library(testthat)
test_that("Tests test-Xhrf_gpp", {
context("X-RF autotune gpp")
set.seed(1423614230)
feat <- iris[, -1]
tr <- rbinom(nrow(iris), 1, .5)
yobs <- iris[, 1]
#
# ntree = 100
# nthread = 0
# verbose = TRUE
# init_points = 5
# n_iter = 1
#
# starting_settings <- list(
# "start_setting_1" = get_setting_strong(feat, ntree, nthread),
# "start_setting_2" = get_setting_weak(feat, ntree, nthread)
# )
# setup_eval <-
# check_setups(starting_settings, feat, tr, yobs, ntree,
# nthread, verbose)
#
# starting_point <-
# starting_settings[[which.min(setup_eval$comb)]]
#
# mean(as.numeric(evaluate_setting(starting_point, feat, tr, yobs)[1,]))
#
#
# Test_Fun_generic(starting_point,
# feat,
# tr,
# yobs,
# mtry_first = 1.2,
# mtry_second = 1.5,
# nodesizeAvg_first = 3.2,
# nodesizeAvg_second = 3.3,
# nodesizeSpl_first = 3.4,
# nodesizeSpl_second = 3.1)
#
#
#
# get_upper_bounds_for_nodesize(starting_point)
#
# starting_point_optimized <-
# GP_optimize_small(starting_point, feat, tr, yobs)
#
expect_output(
xl_gpp <- X_RF_autotune_gpp(
feat,
tr,
yobs,
ntree = 100,
nthread = 0,
verbose = FALSE,
init_points = 1,
n_iter = 1
)
)
expect_equal(EstimateCate(xl_gpp, feat)[4],
0.09801486,
tolerance = 1e-3)
set.seed(11122)
CI <- CateCI(xl_gpp, feat, B = 5, verbose = FALSE)
expect_equal(CI[2, 3],
0.1940758,
tolerance = 1e-3)
## Example for changing internal parameters of GPfit::GP_fit and
## rBayesianOptimization::BayesianOptimization
# # faster speed:
# library(hte)
# feat <- iris[, -1]
# tr <- rbinom(nrow(iris), 1, .5)
# yobs <- iris[, 1]
#
# # slow, using good optimization:
# xl_gpp <- X_RF_autotune_gpp(
# feat,
# tr,
# yobs,
# ntree = 100,
# nthread = 0,
# verbose = TRUE,
# init_points = 5,
# n_iter = 1
# )
#
# # fast, but with worse optimzation:
# xl_gpp <- X_RF_autotune_gpp(
# feat,
# tr,
# yobs,
# ntree = 100,
# nthread = 0,
# verbose = TRUE,
# init_points = 5,
# n_iter = 1,
# maxit = 2
# )
})
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