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author | Xiangrui Meng <meng@databricks.com> | 2014-08-15 21:04:29 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-08-15 21:04:29 -0700 |
commit | 5d25c0b74f6397d78164b96afb8b8cbb1b15cfbd (patch) | |
tree | d2fb3a33cee986c5d7d7c54c88fdbe4d53d98c92 /examples | |
parent | cc3648774e9a744850107bb187f2828d447e0a48 (diff) | |
download | spark-5d25c0b74f6397d78164b96afb8b8cbb1b15cfbd.tar.gz spark-5d25c0b74f6397d78164b96afb8b8cbb1b15cfbd.tar.bz2 spark-5d25c0b74f6397d78164b96afb8b8cbb1b15cfbd.zip |
[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
Diffstat (limited to 'examples')
-rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala | 8 |
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() |