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| author | Yanbo Liang <ybliang8@gmail.com> | 2016-02-22 23:37:09 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2016-02-22 23:37:09 -0800 |
| commit | 72427c3e115daf06f7ad8aa50115a8e0da2c6d62 (patch) | |
| tree | 4f193b6e3d4ffcd30b08149aa2faed5fe08bf1ac /mllib/src/test/scala/org/apache | |
| parent | 4fd1993692d45a0da0289b8c7669cc1dc3fe0f2b (diff) | |
| download | spark-72427c3e115daf06f7ad8aa50115a8e0da2c6d62.tar.gz spark-72427c3e115daf06f7ad8aa50115a8e0da2c6d62.tar.bz2 spark-72427c3e115daf06f7ad8aa50115a8e0da2c6d62.zip | |
[SPARK-13429][MLLIB] Unify Logistic Regression convergence tolerance of ML & MLlib
## What changes were proposed in this pull request?
In order to provide better and consistent result, let's change the default value of MLlib ```LogisticRegressionWithLBFGS convergenceTol``` from ```1E-4``` to ```1E-6``` which will be equal to ML ```LogisticRegression```.
cc dbtsai
## How was the this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #11299 from yanboliang/spark-13429.
Diffstat (limited to 'mllib/src/test/scala/org/apache')
| -rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala index d140545e37..cea0adc55c 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala @@ -667,9 +667,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w test("binary logistic regression with intercept with L1 regularization") { val trainer1 = new LogisticRegressionWithLBFGS().setIntercept(true).setFeatureScaling(true) - trainer1.optimizer.setUpdater(new L1Updater).setRegParam(0.12).setConvergenceTol(1E-6) + trainer1.optimizer.setUpdater(new L1Updater).setRegParam(0.12) val trainer2 = new LogisticRegressionWithLBFGS().setIntercept(true).setFeatureScaling(false) - trainer2.optimizer.setUpdater(new L1Updater).setRegParam(0.12).setConvergenceTol(1E-6) + trainer2.optimizer.setUpdater(new L1Updater).setRegParam(0.12) val model1 = trainer1.run(binaryDataset) val model2 = trainer2.run(binaryDataset) @@ -726,9 +726,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w test("binary logistic regression without intercept with L1 regularization") { val trainer1 = new LogisticRegressionWithLBFGS().setIntercept(false).setFeatureScaling(true) - trainer1.optimizer.setUpdater(new L1Updater).setRegParam(0.12).setConvergenceTol(1E-6) + trainer1.optimizer.setUpdater(new L1Updater).setRegParam(0.12) val trainer2 = new LogisticRegressionWithLBFGS().setIntercept(false).setFeatureScaling(false) - trainer2.optimizer.setUpdater(new L1Updater).setRegParam(0.12).setConvergenceTol(1E-6) + trainer2.optimizer.setUpdater(new L1Updater).setRegParam(0.12) val model1 = trainer1.run(binaryDataset) val model2 = trainer2.run(binaryDataset) @@ -786,9 +786,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w test("binary logistic regression with intercept with L2 regularization") { val trainer1 = new LogisticRegressionWithLBFGS().setIntercept(true).setFeatureScaling(true) - trainer1.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37).setConvergenceTol(1E-6) + trainer1.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37) val trainer2 = new LogisticRegressionWithLBFGS().setIntercept(true).setFeatureScaling(false) - trainer2.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37).setConvergenceTol(1E-6) + trainer2.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37) val model1 = trainer1.run(binaryDataset) val model2 = trainer2.run(binaryDataset) @@ -845,9 +845,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w test("binary logistic regression without intercept with L2 regularization") { val trainer1 = new LogisticRegressionWithLBFGS().setIntercept(false).setFeatureScaling(true) - trainer1.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37).setConvergenceTol(1E-6) + trainer1.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37) val trainer2 = new LogisticRegressionWithLBFGS().setIntercept(false).setFeatureScaling(false) - trainer2.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37).setConvergenceTol(1E-6) + trainer2.optimizer.setUpdater(new SquaredL2Updater).setRegParam(1.37) val model1 = trainer1.run(binaryDataset) val model2 = trainer2.run(binaryDataset) |