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https://issues.apache.org/jira/browse/SPARK-30210?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Paul Anzel updated SPARK-30210:
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Description:
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?
was:
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?
> 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
> 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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