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[SPARK-21306][ML] OneVsRest should support setWeightCol #18554

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facaiy wants to merge 16 commits into from
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[SPARK-21306][ML] OneVsRest should support setWeightCol #18554

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f178595
BUG: cache weightCol if necessary
facaiy Jul 6, 2017
a3e6e96
TST: add unit test
facaiy Jul 6, 2017
931d02d
TST: concise comment
facaiy Jul 6, 2017
25d681f
ENH: python, cache weightCol
facaiy Jul 7, 2017
c380ba7
BUG: check weightCol defined
facaiy Jul 7, 2017
e511b90
TST: model is not None
facaiy Jul 7, 2017
1c215f3
CLN: use if-indent style
facaiy Jul 7, 2017
2d3ce90
ENH: add weightCol
facaiy Jul 13, 2017
ea92228
TST: modify test case
facaiy Jul 13, 2017
57fc4b3
ENH: add weightCol for python part
facaiy Jul 13, 2017
00a7ed8
CLN: revise comment
facaiy Jul 13, 2017
a57f096
TST: python test for setWeightCol
facaiy Jul 13, 2017
54e0fca
DOC: add description
facaiy Jul 13, 2017
9ba0e2b
TST: classifier doesn't have weightCol
facaiy Jul 19, 2017
db303a0
CLN: mv weightCol to the end
facaiy Jul 26, 2017
8c0beba
TST: remove ovr2 nad ovr4
facaiy Jul 26, 2017
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CLN: revise comment
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@facaiy
facaiy committed Jul 13, 2017
commit 00a7ed82cee5a9f2eb5f819e1177f480d9cb8f58
1 change: 0 additions & 1 deletion mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
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Expand Up @@ -323,7 +323,6 @@ final class OneVsRest @Since("1.4.0") (
val numClasses = MetadataUtils.getNumClasses(labelSchema).fold(computeNumClasses())(identity)
instr.logNumClasses(numClasses)

// SPARK-21306: cache weightCol if necessary
val weightColIsUsed = isDefined(weightCol) && $(weightCol).nonEmpty && {
getClassifier match {
case _: HasWeightCol => true
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3 changes: 1 addition & 2 deletions mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
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Expand Up @@ -156,10 +156,9 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext with Defau
assert(output.schema.fieldNames.toSet === Set("label", "features", "prediction"))
}

test("SPARK-21306: OneVsRest should cache weightCol if necessary") {
test("SPARK-21306: OneVsRest should support setWeightCol") {
val dataset2 = dataset.withColumn("weight", lit(1))
val ova = new OneVsRest().setWeightCol("weight").setClassifier(new LogisticRegression())
// failed if weightCol is not cached.
val ovaModel = ova.fit(dataset2)
assert(ovaModel !== null)
}
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1 change: 0 additions & 1 deletion python/pyspark/ml/classification.py
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Expand Up @@ -1548,7 +1548,6 @@ def _fit(self, dataset):

numClasses = int(dataset.agg({labelCol: "max"}).head()["max("+labelCol+")"]) + 1

# SPARK - 21306: cache weightCol if necessary
weightCol = None
if (self.isDefined(self.weightCol) and self.getWeightCol()):
if isinstance(classifier, HasWeightCol):
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