[
https://issues.apache.org/jira/browse/SPARK-16851?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
zhengruifeng updated SPARK-16851:
---------------------------------
Description:
{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$.require(Predef.scala:224)
at
org.apache.spark.ml.classification.ProbabilisticClassificationModel.transform(ProbabilisticClassifier.scala:101)
... 58 elided
{code}
Although model set with wrong threshoulds will fail in prediction, it maybe
nice to evoke exception earlier in {{setThreshoulds}}
was:
{code}
val path =
"./spark-2.0.0-bin-hadoop2.7/data/mllib/sample_multiclass_classification_data.txt"
val data = spark.read.format("libsvm").load(path)
val rf = new RandomForestClassifier()
val model = rf.fit(data)
model.numClasses
res48: Int = 3
model.setThresholds(Array(0.5,0.1))
res49: org.apache.spark.ml.classification.RandomForestClassificationModel =
RandomForestClassificationModel (uid=rfc_b39da354ac8b) with 20 trees
model.transform(data)
java.lang.IllegalArgumentException: requirement failed:
RandomForestClassificationModel.transform() called with non-matching numClasses
and thresholds.length. numClasses=3, but thresholds has length 2
at scala.Predef$.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}
> 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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