Nothing
context("Test xgboost models")
source("helper-utils.R")
test_random_forest_classifier <- function(x, y) {
# Suppress intentional warnings
suppressWarnings(
{
model <- h2o4gpu.random_forest_classifier() %>% fit(x, y)
predictions <- model %>% predict(x)
expect_equal(length(y), length(predictions))
}
)
}
test_succeeds("Random Forest classifier works correctly with numeric labels", {
x <- iris[1:4]
y <- as.integer(iris$Species) - 1
test_random_forest_classifier(x, y)
})
test_succeeds("Random Forest classifier works correctly with sparse feature matrix", {
if (require("Matrix")) {
N <- 100
x <- sparseMatrix(
i = sample(N, N),
j = sample(N, N),
x = runif(N),
dims = c(N, N))
y <- c(rep(0, N/2), rep(1, N/2))
test_random_forest_classifier(x, y)
}
})
test_succeeds("Random Forest classifier works correctly with factor labels", {
x <- iris[1:4]
y <- iris$Species
test_random_forest_classifier(x, y)
})
test_succeeds("Random Forest regressor works correctly", {
x <- longley[1:6]
y <- longley$Employed
model <- h2o4gpu.random_forest_regressor() %>% fit(x, y)
predictions <- model %>% predict(x)
expect_true(0 < predictions && predictions < 100)
expect_error(h2o4gpu.random_forest_regressor() %>% fit(x, as.character(y)), regexp = 'Your model input "y" is non-numeric')
})
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