Nothing
context("Tests the gpe S3 functions")
test_that("Print works for gpe", {
dig <- getOption("digits")
on.exit(options(digits = dig))
options(digits = 3)
#####
# W/ linear terms
set.seed(4825707)
airq.ens <- gpe(
Ozone ~ .,
data= airquality,
base_learners = list(gpe_linear()))
# save_to_test(capture.output(print(airq.ens)), "gpe_airquality_print_output_linear")
expect_equal(capture.output(print(airq.ens)), read_to_test("gpe_airquality_print_output_linear"))
#####
# W/ trees
airq.ens <- gpe(
Ozone ~ .,
data= airquality,
base_learners = list(gpe_trees(ntrees = 3)))
# save_to_test(capture.output(print(airq.ens)), "gpe_airquality_print_output")
expect_equal(capture.output(print(airq.ens)),
read_to_test("gpe_airquality_print_output"))
#####
# W/ earth
airq.ens <- gpe(
Ozone ~ .,
data= airquality,
base_learners = list(
gpe_earth(
ntrain = 3)))
# save_to_test(capture.output(print(airq.ens)), "gpe_airquality_print_output_earth")
expect_equal(capture.output(print(airq.ens)), read_to_test("gpe_airquality_print_output_earth"))
})
test_that("Coef works for gpe", {
set.seed(9116073)
airq.ens <- gpe(
Ozone ~ .,
data=airquality,
base_learners = list(gpe_trees(ntrees = 10)))
coefs <- coef(airq.ens)
# save_to_test(coefs, "gpe_airquality_w_pre_coef")
expect_equal(coefs, read_to_test("gpe_airquality_w_pre_coef"), tolerance = 1.49e-08)
})
test_that("Predict works for gpe and gives previous results", {
#####
# Regression problem
set.seed(seed <- 9638602)
airq.ens <- gpe(
Ozone ~ .,
data = airquality,
base_learners = list(gpe_trees(ntrees = 10)))
preds <- predict(airq.ens)
# plot(preds ~ airquality$Ozone[complete.cases(airquality)])
# abline(a = 0, b = 1, lty = 2)
# save_to_test(preds, "gpe_predict_regression")
expect_equal(preds, read_to_test("gpe_predict_regression"), tolerance = 1.49e-08)
set.seed(seed)
airq.ens <- gpe(
Ozone ~ .,
data=airquality,
base_learners = list(gpe_trees(ntrees = 10)),
model = FALSE) # don't save model
expect_null(airq.ens$data)
expect_error(predict(airq.ens),
"Predict called with no new object and no saved data with gpe")
preds_new <- predict(airq.ens, newdata = airquality)
expect_equal(preds, preds_new)
#####
# Binary
set.seed(seed)
fit <- gpe(
diabetes ~ ., data = PimaIndiansDiabetes,
base_learners = list(gpe_trees(ntrees = 3), gpe_linear()))
preds <- predict(fit, type = "response")
# actual <- PimaIndiansDiabetes$diabetes == levels(PimaIndiansDiabetes$diabetes)[2]
# plot(smooth.spline(preds, actual), type = "l", ylim = c(-.2, 1.2), col = "red")
# points(preds, jitter(as.integer(actual), .1))
# hist(preds)
# save_to_test(preds[1:100], "gpe_predict_binary_response")
expect_equal(preds[1:100], read_to_test("gpe_predict_binary_response"), tolerance = 1.49e-08)
})
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