[SPARK-19616][SPARKR] weightCol and aggregationDepth should be improved for some SparkR APIs

## What changes were proposed in this pull request?

This is a follow-up PR of #16800

When doing SPARK-19456, we found that "" should be consider a NULL column name and should not be set. aggregationDepth should be exposed as an expert parameter.

## How was this patch tested?
Existing tests.

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #16945 from wangmiao1981/svc.

4 files changed, 15 insertions, 5 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
index bd965acf56..0bf543d888 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
@@ -82,7 +82,10 @@ private[r] object AFTSurvivalRegressionWrapper extends MLReadable[AFTSurvivalReg
   }
 
 
-  def fit(formula: String, data: DataFrame): AFTSurvivalRegressionWrapper = {
+  def fit(
+      formula: String,
+      data: DataFrame,
+      aggregationDepth: Int): AFTSurvivalRegressionWrapper = {
 
     val (rewritedFormula, censorCol) = formulaRewrite(formula)
 
@@ -100,6 +103,7 @@ private[r] object AFTSurvivalRegressionWrapper extends MLReadable[AFTSurvivalReg
       .setCensorCol(censorCol)
       .setFitIntercept(rFormula.hasIntercept)
       .setFeaturesCol(rFormula.getFeaturesCol)
+      .setAggregationDepth(aggregationDepth)
 
     val pipeline = new Pipeline()
       .setStages(Array(rFormulaModel, aft))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
index 78f401f29b..cbd6cd1c79 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
@@ -87,9 +87,11 @@ private[r] object GeneralizedLinearRegressionWrapper
       .setFitIntercept(rFormula.hasIntercept)
       .setTol(tol)
       .setMaxIter(maxIter)
-      .setWeightCol(weightCol)
       .setRegParam(regParam)
       .setFeaturesCol(rFormula.getFeaturesCol)
+
+    if (weightCol != null) glr.setWeightCol(weightCol)
+
     val pipeline = new Pipeline()
       .setStages(Array(rFormulaModel, glr))
       .fit(data)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala
index 48632316f3..d31ebb46af 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala
@@ -74,9 +74,10 @@ private[r] object IsotonicRegressionWrapper
     val isotonicRegression = new IsotonicRegression()
       .setIsotonic(isotonic)
       .setFeatureIndex(featureIndex)
-      .setWeightCol(weightCol)
       .setFeaturesCol(rFormula.getFeaturesCol)
 
+    if (weightCol != null) isotonicRegression.setWeightCol(weightCol)
+
     val pipeline = new Pipeline()
       .setStages(Array(rFormulaModel, isotonicRegression))
       .fit(data)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala
index 645bc7247f..c96f99cb83 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala
@@ -96,7 +96,8 @@ private[r] object LogisticRegressionWrapper
       family: String,
       standardization: Boolean,
       thresholds: Array[Double],
-      weightCol: String
+      weightCol: String,
+      aggregationDepth: Int
       ): LogisticRegressionWrapper = {
 
     val rFormula = new RFormula()
@@ -119,10 +120,10 @@ private[r] object LogisticRegressionWrapper
       .setFitIntercept(fitIntercept)
       .setFamily(family)
       .setStandardization(standardization)
-      .setWeightCol(weightCol)
       .setFeaturesCol(rFormula.getFeaturesCol)
       .setLabelCol(rFormula.getLabelCol)
       .setPredictionCol(PREDICTED_LABEL_INDEX_COL)
+      .setAggregationDepth(aggregationDepth)
 
     if (thresholds.length > 1) {
       lr.setThresholds(thresholds)
@@ -130,6 +131,8 @@ private[r] object LogisticRegressionWrapper
       lr.setThreshold(thresholds(0))
     }
 
+    if (weightCol != null) lr.setWeightCol(weightCol)
+
     val idxToStr = new IndexToString()
       .setInputCol(PREDICTED_LABEL_INDEX_COL)
       .setOutputCol(PREDICTED_LABEL_COL)