skip_on_cran()
skip_if_not_installed("modeltests")
library(modeltests)
skip_if_not_installed("glmnetUtils")
suppressPackageStartupMessages(library(glmnetUtils))
set.seed(27)
skip_if_not_installed("modeldata")
library(modeldata)
data(hpc_data)
hpc_data <- hpc_data[1:300, ]
fit <- glmnet(formula = mpg ~ ., data = mtcars)
fit2 <- glmnet(
formula = class ~ compounds + input_fields + iterations + num_pending,
data = hpc_data, family = "multinomial"
)
cv_fit <- cv.glmnet(formula = mpg ~ ., data = mtcars)
cv_fit2 <- cv.glmnet(
formula = class ~ compounds + input_fields + iterations + num_pending,
data = hpc_data, family = "multinomial"
)
test_that("glmnet.formula tidier arguments", {
check_arguments(tidy.glmnet)
check_arguments(glance.glmnet)
check_arguments(tidy.cv.glmnet)
check_arguments(glance.cv.glmnet)
})
test_that("tidy.glmnet.formula", {
td <- tidy(fit)
tdz <- tidy(fit, return_zeros = TRUE)
check_tidy_output(td)
check_tidy_output(tdz)
check_dims(td, expected_cols = 5)
check_dims(tdz, expected_cols = 5)
expect_true(all(td$estimate != 0))
expect_true(any(tdz$estimate == 0))
# multinomial
td2 <- tidy(fit2)
td2z <- tidy(fit2, return_zeros = TRUE)
check_tidy_output(td2)
check_tidy_output(td2z)
expect_s3_class(td2, "tbl_df")
expect_equal(dim(td2), c(1511L, 6L))
expect_equal(dim(td2z), c(2000L, 6L))
expect_true(all(td2$estimate != 0))
expect_true(any(td2z$estimate == 0))
# regression tests
expect_true(is.numeric(td$step) && !any(is.na(td$step)))
expect_true(is.numeric(td2$step) && !any(is.na(td2$step)))
})
test_that("glance.glmnet.formula", {
gl <- glance(fit)
gl2 <- glance(fit2)
check_glance_outputs(gl, gl2)
expect_s3_class(gl, "tbl_df")
expect_equal(dim(gl), c(1L, 3L))
})
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