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

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library(testthat)

context("MLlib functions")

# Tests for MLlib functions in SparkR

sc <- sparkR.init()

sqlContext <- sparkRSQL.init(sc)

test_that("glm and predict", {
  training <- suppressWarnings(createDataFrame(sqlContext, iris))
  test <- select(training, "Sepal_Length")
  model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian")
  prediction <- predict(model, test)
  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")

  # Test stats::predict is working
  x <- rnorm(15)
  y <- x + rnorm(15)
  expect_equal(length(predict(lm(y ~ x))), 15)
})

test_that("glm should work with long formula", {
  training <- suppressWarnings(createDataFrame(sqlContext, iris))
  training$LongLongLongLongLongName <- training$Sepal_Width
  training$VeryLongLongLongLonLongName <- training$Sepal_Length
  training$AnotherLongLongLongLongName <- training$Species
  model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
               data = training)
  vals <- collect(select(predict(model, training), "prediction"))
  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})

test_that("predictions match with native glm", {
  training <- suppressWarnings(createDataFrame(sqlContext, iris))
  model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
  vals <- collect(select(predict(model, training), "prediction"))
  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})

test_that("dot minus and intercept vs native glm", {
  training <- suppressWarnings(createDataFrame(sqlContext, iris))
  model <- glm(Sepal_Width ~ . - Species + 0, data = training)
  vals <- collect(select(predict(model, training), "prediction"))
  rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})

test_that("feature interaction vs native glm", {
  training <- suppressWarnings(createDataFrame(sqlContext, iris))
  model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
  vals <- collect(select(predict(model, training), "prediction"))
  rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})

test_that("summary coefficients match with native glm", {
  training <- suppressWarnings(createDataFrame(sqlContext, iris))
  stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training, solver = "normal"))
  coefs <- unlist(stats$coefficients)
  devianceResiduals <- unlist(stats$devianceResiduals)

  rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
  rCoefs <- unlist(rStats$coefficients)
  rDevianceResiduals <- c(-0.95096, 0.72918)

  expect_true(all(abs(rCoefs - coefs) < 1e-5))
  expect_true(all(abs(rDevianceResiduals - devianceResiduals) < 1e-5))
  expect_true(all(
    rownames(stats$coefficients) ==
    c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
})

test_that("summary coefficients match with native glm of family 'binomial'", {
  df <- suppressWarnings(createDataFrame(sqlContext, iris))
  training <- filter(df, df$Species != "setosa")
  stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
    family = "binomial"))
  coefs <- as.vector(stats$coefficients[,1])

  rTraining <- iris[iris$Species %in% c("versicolor","virginica"),]
  rCoefs <- as.vector(coef(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
    family = binomial(link = "logit"))))

  expect_true(all(abs(rCoefs - coefs) < 1e-4))
  expect_true(all(
    rownames(stats$coefficients) ==
    c("(Intercept)", "Sepal_Length", "Sepal_Width")))
})

test_that("summary works on base GLM models", {
  baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
  baseSummary <- summary(baseModel)
  expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
})

test_that("kmeans", {
  newIris <- iris
  newIris$Species <- NULL
  training <- suppressWarnings(createDataFrame(sqlContext, newIris))

  # Cache the DataFrame here to work around the bug SPARK-13178.
  cache(training)
  take(training, 1)

  model <- kmeans(x = training, centers = 2)
  sample <- take(select(predict(model, training), "prediction"), 1)
  expect_equal(typeof(sample$prediction), "integer")
  expect_equal(sample$prediction, 1)

  # Test stats::kmeans is working
  statsModel <- kmeans(x = newIris, centers = 2)
  expect_equal(sort(unique(statsModel$cluster)), c(1, 2))

  # Test fitted works on KMeans
  fitted.model <- fitted(model)
  expect_equal(sort(collect(distinct(select(fitted.model, "prediction")))$prediction), c(0, 1))

  # Test summary works on KMeans
  summary.model <- summary(model)
  cluster <- summary.model$cluster
  expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
})