[SPARK-18239][SPARKR] Gradient Boosted Tree for R

## What changes were proposed in this pull request?

Gradient Boosted Tree in R.
With a few minor improvements to RandomForest in R.

Since this is relatively isolated I'd like to target this for branch-2.1

## How was this patch tested?

manual tests, unit tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #15746 from felixcheung/rgbt.

1 files changed, 68 insertions, 0 deletions

diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index 5f742d9045..33e9d0d267 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -949,4 +949,72 @@ test_that("spark.randomForest Classification", {
   unlink(modelPath)
 })
 
+test_that("spark.gbt", {
+  # regression
+  data <- suppressWarnings(createDataFrame(longley))
+  model <- spark.gbt(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, seed = 123)
+  predictions <- collect(predict(model, data))
+  expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187,
+                                         63.221, 63.639, 64.989, 63.761,
+                                         66.019, 67.857, 68.169, 66.513,
+                                         68.655, 69.564, 69.331, 70.551),
+               tolerance = 1e-4)
+  stats <- summary(model)
+  expect_equal(stats$numTrees, 20)
+  expect_equal(stats$formula, "Employed ~ .")
+  expect_equal(stats$numFeatures, 6)
+  expect_equal(length(stats$treeWeights), 20)
+
+  modelPath <- tempfile(pattern = "spark-gbtRegression", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  stats2 <- summary(model2)
+  expect_equal(stats$formula, stats2$formula)
+  expect_equal(stats$numFeatures, stats2$numFeatures)
+  expect_equal(stats$features, stats2$features)
+  expect_equal(stats$featureImportances, stats2$featureImportances)
+  expect_equal(stats$numTrees, stats2$numTrees)
+  expect_equal(stats$treeWeights, stats2$treeWeights)
+
+  unlink(modelPath)
+
+  # classification
+  # label must be binary - GBTClassifier currently only supports binary classification.
+  iris2 <- iris[iris$Species != "virginica", ]
+  data <- suppressWarnings(createDataFrame(iris2))
+  model <- spark.gbt(data, Species ~ Petal_Length + Petal_Width, "classification")
+  stats <- summary(model)
+  expect_equal(stats$numFeatures, 2)
+  expect_equal(stats$numTrees, 20)
+  expect_error(capture.output(stats), NA)
+  expect_true(length(capture.output(stats)) > 6)
+  predictions <- collect(predict(model, data))$prediction
+  # test string prediction values
+  expect_equal(length(grep("setosa", predictions)), 50)
+  expect_equal(length(grep("versicolor", predictions)), 50)
+
+  modelPath <- tempfile(pattern = "spark-gbtClassification", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  stats2 <- summary(model2)
+  expect_equal(stats$depth, stats2$depth)
+  expect_equal(stats$numNodes, stats2$numNodes)
+  expect_equal(stats$numClasses, stats2$numClasses)
+
+  unlink(modelPath)
+
+  iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
+  df <- suppressWarnings(createDataFrame(iris2))
+  m <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
+  s <- summary(m)
+  # test numeric prediction values
+  expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction))
+  expect_equal(s$numFeatures, 5)
+  expect_equal(s$numTrees, 20)
+})
+
 sparkR.session.stop()