[SPARK-18412][SPARKR][ML] Fix exception for some SparkR ML algorithms training on libsvm data

## What changes were proposed in this pull request?
* Fix the following exceptions which throws when ```spark.randomForest```(classification), ```spark.gbt```(classification), ```spark.naiveBayes``` and ```spark.glm```(binomial family) were fitted on libsvm data.
```
java.lang.IllegalArgumentException: requirement failed: If label column already exists, forceIndexLabel can not be set with true.
```
See [SPARK-18412](https://issues.apache.org/jira/browse/SPARK-18412) for more detail about how to reproduce this bug.
* Refactor out ```getFeaturesAndLabels``` to RWrapperUtils, since lots of ML algorithm wrappers use this function.
* Drop some unwanted columns when making prediction.

## How was this patch tested?
Add unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15851 from yanboliang/spark-18412.

1 files changed, 15 insertions, 3 deletions

diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index b76f75dbdc..07df4b6d6f 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -881,7 +881,8 @@ test_that("spark.kstest", {
   expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
 })
 
-test_that("spark.randomForest Regression", {
+test_that("spark.randomForest", {
+  # regression
   data <- suppressWarnings(createDataFrame(longley))
   model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
                               numTrees = 1)
@@ -923,9 +924,8 @@ test_that("spark.randomForest Regression", {
   expect_equal(stats$treeWeights, stats2$treeWeights)
 
   unlink(modelPath)
-})
 
-test_that("spark.randomForest Classification", {
+  # classification
   data <- suppressWarnings(createDataFrame(iris))
   model <- spark.randomForest(data, Species ~ Petal_Length + Petal_Width, "classification",
                               maxDepth = 5, maxBins = 16)
@@ -971,6 +971,12 @@ test_that("spark.randomForest Classification", {
   predictions <- collect(predict(model, data))$prediction
   expect_equal(length(grep("1.0", predictions)), 50)
   expect_equal(length(grep("2.0", predictions)), 50)
+
+  # spark.randomForest classification can work on libsvm data
+  data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
+                source = "libsvm")
+  model <- spark.randomForest(data, label ~ features, "classification")
+  expect_equal(summary(model)$numFeatures, 4)
 })
 
 test_that("spark.gbt", {
@@ -1039,6 +1045,12 @@ test_that("spark.gbt", {
   expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction))
   expect_equal(s$numFeatures, 5)
   expect_equal(s$numTrees, 20)
+
+  # spark.gbt classification can work on libsvm data
+  data <- read.df(absoluteSparkPath("data/mllib/sample_binary_classification_data.txt"),
+                source = "libsvm")
+  model <- spark.gbt(data, label ~ features, "classification")
+  expect_equal(summary(model)$numFeatures, 692)
 })
 
 sparkR.session.stop()