diff options
Diffstat (limited to 'R/pkg/inst/tests')
-rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 20 |
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) |