[jira] [Assigned] (SPARK-16851) Incorrect threshould length in 'setThresholds()' evoke Exception

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-16851?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-16851:
------------------------------------

    Assignee:     (was: Apache Spark)

> Incorrect threshould length in 'setThresholds()' evoke Exception 
> -----------------------------------------------------------------
>
>                 Key: SPARK-16851
>                 URL: https://issues.apache.org/jira/browse/SPARK-16851
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: zhengruifeng
>            Priority: Trivial
>
> {code}
> val path = "./spark-2.0.0-bin-hadoop2.7/data/mllib/sample_multiclass_classification_data.txt"
> val data = spark.read.format("libsvm").load(path)
> val rf = new RandomForestClassifier()
> val model = rf.fit(data)
> model.numClasses
> res48: Int = 3
> model.setThresholds(Array(0.5,0.1))
> res49: org.apache.spark.ml.classification.RandomForestClassificationModel = RandomForestClassificationModel (uid=rfc_b39da354ac8b) with 20 trees
> model.transform(data)
> java.lang.IllegalArgumentException: requirement failed: RandomForestClassificationModel.transform() called with non-matching numClasses and thresholds.length. numClasses=3, but thresholds has length 2
>   at scala.Predef$.require(Predef.scala:224)
>   at org.apache.spark.ml.classification.ProbabilisticClassificationModel.transform(ProbabilisticClassifier.scala:101)
>   ... 58 elided
> {code}
> Although model set with wrong threshoulds will fail in prediction, it maybe nice to evoke exception earlier in {{setThreshoulds}}



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