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Posted to issues@spark.apache.org by "Nicholas Brett Marcott (Jira)" <ji...@apache.org> on 2020/12/26 08:59:00 UTC
[jira] [Comment Edited] (SPARK-30210) Give more informative error
for BinaryClassificationEvaluator when data with only one label is provided
[ https://issues.apache.org/jira/browse/SPARK-30210?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17252664#comment-17252664 ]
Nicholas Brett Marcott edited comment on SPARK-30210 at 12/26/20, 8:58 AM:
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[~anzelpwj] could you provide a simple code example to reproduce the error?
My simple case (a few others attempted as well) which only reference one label doesn't throw errors in v2.4.7 nor v3.0.1:
{code:java}
>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
>>> evaluator = BinaryClassificationEvaluator(labelCol='label', metricName='areaUnderROC')
>>> df = spark.createDataFrame([(1.0,1.0)],['label','rawPrediction'])
>>> evaluator.evaluate(df)
1.0
{code}
was (Author: nmarcott):
[~anzelpwj] could you provide a simple code example to reproduce the error?
My simple case (a few others attempted as well) which only reference one label doesn't throw errors:
{code:java}
>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
>>> evaluator = BinaryClassificationEvaluator(labelCol='label', metricName='areaUnderROC')
>>> df = spark.createDataFrame([(1.0,1.0)],['label','rawPrediction'])
>>> evaluator.evaluate(df)
1.0
{code}
> Give more informative error for BinaryClassificationEvaluator when data with only one label is provided
> -------------------------------------------------------------------------------------------------------
>
> Key: SPARK-30210
> URL: https://issues.apache.org/jira/browse/SPARK-30210
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 3.1.0
> Environment: Pyspark on Databricks
> Reporter: Paul Anzel
> Priority: Minor
>
> Hi all,
> When I was trying to do some machine learning work with pyspark I ran into a confusing error message:
> {{# Model and train/test set generated...}}
> {{ evaluator = BinaryClassificationEvaluator(labelCol=label, metricName='areaUnderROC')}}
> {{ prediction = model.transform(test_data)}}
> {{ auc = evaluator.evaluate(prediction)}}
> {{org.apache.spark.SparkException: Job aborted due to stage failure: Task 37 in stage 21.0 failed 4 times, most recent failure: Lost task 37.3 in stage 21.0 (TID 2811, 10.139.65.48, executor 16): java.lang.ArrayIndexOutOfBoundsException}}
> After some investigation, I found that the issue was that the data I was trying to predict on only had one label represented, rather than both positive and negative labels. Easy enough to fix, but I would like to ask if we could replace this error with one that explicitly points out the issue. Would it be acceptable to have a check ahead of time on labels that ensures all labels are represented? Alternately, can we change the docs for BinaryClassificationEvaluator to explain what this error means?
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