[SPARK-13449] Naive Bayes wrapper in SparkR

## What changes were proposed in this pull request?

This PR continues the work in #11486 from yinxusen with some code refactoring. In R package e1071, `naiveBayes` supports both categorical (Bernoulli) and continuous features (Gaussian), while in MLlib we support Bernoulli and multinomial. This PR implements the common subset: Bernoulli.

I moved the implementation out from SparkRWrappers to NaiveBayesWrapper to make it easier to read. Argument names, default values, and summary now match e1071's naiveBayes.

I removed the preprocess part that omit NA values because we don't know which columns to process.

## How was this patch tested?

Test against output from R package e1071's naiveBayes.

cc: yanboliang yinxusen

Closes #11486

Author: Xusen Yin <yinxusen@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #11890 from mengxr/SPARK-13449.

1 files changed, 59 insertions, 0 deletions

diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index e120462964..44b48369ef 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -141,3 +141,62 @@ test_that("kmeans", {
   cluster <- summary.model$cluster
   expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
 })
+
+test_that("naiveBayes", {
+  # R code to reproduce the result.
+  # We do not support instance weights yet. So we ignore the frequencies.
+  #
+  #' library(e1071)
+  #' t <- as.data.frame(Titanic)
+  #' t1 <- t[t$Freq > 0, -5]
+  #' m <- naiveBayes(Survived ~ ., data = t1)
+  #' m
+  #' predict(m, t1)
+  #
+  # -- output of 'm'
+  #
+  # A-priori probabilities:
+  # Y
+  #        No       Yes
+  # 0.4166667 0.5833333
+  #
+  # Conditional probabilities:
+  #      Class
+  # Y           1st       2nd       3rd      Crew
+  #   No  0.2000000 0.2000000 0.4000000 0.2000000
+  #   Yes 0.2857143 0.2857143 0.2857143 0.1428571
+  #
+  #      Sex
+  # Y     Male Female
+  #   No   0.5    0.5
+  #   Yes  0.5    0.5
+  #
+  #      Age
+  # Y         Child     Adult
+  #   No  0.2000000 0.8000000
+  #   Yes 0.4285714 0.5714286
+  #
+  # -- output of 'predict(m, t1)'
+  #
+  # Yes Yes Yes Yes No  No  Yes Yes No  No  Yes Yes Yes Yes Yes Yes Yes Yes No  No  Yes Yes No  No
+  #
+
+  t <- as.data.frame(Titanic)
+  t1 <- t[t$Freq > 0, -5]
+  df <- suppressWarnings(createDataFrame(sqlContext, t1))
+  m <- naiveBayes(Survived ~ ., data = df)
+  s <- summary(m)
+  expect_equal(as.double(s$apriori[1, "Yes"]), 0.5833333, tolerance = 1e-6)
+  expect_equal(sum(s$apriori), 1)
+  expect_equal(as.double(s$tables["Yes", "Age_Adult"]), 0.5714286, tolerance = 1e-6)
+  p <- collect(select(predict(m, df), "prediction"))
+  expect_equal(p$prediction, c("Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No",
+                               "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No",
+                               "Yes", "Yes", "No", "No"))
+
+  # Test e1071::naiveBayes
+  if (requireNamespace("e1071", quietly = TRUE)) {
+    expect_that(m <- e1071::naiveBayes(Survived ~ ., data = t1), not(throws_error()))
+    expect_equal(as.character(predict(m, t1[1, ])), "Yes")
+  }
+})