[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.

1 files changed, 5 insertions, 15 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)