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https://issues.apache.org/jira/browse/SPARK-30210?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17252664#comment-17252664
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Nicholas Brett Marcott commented on SPARK-30210:
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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:
{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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