{"push_id":1323094432,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"a19a1bb59411177caaf99581e89098826b7d0c7b","before":"37eb9184f1e9f1c07142c66936671f4711ef407d","commits":[{"sha":"a19a1bb59411177caaf99581e89098826b7d0c7b","author":{"name":"Yanbo Liang","email":"b6929798b4e4fba75014af8265904a7818902ec7@gmail.com"},"message":"[SPARK-16356][FOLLOW-UP][ML] Enforce ML test of exception for local/distributed Dataset.\n\n## What changes were proposed in this pull request?\n#14035 added ```testImplicits``` to ML unit tests and promoted ```toDF()```, but left one minor issue at ```VectorIndexerSuite```. If we create the DataFrame by ```Seq(...).toDF()```, it will throw different error/exception compared with ```sc.parallelize(Seq(...)).toDF()``` for one of the test cases.\nAfter in-depth study, I found it was caused by different behavior of local and distributed Dataset if the UDF failed at ```assert```. If the data is local Dataset, it throws ```AssertionError``` directly; If the data is distributed Dataset, it throws ```SparkException``` which is the wrapper of ```AssertionError```. I think we should enforce this test to cover both case.\n\n## How was this patch tested?\nUnit test.\n\nAuthor: Yanbo Liang <ybliang8@gmail.com>\n\nCloses #15261 from yanboliang/spark-16356.","distinct":true,"url":"https://api.github.com/repos/dongjoon-hyun/spark/commits/a19a1bb59411177caaf99581e89098826b7d0c7b"}]}
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