[jira] [Created] (SPARK-30210) Give more informative error for BinaryClassificationEvaluator when data with only one label is provided

["Paul Anzel (Jira)" <ji...@apache.org>]

Paul Anzel created SPARK-30210:
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             Summary: 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: 2.4.5
         Environment: Pyspark on Databricks
            Reporter: Paul Anzel


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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