Nothing
context("Unit tests for PLR with repeated cross-fitting")
library("mlr3learners")
lgr::get_logger("mlr3")$set_threshold("warn")
on_cran = !identical(Sys.getenv("NOT_CRAN"), "true")
if (on_cran) {
test_cases = expand.grid(
learner = "regr.lm",
dml_procedure = "dml1",
score = "partialling out",
n_rep = c(5),
stringsAsFactors = FALSE)
test_cases[".test_name"] = apply(test_cases, 1, paste, collapse = "_")
} else {
test_cases = expand.grid(
learner = c("regr.lm", "regr.cv_glmnet"),
dml_procedure = c("dml1", "dml2"),
score = c("IV-type", "partialling out"),
n_rep = c(2, 5),
stringsAsFactors = FALSE)
test_cases[".test_name"] = apply(test_cases, 1, paste, collapse = "_")
}
patrick::with_parameters_test_that("Unit tests for PLR:",
.cases = test_cases, {
learner_pars = get_default_mlmethod_plr(learner)
n_rep_boot = 498
set.seed(3141)
n_folds = 5
if (score == "IV-type") {
ml_g = learner_pars$ml_g$clone()
} else {
ml_g = NULL
}
plr_hat = dml_plr(data_plr$df,
y = "y", d = "d",
n_folds = n_folds, n_rep = n_rep,
ml_l = learner_pars$ml_l$clone(),
ml_m = learner_pars$ml_m$clone(),
ml_g = ml_g,
dml_procedure = dml_procedure, score = score)
theta = plr_hat$coef
se = plr_hat$se
t = plr_hat$t
pval = plr_hat$pval
# ci = confint(plr_hat, level = 0.95, joint = FALSE)
boot_theta = bootstrap_plr(plr_hat$thetas, plr_hat$ses,
data_plr$df,
y = "y", d = "d",
n_folds = n_folds, n_rep = n_rep,
smpls = plr_hat$smpls,
all_preds = plr_hat$all_preds,
bootstrap = "normal", n_rep_boot = n_rep_boot,
score = score)$boot_coef
set.seed(3141)
if (score == "IV-type") {
ml_g = learner_pars$ml_g$clone()
} else {
ml_g = NULL
}
double_mlplr_obj = DoubleMLPLR$new(
data = data_plr$dml_data,
ml_l = learner_pars$ml_l$clone(),
ml_m = learner_pars$ml_m$clone(),
ml_g = ml_g,
dml_procedure = dml_procedure,
n_folds = n_folds,
score = score,
n_rep = n_rep)
double_mlplr_obj$fit()
theta_obj = double_mlplr_obj$coef
se_obj = double_mlplr_obj$se
t_obj = double_mlplr_obj$t_stat
pval_obj = double_mlplr_obj$pval
# ci_obj = double_mlplr_obj$confint(level = 0.95, joint = FALSE)
# bootstrap
double_mlplr_obj$bootstrap(method = "normal", n_rep_boot = n_rep_boot)
boot_theta_obj = double_mlplr_obj$boot_coef
expect_equal(theta, theta_obj, tolerance = 1e-8)
expect_equal(se, se_obj, tolerance = 1e-8)
expect_equal(t, t_obj, tolerance = 1e-8)
expect_equal(pval, pval_obj, tolerance = 1e-8)
# expect_equal(ci, ci_obj, tolerance = 1e-8)
expect_equal(as.vector(boot_theta), as.vector(boot_theta_obj), tolerance = 1e-8)
}
)
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