spark-instrumented-optimizer/R/pkg/inst/tests/testthat/test_mllib_tree.R

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library(testthat)

context("MLlib tree-based algorithms")

# Tests for MLlib tree-based algorithms in SparkR
sparkSession <- sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)

absoluteSparkPath <- function(x) {
  sparkHome <- sparkR.conf("spark.home")
  file.path(sparkHome, x)
}

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$maxDepth, 5)
  expect_equal(stats$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$maxDepth, stats2$maxDepth)
  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$numTrees, 20)
  expect_equal(stats$maxDepth, 5)
  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)
  expect_equal(stats$maxDepth, 5)

  # 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)
})

test_that("spark.randomForest", {
  # regression
  data <- suppressWarnings(createDataFrame(longley))
  model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
                              numTrees = 1)

  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$numTrees, 1)
  expect_equal(stats$maxDepth, 5)
  expect_error(capture.output(stats), NA)
  expect_true(length(capture.output(stats)) > 6)

  model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
                              numTrees = 20, seed = 123)
  predictions <- collect(predict(model, data))
  expect_equal(predictions$prediction, c(60.32820, 61.22315, 60.69025, 62.11070,
                                         63.53160, 64.05470, 65.12710, 64.30450,
                                         66.70910, 67.86125, 68.08700, 67.21865,
                                         68.89275, 69.53180, 69.39640, 69.68250),
               tolerance = 1e-4)
  stats <- summary(model)
  expect_equal(stats$numTrees, 20)
  expect_equal(stats$maxDepth, 5)

  modelPath <- tempfile(pattern = "spark-randomForestRegression", 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$maxDepth, stats2$maxDepth)
  expect_equal(stats$treeWeights, stats2$treeWeights)

  unlink(modelPath)

  # classification
  data <- suppressWarnings(createDataFrame(iris))
  model <- spark.randomForest(data, Species ~ Petal_Length + Petal_Width, "classification",
                              maxDepth = 5, maxBins = 16)

  stats <- summary(model)
  expect_equal(stats$numFeatures, 2)
  expect_equal(stats$numTrees, 20)
  expect_equal(stats$maxDepth, 5)
  expect_error(capture.output(stats), NA)
  expect_true(length(capture.output(stats)) > 6)
  # Test string prediction values
  predictions <- collect(predict(model, data))$prediction
  expect_equal(length(grep("setosa", predictions)), 50)
  expect_equal(length(grep("versicolor", predictions)), 50)

  modelPath <- tempfile(pattern = "spark-randomForestClassification", 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)

  # Test numeric response variable
  labelToIndex <- function(species) {
    switch(as.character(species),
      setosa = 0.0,
      versicolor = 1.0,
      virginica = 2.0
    )
  }
  iris$NumericSpecies <- lapply(iris$Species, labelToIndex)
  data <- suppressWarnings(createDataFrame(iris[-5]))
  model <- spark.randomForest(data, NumericSpecies ~ Petal_Length + Petal_Width, "classification",
                              maxDepth = 5, maxBins = 16)
  stats <- summary(model)
  expect_equal(stats$numFeatures, 2)
  expect_equal(stats$numTrees, 20)
  expect_equal(stats$maxDepth, 5)

  # Test numeric prediction values
  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)
})

sparkR.session.stop()