test for initializer with IV-type score PLIV

Author: PhilippBach

tests/testthat/test-double_ml_pliv_partial_functional_initializer_IVtype.R

@@ -13,7 +13,7 @@ if (on_cran) {
   test_cases = expand.grid(
     learner = c("regr.lm", "regr.cv_glmnet"),
     dml_procedure = c("dml1", "dml2"),
-    score = c("IV-type"),
+    score = "IV-type",
     stringsAsFactors = FALSE)
 }
 test_cases[".test_name"] = apply(test_cases, 1, paste, collapse = "_")
@@ -24,67 +24,40 @@ patrick::with_parameters_test_that("Unit tests for PLIV (partialX functional ini
     df = data_pliv$df
     Xnames = names(df)[names(df) %in% c("y", "d", "z", "z2") == FALSE]
     data_ml = double_ml_data_from_data_frame(df,
-      y_col = "y",
-      d_cols = "d", x_cols = Xnames, z_cols = "z")
-
-    if (score == "partialling out") {
-      # Partial out X (default PLIV)
-      set.seed(3141)
-      double_mlpliv_obj = DoubleMLPLIV$new(data_ml,
-                                           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)
-      
-      double_mlpliv_obj$fit()
-      theta_obj = double_mlpliv_obj$coef
-      se_obj = double_mlpliv_obj$se
-      
-      # Partial out X
-      set.seed(3141)
-      double_mlpliv_partX = DoubleMLPLIV.partialX(data_ml,
-                                                  n_folds = 5,
-                                                  ml_l = learner_pars$ml_l$clone(),
-                                                  ml_m = learner_pars$ml_m$clone(),
-                                                  ml_r = learner_pars$ml_r$clone(),
-                                                  dml_procedure = dml_procedure,
-                                                  score = score)
-    } else if (score == "IV-type") {
-            # Partial out X (default PLIV)
-      set.seed(3141)
-      double_mlpliv_obj = DoubleMLPLIV$new(data_ml,
-                                           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$fit()
-      theta_obj = double_mlpliv_obj$coef
-      se_obj = double_mlpliv_obj$se
-      
-      # Partial out X
-      set.seed(3141)
-      double_mlpliv_partX = DoubleMLPLIV.partialX(data_ml,
-                                                  n_folds = 5,
-                                                  ml_l = learner_pars$ml_l$clone(),
-                                                  ml_m = learner_pars$ml_m$clone(),
-                                                  ml_r = learner_pars$ml_r$clone(),
-                                                  ml_g = learner_pars$ml_g$clone(),
-                                                  dml_procedure = dml_procedure,
-                                                  score = score)
-      
-    }
-
-    double_mlpliv_partX$fit()
-    theta_partX = double_mlpliv_partX$coef
-    se_partX = double_mlpliv_partX$se
-
-    expect_equal(theta_partX, theta_obj, tolerance = 1e-8)
-    expect_equal(se_partX, se_obj, tolerance = 1e-8)
+                                             y_col = "y",
+                                             d_cols = "d", x_cols = Xnames, z_cols = "z")
+    
+    # Partial out X (default PLIV)
+    set.seed(3141)
+    double_mlpliv_obj = DoubleMLPLIV$new(data_ml,
+                                         n_folds = 5,
+                                         ml_l = learner_pars$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()
+    theta_obj = double_mlpliv_obj$coef
+    se_obj = double_mlpliv_obj$se
+    
+    # Partial out X
+    set.seed(3141)
+    double_mlpliv_partX = DoubleMLPLIV.partialX(data_ml,
+                                                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_partX$fit()
+  theta_partX = double_mlpliv_partX$coef
+  se_partX = double_mlpliv_partX$se
+  
+  expect_equal(theta_partX, theta_obj, tolerance = 1e-8)
+  expect_equal(se_partX, se_obj, tolerance = 1e-8)
   }
 )