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test-double_ml_irm.R
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test-double_ml_irm.R
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context("Unit tests for IRM")
library("mlr3learners")
lgr::get_logger("mlr3")$set_threshold("warn")
on_cran = !identical(Sys.getenv("NOT_CRAN"), "true")
if (on_cran) {
test_cases = expand.grid(
learner = "cv_glmnet",
dml_procedure = "dml1",
score = "ATTE",
i_setting = 1:(length(data_irm)),
trimming_threshold = 0,
stringsAsFactors = FALSE)
test_cases["test_name"] = apply(test_cases, 1, paste, collapse = "_")
} else {
test_cases = expand.grid(
learner = "cv_glmnet",
dml_procedure = c("dml1", "dml2"),
score = c("ATE", "ATTE"),
i_setting = 1:(length(data_irm)),
trimming_threshold = 0,
stringsAsFactors = FALSE)
test_cases["test_name"] = apply(test_cases, 1, paste, collapse = "_")
}
patrick::with_parameters_test_that("Unit tests for IRM:",
.cases = test_cases, {
learner_pars = get_default_mlmethod_irm(learner)
n_rep_boot = 498
set.seed(i_setting)
irm_hat = dml_irm(data_irm[[i_setting]],
y = "y", d = "d",
n_folds = 5, mlmethod = learner_pars$mlmethod,
params = learner_pars$params,
dml_procedure = dml_procedure, score = score)
theta = irm_hat$coef
se = irm_hat$se
boot_theta = bootstrap_irm(irm_hat$thetas, irm_hat$ses,
data_irm[[i_setting]],
y = "y", d = "d",
n_folds = 5, smpls = irm_hat$smpls,
all_preds= irm_hat$all_preds,
score = score,
bootstrap = "normal", n_rep_boot = n_rep_boot)$boot_coef
set.seed(i_setting)
Xnames = names(data_irm[[i_setting]])[names(data_irm[[i_setting]]) %in% c("y", "d", "z") == FALSE]
data_ml = double_ml_data_from_data_frame(data_irm[[i_setting]],
y_col = "y",
d_cols = "d", x_cols = Xnames)
double_mlirm_obj = DoubleMLIRM$new(data_ml,
n_folds = 5,
ml_g = learner_pars$mlmethod$mlmethod_g,
ml_m = learner_pars$mlmethod$mlmethod_m,
dml_procedure = dml_procedure,
score = score,
trimming_threshold = trimming_threshold)
# set params for nuisance part m
double_mlirm_obj$set_ml_nuisance_params(
learner = "ml_m",
treat_var = "d",
params = learner_pars$params$params_m)
# set params for nuisance part g
double_mlirm_obj$set_ml_nuisance_params(
learner = "ml_g0",
treat_var = "d",
params = learner_pars$params$params_g)
double_mlirm_obj$set_ml_nuisance_params(
learner = "ml_g1",
treat_var = "d",
params = learner_pars$params$params_g)
double_mlirm_obj$fit()
theta_obj = double_mlirm_obj$coef
se_obj = double_mlirm_obj$se
# bootstrap
double_mlirm_obj$bootstrap(method = 'normal', n_rep = n_rep_boot)
boot_theta_obj = double_mlirm_obj$boot_coef
# at the moment the object result comes without a name
expect_equal(theta, theta_obj, tolerance = 1e-8)
expect_equal(se, se_obj, tolerance = 1e-8)
expect_equal(as.vector(boot_theta), as.vector(boot_theta_obj), tolerance = 1e-8)
}
)