tests/testthat/test-double_ml_pliv.R

In DoubleML: Double Machine Learning in R

context("Unit tests for PLIV")

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",
    stringsAsFactors = FALSE)
} else {
  test_cases = expand.grid(
    learner = c("regr.lm", "regr.glmnet", "graph_learner"),
    dml_procedure = c("dml1", "dml2"),
    score = c("partialling out", "IV-type"),
    stringsAsFactors = FALSE)
}
test_cases[".test_name"] = apply(test_cases, 1, paste, collapse = "_")

patrick::with_parameters_test_that("Unit tests for PLIV:",
  .cases = test_cases, {
    learner_pars = get_default_mlmethod_pliv(learner)
    n_rep_boot = 498

    set.seed(3141)
    pliv_hat = dml_pliv(data_pliv$df,
      y = "y", d = "d", z = "z",
      n_folds = 5,
      ml_l = learner_pars$ml_l$clone(),
      ml_m = learner_pars$ml_m$clone(),
      ml_r = learner_pars$ml_r$clone(),
      ml_g = learner_pars$ml_g$clone(),
      dml_procedure = dml_procedure, score = score)
    theta = pliv_hat$coef
    se = pliv_hat$se

    boot_theta = bootstrap_pliv(pliv_hat$thetas, pliv_hat$ses,
      data_pliv$df,
      y = "y", d = "d", z = "z",
      n_folds = 5, smpls = pliv_hat$smpls,
      all_preds = pliv_hat$all_preds,
      bootstrap = "normal", n_rep_boot = n_rep_boot,
      score = score)$boot_coef

    set.seed(3141)
    if (score == "partialling out") {
      double_mlpliv_obj = DoubleMLPLIV$new(
        data = data_pliv$dml_data,
        n_folds = 5,
        ml_l = learner_pars$ml_l$clone(),
        ml_m = learner_pars$ml_m$clone(),
        ml_r = learner_pars$ml_r$clone(),
        dml_procedure = dml_procedure,
        score = score)
    } else {
      double_mlpliv_obj = DoubleMLPLIV$new(
        data = data_pliv$dml_data,
        n_folds = 5,
        ml_l = learner_pars$ml_l$clone(),
        ml_m = learner_pars$ml_m$clone(),
        ml_r = learner_pars$ml_r$clone(),
        ml_g = learner_pars$ml_g$clone(),
        dml_procedure = dml_procedure,
        score = score)
    }

    double_mlpliv_obj$fit(store_predictions = T)
    theta_obj = double_mlpliv_obj$coef
    se_obj = double_mlpliv_obj$se

    # bootstrap
    double_mlpliv_obj$bootstrap(method = "normal", n_rep = n_rep_boot)
    boot_theta_obj = double_mlpliv_obj$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)
  }
)