[jira] [Assigned] (SPARK-14489) RegressionEvaluator returns NaN for ALS in Spark ml

["Nick Pentreath (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-14489?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Nick Pentreath reassigned SPARK-14489:
--------------------------------------

    Assignee: Nick Pentreath

> RegressionEvaluator returns NaN for ALS in Spark ml
> ---------------------------------------------------
>
>                 Key: SPARK-14489
>                 URL: https://issues.apache.org/jira/browse/SPARK-14489
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 1.6.0
>         Environment: AWS EMR
>            Reporter: Boris Clémençon 
>            Assignee: Nick Pentreath
>              Labels: patch
>   Original Estimate: 4h
>  Remaining Estimate: 4h
>
> When building a Spark ML pipeline containing an ALS estimator, the metrics "rmse", "mse", "r2" and "mae" all return NaN. 
> The reason is in CrossValidator.scala line 109. The K-folds are randomly generated. For large and sparse datasets, there is a significant probability that at least one user of the validation set is missing in the training set, hence generating a few NaN estimation with transform method and NaN RegressionEvaluator's metrics too. 
> Suggestion to fix the bug: remove the NaN values while computing the rmse or other metrics (ie, removing users or items in validation test that is missing in the learning set). Send logs when this happen.
> Issue SPARK-14153 seems to be the same pbm
> {code:title=Bar.scala|borderStyle=solid}
>     val splits = MLUtils.kFold(dataset.rdd, $(numFolds), 0)
>     splits.zipWithIndex.foreach { case ((training, validation), splitIndex) =>
>       val trainingDataset = sqlCtx.createDataFrame(training, schema).cache()
>       val validationDataset = sqlCtx.createDataFrame(validation, schema).cache()
>       // multi-model training
>       logDebug(s"Train split $splitIndex with multiple sets of parameters.")
>       val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
>       trainingDataset.unpersist()
>       var i = 0
>       while (i < numModels) {
>         // TODO: duplicate evaluator to take extra params from input
>         val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
>         logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
>         metrics(i) += metric
>         i += 1
>       }
>       validationDataset.unpersist()
>     }
> {code}



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