library(testthat)
test_that("Tests X_RF_autotune_hyperband", {
context("X-RF autotune hyperband")
set.seed(1423614230)
feat <- iris[, -1]
tr <- rbinom(nrow(iris), 1, .5)
yobs <- iris[, 1]
sample.fraction <- .75
num_iter <- 3 ^ 2
eta <- 3
verbose <- TRUE
seed <- 24750371
nthread <- 1
xl <- X_RF_autotune_hyperband(
feat = feat,
tr = tr,
yobs = yobs,
sample.fraction = sample.fraction,
num_iter = num_iter,
eta = eta,
verbose = FALSE,
seed = seed,
nthread = nthread
)
expect_equal(EstimateCate(xl, feat)[1],
-0.07525655,
tolerance = 1e-3)
#### real example ####
set.seed(432)
cate_problem <-
simulate_causal_experiment(
ntrain = 400,
ntest = 10000,
dim = 20,
alpha = .1,
feat_distribution = "normal",
setup = "RespSparseTau1strong",
testseed = 543,
trainseed = 234
)
xl_tuned <- X_RF_autotune_hyperband(
feat = cate_problem$feat_tr,
yobs = cate_problem$Yobs_tr,
tr = cate_problem$W_tr,
num_iter = 3 ^ 2,
verbose = FALSE
)
expect_equal(mean((
EstimateCate(xl_tuned, cate_problem$feat_te) - cate_problem$tau_te
) ^ 2),
337.1134,
tolerance = 1)
# ----------------------------------------------------------------------------
set.seed(21)
feat <- iris[, -1]
tr <- rbinom(nrow(iris), 1, .5)
yobs <- iris[, 1]
num_iter <- 2 ^ 3
eta <- 2
verbose <- TRUE
seed <- 24750371
nthread <- 1
xl_at <- X_RF_autotune_hyperband(
feat = feat,
tr = tr,
yobs = yobs,
num_iter = num_iter,
eta = eta,
verbose = FALSE,
nthread = 1
)
CIs <- CateCI(
theObject = xl_at,
feature_new = feat,
B = 5,
verbose = FALSE
)
#theObject = xl_at; feature_new = feat; B = 5; verbose = FALSE
expect_equal(as.numeric(CIs[1,]),
c(-0.222455421, -0.454358265, 0.009447424),
tolerance = 1e-4)
})
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