[SPARK-3078][MLLIB] Make LRWithLBFGS API consistent with others

Should ask users to set parameters through the optimizer. dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #1973 from mengxr/lr-lbfgs and squashes the following commits:

e3efbb1 [Xiangrui Meng] fix tests
21b3579 [Xiangrui Meng] fix method name
641eea4 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into lr-lbfgs
456ab7c [Xiangrui Meng] update LRWithLBFGS

1 files changed, 4 insertions, 4 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
index 56b02b65d8..a6f78d2441 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
@@ -21,7 +21,7 @@ import org.apache.log4j.{Level, Logger}
 import scopt.OptionParser
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.mllib.classification.{LogisticRegressionWithSGD, SVMWithSGD}
+import org.apache.spark.mllib.classification.{LogisticRegressionWithLBFGS, SVMWithSGD}
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}
@@ -66,7 +66,8 @@ object BinaryClassification {
         .text("number of iterations")
         .action((x, c) => c.copy(numIterations = x))
       opt[Double]("stepSize")
-        .text(s"initial step size, default: ${defaultParams.stepSize}")
+        .text("initial step size (ignored by logistic regression), " +
+          s"default: ${defaultParams.stepSize}")
         .action((x, c) => c.copy(stepSize = x))
       opt[String]("algorithm")
         .text(s"algorithm (${Algorithm.values.mkString(",")}), " +
@@ -125,10 +126,9 @@ object BinaryClassification {
 
     val model = params.algorithm match {
       case LR =>
-        val algorithm = new LogisticRegressionWithSGD()
+        val algorithm = new LogisticRegressionWithLBFGS()
         algorithm.optimizer
           .setNumIterations(params.numIterations)
-          .setStepSize(params.stepSize)
           .setUpdater(updater)
           .setRegParam(params.regParam)
         algorithm.run(training).clearThreshold()