[SPARK-10194] [MLLIB] [PYSPARK] SGD algorithms need convergenceTol parameter in Python

[SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382) added a ```convergenceTol``` parameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8457 from yanboliang/spark-10194.

1 files changed, 15 insertions, 5 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index f585aacd45..69ce7f5070 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -132,7 +132,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       regParam: Double,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val lrAlg = new LinearRegressionWithSGD()
     lrAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -141,6 +142,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     lrAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       lrAlg,
@@ -159,7 +161,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       miniBatchFraction: Double,
       initialWeights: Vector,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val lassoAlg = new LassoWithSGD()
     lassoAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -168,6 +171,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     trainRegressionModel(
       lassoAlg,
       data,
@@ -185,7 +189,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       miniBatchFraction: Double,
       initialWeights: Vector,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val ridgeAlg = new RidgeRegressionWithSGD()
     ridgeAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -194,6 +199,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     trainRegressionModel(
       ridgeAlg,
       data,
@@ -212,7 +218,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       initialWeights: Vector,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val SVMAlg = new SVMWithSGD()
     SVMAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -221,6 +228,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     SVMAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       SVMAlg,
@@ -240,7 +248,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       regParam: Double,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val LogRegAlg = new LogisticRegressionWithSGD()
     LogRegAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -249,6 +258,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     LogRegAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       LogRegAlg,