[SPARK-18291][SPARKR][ML] Revert "[SPARK-18291][SPARKR][ML] SparkR glm predict should output original label when family = binomial."

## What changes were proposed in this pull request?
It's better we can fix this issue by providing an option ```type``` for users to change the ```predict``` output schema, then they could output probabilities, log-space predictions, or original labels. In order to not involve breaking API change for 2.1, so revert this change firstly and will add it back after [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) resolved.

## How was this patch tested?
Existing unit tests.

This reverts commit daa975f4bfa4f904697bf3365a4be9987032e490.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #16118 from yanboliang/spark-18291-revert.

2 files changed, 12 insertions, 86 deletions

diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index 0553e704bd..dcfeeb4cd2 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -64,16 +64,6 @@ test_that("spark.glm and predict", {
   rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
   expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
 
-  # binomial family
-  binomialTraining <- training[training$Species %in% c("versicolor", "virginica"), ]
-  model <- spark.glm(binomialTraining, Species ~ Sepal_Length + Sepal_Width,
-    family = binomial(link = "logit"))
-  prediction <- predict(model, binomialTraining)
-  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character")
-  expected <- c("virginica", "virginica", "virginica", "versicolor", "virginica",
-    "versicolor", "virginica", "versicolor", "virginica", "versicolor")
-  expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected)
-
   # poisson family
   model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species,
   family = poisson(link = identity))
@@ -138,10 +128,10 @@ test_that("spark.glm summary", {
   expect_equal(stats$aic, rStats$aic)
 
   # Test spark.glm works with weighted dataset
-  a1 <- c(0, 1, 2, 3, 4)
-  a2 <- c(5, 2, 1, 3, 2)
-  w <- c(1, 2, 3, 4, 5)
-  b <- c(1, 0, 1, 0, 0)
+  a1 <- c(0, 1, 2, 3)
+  a2 <- c(5, 2, 1, 3)
+  w <- c(1, 2, 3, 4)
+  b <- c(1, 0, 1, 0)
   data <- as.data.frame(cbind(a1, a2, w, b))
   df <- createDataFrame(data)
 
@@ -168,7 +158,7 @@ test_that("spark.glm summary", {
   data <- as.data.frame(cbind(a1, a2, b))
   df <- suppressWarnings(createDataFrame(data))
   regStats <- summary(spark.glm(df, b ~ a1 + a2, regParam = 1.0))
-  expect_equal(regStats$aic, 14.00976, tolerance = 1e-4) # 14.00976 is from summary() result
+  expect_equal(regStats$aic, 13.32836, tolerance = 1e-4) # 13.32836 is from summary() result
 
   # Test spark.glm works on collinear data
   A <- matrix(c(1, 2, 3, 4, 2, 4, 6, 8), 4, 2)
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 8bcc9fe5d1..78f401f29b 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
@@ -23,17 +23,12 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.ml.{Pipeline, PipelineModel}
-import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NominalAttribute}
-import org.apache.spark.ml.feature.{IndexToString, RFormula}
-import org.apache.spark.ml.regression._
-import org.apache.spark.ml.Transformer
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.feature.RFormula
 import org.apache.spark.ml.r.RWrapperUtils._
+import org.apache.spark.ml.regression._
 import org.apache.spark.ml.util._
 import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types._
 
 private[r] class GeneralizedLinearRegressionWrapper private (
     val pipeline: PipelineModel,
@@ -48,8 +43,6 @@ private[r] class GeneralizedLinearRegressionWrapper private (
     val rNumIterations: Int,
     val isLoaded: Boolean = false) extends MLWritable {
 
-  import GeneralizedLinearRegressionWrapper._
-
   private val glm: GeneralizedLinearRegressionModel =
     pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel]
 
@@ -60,16 +53,7 @@ private[r] class GeneralizedLinearRegressionWrapper private (
   def residuals(residualsType: String): DataFrame = glm.summary.residuals(residualsType)
 
   def transform(dataset: Dataset[_]): DataFrame = {
-    if (rFamily == "binomial") {
-      pipeline.transform(dataset)
-        .drop(PREDICTED_LABEL_PROB_COL)
-        .drop(PREDICTED_LABEL_INDEX_COL)
-        .drop(glm.getFeaturesCol)
-        .drop(glm.getLabelCol)
-    } else {
-      pipeline.transform(dataset)
-        .drop(glm.getFeaturesCol)
-    }
+    pipeline.transform(dataset).drop(glm.getFeaturesCol)
   }
 
   override def write: MLWriter =
@@ -79,10 +63,6 @@ private[r] class GeneralizedLinearRegressionWrapper private (
 private[r] object GeneralizedLinearRegressionWrapper
   extends MLReadable[GeneralizedLinearRegressionWrapper] {
 
-  val PREDICTED_LABEL_PROB_COL = "pred_label_prob"
-  val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
-  val PREDICTED_LABEL_COL = "prediction"
-
   def fit(
       formula: String,
       data: DataFrame,
@@ -93,7 +73,6 @@ private[r] object GeneralizedLinearRegressionWrapper
       weightCol: String,
       regParam: Double): GeneralizedLinearRegressionWrapper = {
     val rFormula = new RFormula().setFormula(formula)
-    if (family == "binomial") rFormula.setForceIndexLabel(true)
     checkDataColumns(rFormula, data)
     val rFormulaModel = rFormula.fit(data)
     // get labels and feature names from output schema
@@ -111,28 +90,9 @@ private[r] object GeneralizedLinearRegressionWrapper
       .setWeightCol(weightCol)
       .setRegParam(regParam)
       .setFeaturesCol(rFormula.getFeaturesCol)
-      .setLabelCol(rFormula.getLabelCol)
-    val pipeline = if (family == "binomial") {
-      // Convert prediction from probability to label index.
-      val probToPred = new ProbabilityToPrediction()
-        .setInputCol(PREDICTED_LABEL_PROB_COL)
-        .setOutputCol(PREDICTED_LABEL_INDEX_COL)
-      // Convert prediction from label index to original label.
-      val labelAttr = Attribute.fromStructField(schema(rFormulaModel.getLabelCol))
-        .asInstanceOf[NominalAttribute]
-      val labels = labelAttr.values.get
-      val idxToStr = new IndexToString()
-        .setInputCol(PREDICTED_LABEL_INDEX_COL)
-        .setOutputCol(PREDICTED_LABEL_COL)
-        .setLabels(labels)
-
-      new Pipeline()
-        .setStages(Array(rFormulaModel, glr.setPredictionCol(PREDICTED_LABEL_PROB_COL),
-          probToPred, idxToStr))
-        .fit(data)
-    } else {
-      new Pipeline().setStages(Array(rFormulaModel, glr)).fit(data)
-    }
+    val pipeline = new Pipeline()
+      .setStages(Array(rFormulaModel, glr))
+      .fit(data)
 
     val glm: GeneralizedLinearRegressionModel =
       pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel]
@@ -248,27 +208,3 @@ private[r] object GeneralizedLinearRegressionWrapper
     }
   }
 }
-
-/**
- * This utility transformer converts the predicted value of GeneralizedLinearRegressionModel
- * with "binomial" family from probability to prediction according to threshold 0.5.
- */
-private[r] class ProbabilityToPrediction private[r] (override val uid: String)
-  extends Transformer with HasInputCol with HasOutputCol with DefaultParamsWritable {
-
-  def this() = this(Identifiable.randomUID("probToPred"))
-
-  def setInputCol(value: String): this.type = set(inputCol, value)
-
-  def setOutputCol(value: String): this.type = set(outputCol, value)
-
-  override def transformSchema(schema: StructType): StructType = {
-    StructType(schema.fields :+ StructField($(outputCol), DoubleType))
-  }
-
-  override def transform(dataset: Dataset[_]): DataFrame = {
-    dataset.withColumn($(outputCol), round(col($(inputCol))))
-  }
-
-  override def copy(extra: ParamMap): ProbabilityToPrediction = defaultCopy(extra)
-}