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Posted to issues@spark.apache.org by "Yanbo Liang (JIRA)" <ji...@apache.org> on 2017/05/19 10:39:04 UTC
[jira] [Commented] (SPARK-20810) ML LinearSVC vs MLlib SVMWithSGD
output different solution
[ https://issues.apache.org/jira/browse/SPARK-20810?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16017217#comment-16017217 ]
Yanbo Liang commented on SPARK-20810:
-------------------------------------
cc [~josephkb] [~yuhaoyan]
> ML LinearSVC vs MLlib SVMWithSGD output different solution
> ----------------------------------------------------------
>
> Key: SPARK-20810
> URL: https://issues.apache.org/jira/browse/SPARK-20810
> Project: Spark
> Issue Type: Question
> Components: ML, MLlib
> Affects Versions: 2.2.0
> Reporter: Yanbo Liang
>
> Fitting with SVM classification model on the same dataset, ML {{LinearSVC}} produces different solution compared with MLlib {{SVMWithSGD}}. I understand they use different optimization solver (OWLQN vs SGD), does it make sense to converge to different solution?
> AFAIK, both of them use {{hinge loss}} which is convex but not differentiable function. Since the derivative of the hinge loss at certain place is non-deterministic, should we switch to use {{squared hinge loss}} which is the default loss function of {{sklearn.svm.LinearSVC}}?
> This issue is very easy to reproduce, you can paste the following code snippet to {{LinearSVCSuite}} and then click run in Intellij IDE.
> {code}
> test("LinearSVC vs SVMWithSGD") {
> import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
> import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
> val trainer1 = new LinearSVC()
> .setRegParam(0.00002)
> .setMaxIter(200)
> .setTol(1e-4)
> val model1 = trainer1.fit(binaryDataset)
> println(model1.coefficients)
> println(model1.intercept)
> val oldData = binaryDataset.rdd.map { case Row(label: Double, features: Vector) =>
> OldLabeledPoint(label, OldVectors.fromML(features))
> }
> val trainer2 = new SVMWithSGD().setIntercept(true)
> trainer2.optimizer.setRegParam(0.00002).setNumIterations(200).setConvergenceTol(1e-4)
> val model2 = trainer2.run(oldData)
> println(model2.weights)
> println(model2.intercept)
> }
> {code}
> The output is:
> {code}
> [7.24661385022775,14.774484832179743,22.00945617480461,29.558498069476084]
> 7.373454363024084
> [0.58166680313823,1.1938960150473041,1.7940106824589588,2.4884300611292165]
> 0.667790514894194
> {code}
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