = munge(src_dataframe).randomSplit([70., 30.], seed=12345)

pipe_stages = [
    StringIndexer(inputCol='lang', outputCol='lang_idx'),
    OneHotEncoder(inputCol='lang_idx', outputCol='lang_onehot'),
    Tokenizer(inputCol='ip', outputCol='ip_tokens'),
    HashingTF(numFeatures=2**10, inputCol='ip_tokens', outputCol='ip_vector'),
    Tokenizer(inputCol='txt', outputCol='txt_tokens'),
    HashingTF(numFeatures=2**18, inputCol='txt_tokens', outputCol='txt_vector'),
    VectorAssembler(inputCols=['lang_onehot', 'ip_vector', 'txt_vector'], outputCol='features'),
    LogisticRegression(labelCol='label', featuresCol='features')
]

pipe = Pipeline(stages=pipe_stages)
pipemodel = pipe.fit(train)

这是堆栈跟踪：

Py4JJavaError: An error occurred while calling o10793.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 18 in stage 627.0 failed 1 times, most recent failure: Lost task 18.0 in stage 627.0 (TID 23259, localhost): org.apache.spark.SparkException: Unseen label: pl-PL.
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:157)
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:153)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.evalExpr2$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:51)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:49)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:282)
    at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
    at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    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)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952)
    at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1025)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007)
    at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1136)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1113)
    at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:271)
    at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:159)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:71)
    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:497)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Unseen label: pl-PL.
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:157)
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:153)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.evalExpr2$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:51)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:49)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:282)
    at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
    at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more

最有趣的一行是：

org.apache.spark.SparkException: Unseen label: pl-PL.

~~不知道，pl-PL哪个是列中的值lang可能会在label列中混淆，这是一个float，未string~~编辑：一些仓促的合并，感谢@zero323

我进一步研究并发现，这pl-PL是来自数据集测试部分的值，而不是训练。所以现在我什至不知道在哪里寻找罪魁祸首：它可能很容易是randomSplit代码，而不是StringIndexer，谁知道还有什么。

我该如何调查？

score 8 · Accepted Answer

Unseen label 是不对应于特定列的通用消息。最可能的问题在于以下阶段：

StringIndexer(inputCol='lang', outputCol='lang_idx')

存在pl-PL于train("lang")和不存在于test("lang")。

您可以使用setHandleInvalidwith更正它skip：

from pyspark.ml.feature import StringIndexer

train = sc.parallelize([(1, "foo"), (2, "bar")]).toDF(["k", "v"])
test = sc.parallelize([(3, "foo"), (4, "foobar")]).toDF(["k", "v"])

indexer = StringIndexer(inputCol="v", outputCol="vi")
indexer.fit(train).transform(test).show()

## Py4JJavaError: An error occurred while calling o112.showString.
## : org.apache.spark.SparkException: Job aborted due to stage failure: 
##   ...
##   org.apache.spark.SparkException: Unseen label: foobar.

indexer.setHandleInvalid("skip").fit(train).transform(test).show()

## +---+---+---+
## |  k|  v| vi|
## +---+---+---+
## |  3|foo|1.0|
## +---+---+---+

或者，在最新版本中，keep：

indexer.setHandleInvalid("keep").fit(train).transform(test).show()

## +---+------+---+
## |  k|     v| vi|
## +---+------+---+
## |  3|   foo|0.0|
## |  4|foobar|2.0|
## +---+------+---+

score 5 · Accepted Answer

好吧，我想我明白了。至少我得到了这个工作。

缓存数据帧（包括训练/测试部分）解决了这个问题。这就是我在这个 JIRA 问题中发现的：https ://issues.apache.org/jira/browse/SPARK-12590 。

所以这不是一个错误，只是randomSample可能在相同但不同分区的数据集上产生不同的结果。显然，我的一些 munging 函数（或Pipeline）涉及重新分区，因此，训练集重新计算的结果与其定义可能不同。

我仍然感兴趣 - 它是可重复性：它总是“pl-PL”行混入数据集的错误部分，即它不是随机重新分区。这是确定性的，只是不一致。我想知道它到底是怎么发生的。

apache-spark - spark.ml StringIndexer 在 fit() 上抛出“Unseen label”

2 回答 2

Related

Reference