Nothing
test_that('updating', {
expect_snapshot(
poisson_reg(penalty = 1) %>%
set_engine("glmnet", lambda.min.ratio = 0.001) %>%
update(mixture = tune())
)
})
test_that('bad input', {
expect_error(poisson_reg(mode = "bogus"))
expect_error(translate(poisson_reg(mode = "regression"), engine = NULL))
expect_error(translate(poisson_reg(formula = y ~ x)))
})
# ------------------------------------------------------------------------------
# glm execution tests
test_that("glm execution", {
expect_error(
res <- fit(
glm_spec,
count ~ .,
data = seniors,
control = ctrl
),
regexp = NA
)
expect_error(
res <- fit_xy(
glm_spec,
x = seniors[, 1:3],
y = seniors$count,
control = ctrl
),
regexp = NA
)
expect_error(
res <- fit(
glm_spec,
y ~ x,
data = seniors,
control = ctrl
)
)
})
test_that("glm prediction", {
glm_fit <- glm(count ~ ., data = seniors, family = "poisson")
glm_pred <- unname(predict(glm_fit, seniors[1:3, 1:3], type = "response"))
res_xy <- fit_xy(
glm_spec,
x = seniors[, 1:3],
y = seniors$count,
control = ctrl
)
expect_equal(glm_pred, predict(res_xy, seniors[1:3, 1:3])$.pred)
res_form <- fit(
glm_spec,
count ~ .,
data = seniors,
control = ctrl
)
expect_equal(glm_pred, predict(res_form, seniors[1:3, ])$.pred)
})
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