change default for trimming and fix test defaults

Author: SvenKlaassen

doubleml/_utils.py

@@ -234,4 +234,4 @@ def _check_is_propensity(preds, learner, learner_name, smpls, eps=1e-12):
     if any((preds[test_indices] < eps) | (preds[test_indices] > 1 - eps)):
         warnings.warn(f'Propensity predictions from learner {str(learner)} for'
                       f' {learner_name} are close to zero or one (eps={eps}).')
-    return
+    return

doubleml/double_ml_iivm.py

@@ -61,7 +61,7 @@ class DoubleMLIIVM(LinearScoreMixin, DoubleML):
 
     trimming_threshold : float
         The threshold used for trimming.
-        Default is ``1e-12``.
+        Default is ``1e-2``.
 
     draw_sample_splitting : bool
         Indicates whether the sample splitting should be drawn during initialization of the object.
@@ -129,7 +129,7 @@ def __init__(self,
                  subgroups=None,
                  dml_procedure='dml2',
                  trimming_rule='truncate',
-                 trimming_threshold=1e-12,
+                 trimming_threshold=1e-2,
                  draw_sample_splitting=True,
                  apply_cross_fitting=True):
         super().__init__(obj_dml_data,

doubleml/double_ml_irm.py

@@ -55,7 +55,7 @@ class DoubleMLIRM(LinearScoreMixin, DoubleML):
 
     trimming_threshold : float
         The threshold used for trimming.
-        Default is ``1e-12``.
+        Default is ``1e-2``.
 
     draw_sample_splitting : bool
         Indicates whether the sample splitting should be drawn during initialization of the object.
@@ -114,7 +114,7 @@ def __init__(self,
                  score='ATE',
                  dml_procedure='dml2',
                  trimming_rule='truncate',
-                 trimming_threshold=1e-12,
+                 trimming_threshold=1e-2,
                  draw_sample_splitting=True,
                  apply_cross_fitting=True):
         super().__init__(obj_dml_data,

doubleml/tests/_utils_iivm_manual.py

@@ -8,7 +8,7 @@
 def fit_iivm(y, x, d, z,
              learner_g, learner_m, learner_r, all_smpls, dml_procedure, score,
              n_rep=1, g0_params=None, g1_params=None, m_params=None, r0_params=None, r1_params=None,
-             trimming_threshold=1e-12, always_takers=True, never_takers=True):
+             trimming_threshold=1e-2, always_takers=True, never_takers=True):
     n_obs = len(y)
 
     thetas = np.zeros(n_rep)

doubleml/tests/_utils_irm_manual.py

@@ -8,7 +8,7 @@
 def fit_irm(y, x, d,
             learner_g, learner_m, all_smpls, dml_procedure, score,
             n_rep=1, g0_params=None, g1_params=None, m_params=None,
-            trimming_threshold=1e-12):
+            trimming_threshold=1e-2):
     n_obs = len(y)
 
     thetas = np.zeros(n_rep)

doubleml/tests/test_doubleml_model_defaults.py

@@ -35,7 +35,7 @@ def _assert_resampling_default_settings(dml_obj):
     assert dml_obj.apply_cross_fitting
 
     # fit method
-    assert dml_obj.predictions is None
+    assert dml_obj.predictions is not None
     assert dml_obj.models is None
 
     # bootstrap method
@@ -71,7 +71,7 @@ def test_irm_defaults():
     assert dml_irm.score == 'ATE'
     assert dml_irm.dml_procedure == 'dml2'
     assert dml_irm.trimming_rule == 'truncate'
-    assert dml_irm.trimming_threshold == 1e-12
+    assert dml_irm.trimming_threshold == 1e-2
 
 
 @pytest.mark.ci
@@ -81,4 +81,4 @@ def test_iivm_defaults():
     assert dml_iivm.subgroups == {'always_takers': True, 'never_takers': True}
     assert dml_iivm.dml_procedure == 'dml2'
     assert dml_iivm.trimming_rule == 'truncate'
-    assert dml_iivm.trimming_threshold == 1e-12
+    assert dml_iivm.trimming_threshold == 1e-2