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

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

zhengruifeng created SPARK-16851:
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             Summary: 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
{{code}}

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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