Skip doctests for summary outputs

SvenKlaassen · SvenKlaassen · commit 33a86d0f70d7 · 2025-11-14T08:53:39.000+01:00
diff --git a/doubleml/did/did.py b/doubleml/did/did.py
@@ -70,7 +70,7 @@ class DoubleMLDID(LinearScoreMixin, DoubleML):
     >>> data = make_did_SZ2020(n_obs=500, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLDIDData(data, 'y', 'd')
     >>> dml_did_obj = dml.DoubleMLDID(obj_dml_data, ml_g, ml_m)
-    >>> dml_did_obj.fit().summary
+    >>> dml_did_obj.fit().summary  # doctest: +SKIP
            coef   std err         t     P>|t|     2.5 %    97.5 %
     d -2.840718  1.760386 -1.613691  0.106595 -6.291011  0.609575
 
diff --git a/doubleml/did/did_cs.py b/doubleml/did/did_cs.py
@@ -69,7 +69,7 @@ class DoubleMLDIDCS(LinearScoreMixin, DoubleML):
     >>> data = make_did_SZ2020(n_obs=500, cross_sectional_data=True, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLDIDData(data, 'y', 'd', t_col='t')
     >>> dml_did_obj = dml.DoubleMLDIDCS(obj_dml_data, ml_g, ml_m)
-    >>> dml_did_obj.fit().summary
+    >>> dml_did_obj.fit().summary  # doctest: +SKIP
          coef   std err         t     P>|t|      2.5 %    97.5 %
     d -4.9944  7.561785 -0.660479  0.508947 -19.815226  9.826426
     """
diff --git a/doubleml/did/did_multi.py b/doubleml/did/did_multi.py
@@ -140,7 +140,7 @@ class DoubleMLDIDMulti:
     ...     gt_combinations="standard",
     ...     control_group="never_treated",
     ... )
-    >>> print(dml_did_obj.fit().summary)
+    >>> print(dml_did_obj.fit().summary)  # doctest: +SKIP
                                       coef   std err  ...     2.5 %    97.5 %
     ATT(2025-03,2025-01,2025-02) -0.797617  0.459617  ... -1.698450  0.103215
     ATT(2025-03,2025-02,2025-03)  0.270311  0.456453  ... -0.624320  1.164941
diff --git a/doubleml/double_ml.py b/doubleml/double_ml.py
@@ -1255,7 +1255,7 @@ def evaluate_learners(self, learners=None, metric=_rmse):
         >>> def mae(y_true, y_pred):
         ...     subset = np.logical_not(np.isnan(y_true))
         ...     return mean_absolute_error(y_true[subset], y_pred[subset])
-        >>> dml_irm_obj.evaluate_learners(metric=mae)
+        >>> dml_irm_obj.evaluate_learners(metric=mae)  # doctest: +SKIP
         {'ml_g0': array([[0.88173585]]), 'ml_g1': array([[0.83854057]]), 'ml_m': array([[0.35871235]])}
         """
         # if no learners are provided try to evaluate all learners
diff --git a/doubleml/irm/cvar.py b/doubleml/irm/cvar.py
@@ -97,7 +97,7 @@ class DoubleMLCVAR(LinearScoreMixin, DoubleML):
     >>> data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
     >>> dml_cvar_obj = dml.DoubleMLCVAR(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)
-    >>> dml_cvar_obj.fit().summary
+    >>> dml_cvar_obj.fit().summary  # doctest: +SKIP
            coef   std err         t         P>|t|     2.5 %    97.5 %
     d  1.588364  0.096616  16.43989  9.909942e-61  1.398999  1.777728
 
diff --git a/doubleml/irm/iivm.py b/doubleml/irm/iivm.py
@@ -95,7 +95,7 @@ class DoubleMLIIVM(LinearScoreMixin, DoubleML):
     >>> data = make_iivm_data(theta=0.5, n_obs=1000, dim_x=20, alpha_x=1.0, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='z')
     >>> dml_iivm_obj = dml.DoubleMLIIVM(obj_dml_data, ml_g, ml_m, ml_r)
-    >>> dml_iivm_obj.fit().summary
+    >>> dml_iivm_obj.fit().summary  # doctest: +SKIP
            coef   std err         t     P>|t|     2.5 %    97.5 %
     d  0.362398  0.191578  1.891649  0.058538 -0.013088  0.737884
 
diff --git a/doubleml/irm/irm.py b/doubleml/irm/irm.py
@@ -96,7 +96,7 @@ class DoubleMLIRM(LinearScoreMixin, DoubleML):
     >>> data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
     >>> dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m)
-    >>> dml_irm_obj.fit().summary
+    >>> dml_irm_obj.fit().summary  # doctest: +SKIP
            coef   std err         t     P>|t|     2.5 %    97.5 %
     d  0.371972  0.206802  1.798685  0.072069 -0.033353  0.777297
 
diff --git a/doubleml/irm/lpq.py b/doubleml/irm/lpq.py
@@ -99,7 +99,7 @@ class DoubleMLLPQ(NonLinearScoreMixin, DoubleML):
     >>> data = make_iivm_data(theta=0.5, n_obs=1000, dim_x=20, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='z')
     >>> dml_lpq_obj = dml.DoubleMLLPQ(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)
-    >>> dml_lpq_obj.fit().summary
+    >>> dml_lpq_obj.fit().summary  # doctest: +SKIP
            coef   std err         t    P>|t|    2.5 %    97.5 %
     d  0.217244  0.636453  0.341336  0.73285 -1.03018  1.464668
     """
diff --git a/doubleml/irm/pq.py b/doubleml/irm/pq.py
@@ -105,7 +105,7 @@ class DoubleMLPQ(NonLinearScoreMixin, DoubleML):
     >>> data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
     >>> dml_pq_obj = dml.DoubleMLPQ(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)
-    >>> dml_pq_obj.fit().summary
+    >>> dml_pq_obj.fit().summary  # doctest: +SKIP
            coef   std err         t     P>|t|     2.5 %    97.5 %
     d  0.553878  0.149858  3.696011  0.000219  0.260161  0.847595
     """
diff --git a/doubleml/irm/qte.py b/doubleml/irm/qte.py
@@ -88,7 +88,7 @@ class DoubleMLQTE(SampleSplittingMixin):
     >>> data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
     >>> dml_qte_obj = dml.DoubleMLQTE(obj_dml_data, ml_g, ml_m, quantiles=[0.25, 0.5, 0.75])
-    >>> dml_qte_obj.fit().summary
+    >>> dml_qte_obj.fit().summary  # doctest: +SKIP
               coef   std err         t     P>|t|     2.5 %    97.5 %
     0.25  0.274825  0.347310  0.791297  0.428771 -0.405890  0.955541
     0.50  0.449150  0.192539  2.332782  0.019660  0.071782  0.826519
diff --git a/doubleml/irm/ssm.py b/doubleml/irm/ssm.py
@@ -94,7 +94,7 @@ class DoubleMLSSM(LinearScoreMixin, DoubleML):
     >>> ml_pi_sim = clone(learner_class)
     >>> ml_m_sim = clone(learner_class)
     >>> obj_dml_sim = DoubleMLSSM(simul_data, ml_g_sim, ml_pi_sim, ml_m_sim)
-    >>> obj_dml_sim.fit().summary
+    >>> obj_dml_sim.fit().summary  # doctest: +SKIP
            coef   std err         t         P>|t|    2.5 %    97.5 %
     d  0.518517  0.065535  7.912033  2.532202e-15  0.39007  0.646963
 
diff --git a/doubleml/plm/lplr.py b/doubleml/plm/lplr.py
@@ -61,7 +61,7 @@ class DoubleMLLPLR(NonLinearScoreMixin, DoubleML):
     >>> ml_M = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
     >>> obj_dml_data = make_lplr_LZZ2020(alpha=0.5, n_obs=500, dim_x=20)
     >>> dml_lplr_obj = dml.DoubleMLLPLR(obj_dml_data, ml_M, ml_t, ml_m)
-    >>> dml_lplr_obj.fit().summary                                                                      # doctest: +SKIP
+    >>> dml_lplr_obj.fit().summary  # doctest: +SKIP
            coef   std err         t     P>|t|     2.5 %    97.5 %
     d  0.661166  0.172672  3.829038  0.000129  0.322736  0.999596
 
diff --git a/doubleml/plm/pliv.py b/doubleml/plm/pliv.py
@@ -73,7 +73,7 @@ class DoubleMLPLIV(LinearScoreMixin, DoubleML):
     >>> data = make_pliv_CHS2015(alpha=0.5, n_obs=500, dim_x=20, dim_z=1, return_type='DataFrame')
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='Z1')
     >>> dml_pliv_obj = dml.DoubleMLPLIV(obj_dml_data, ml_l, ml_m, ml_r)
-    >>> dml_pliv_obj.fit().summary
+    >>> dml_pliv_obj.fit().summary  # doctest: +SKIP
            coef   std err         t         P>|t|     2.5 %  97.5 %
     d  0.511722  0.087184  5.869427  4.373034e-09  0.340844  0.6826
 
diff --git a/doubleml/plm/plr.py b/doubleml/plm/plr.py
@@ -68,7 +68,7 @@ class DoubleMLPLR(LinearScoreMixin, DoubleML):
     >>> ml_m = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
     >>> obj_dml_data = make_plr_CCDDHNR2018(alpha=0.5, n_obs=500, dim_x=20)
     >>> dml_plr_obj = dml.DoubleMLPLR(obj_dml_data, ml_g, ml_m)
-    >>> dml_plr_obj.fit().summary
+    >>> dml_plr_obj.fit().summary  # doctest: +SKIP
            coef   std err          t         P>|t|     2.5 %    97.5 %
     d  0.480691  0.040533  11.859129  1.929729e-32  0.401247  0.560135
 
diff --git a/doubleml/rdd/rdd.py b/doubleml/rdd/rdd.py
@@ -91,6 +91,7 @@ class RDFlex:
     >>> ml_g = RandomForestRegressor()
     >>> ml_m = RandomForestClassifier()
     >>> rdflex_obj = dml.rdd.RDFlex(obj_dml_data, ml_g, ml_m, fuzzy=True)
+    >>> print(rdflex_obj.fit())  # doctest: +SKIP
     """
 
     def __init__(
diff --git a/doubleml/utils/_check_return_types.py b/doubleml/utils/_check_return_types.py
@@ -11,7 +11,7 @@ def check_basic_return_types(dml_obj, cls):
     assert isinstance(dml_obj.__str__(), str)
     assert isinstance(dml_obj.summary, pd.DataFrame)
     assert isinstance(dml_obj.draw_sample_splitting(), cls)
-    if not dml_obj._is_cluster_data and not hasattr(dml_obj, "n_folds_inner"):
+    if not dml_obj._is_cluster_data and not hasattr(dml_obj, "n_folds_inner"):  # set_sample_splitting is not available
         assert isinstance(dml_obj.set_sample_splitting(dml_obj.smpls), cls)
     elif dml_obj._is_cluster_data:
         assert dml_obj._dml_data.is_cluster_data
