remove deprecated nuisance tuning and parameter setters for plr and pliv

Author: SvenKlaassen

doubleml/double_ml_pliv.py

@@ -281,17 +281,6 @@ def _check_data(self, obj_dml_data):
                              'use DoubleMLPLR instead of DoubleMLPLIV.')
         return
 
-    # To be removed in version 0.6.0
-    def set_ml_nuisance_params(self, learner, treat_var, params):
-        if isinstance(self.score, str) & (self.score == 'partialling out') & (learner == 'ml_g'):
-            warnings.warn(("Learner ml_g was renamed to ml_l. "
-                           "Please adapt the argument learner accordingly. "
-                           "The provided parameters are set for ml_l. "
-                           "The redirection will be removed in a future version."),
-                          DeprecationWarning, stacklevel=2)
-            learner = 'ml_l'
-        super(DoubleMLPLIV, self).set_ml_nuisance_params(learner, treat_var, params)
-
     def _nuisance_est(self, smpls, n_jobs_cv, return_models=False):
         if self.partialX & (not self.partialZ):
             psi_elements, preds = self._nuisance_est_partial_x(smpls, n_jobs_cv, return_models)
@@ -523,41 +512,6 @@ def _nuisance_est_partial_xz(self, smpls, n_jobs_cv, return_models=False):
 
         return psi_elements, preds
 
-    # To be removed in version 0.6.0
-    def tune(self,
-             param_grids,
-             tune_on_folds=False,
-             scoring_methods=None,  # if None the estimator's score method is used
-             n_folds_tune=5,
-             search_mode='grid_search',
-             n_iter_randomized_search=100,
-             n_jobs_cv=None,
-             set_as_params=True,
-             return_tune_res=False):
-
-        if isinstance(self.score, str) and (self.score == 'partialling out') and (param_grids is not None) and \
-                ('ml_g' in param_grids) and ('ml_l' not in param_grids):
-            warnings.warn(("Learner ml_g was renamed to ml_l. "
-                           "Please adapt the key of param_grids accordingly. "
-                           "The provided param_grids for ml_g are set for ml_l. "
-                           "The redirection will be removed in a future version."),
-                          DeprecationWarning, stacklevel=2)
-            param_grids['ml_l'] = param_grids.pop('ml_g')
-
-        if isinstance(self.score, str) and (self.score == 'partialling out') and (scoring_methods is not None) and \
-                ('ml_g' in scoring_methods) and ('ml_l' not in scoring_methods):
-            warnings.warn(("Learner ml_g was renamed to ml_l. "
-                           "Please adapt the key of scoring_methods accordingly. "
-                           "The provided scoring_methods for ml_g are set for ml_l. "
-                           "The redirection will be removed in a future version."),
-                          DeprecationWarning, stacklevel=2)
-            scoring_methods['ml_l'] = scoring_methods.pop('ml_g')
-
-        tune_res = super(DoubleMLPLIV, self).tune(param_grids, tune_on_folds, scoring_methods, n_folds_tune,
-                                                  search_mode, n_iter_randomized_search, n_jobs_cv, set_as_params,
-                                                  return_tune_res)
-        return tune_res
-
     def _nuisance_tuning_partial_x(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
                                    search_mode, n_iter_randomized_search):
         x, y = check_X_y(self._dml_data.x, self._dml_data.y,

doubleml/double_ml_plr.py

@@ -163,17 +163,6 @@ def _check_data(self, obj_dml_data):
                              'To fit a partially linear IV regression model use DoubleMLPLIV instead of DoubleMLPLR.')
         return
 
-    # To be removed in version 0.6.0
-    def set_ml_nuisance_params(self, learner, treat_var, params):
-        if isinstance(self.score, str) & (self.score == 'partialling out') & (learner == 'ml_g'):
-            warnings.warn(("Learner ml_g was renamed to ml_l. "
-                           "Please adapt the argument learner accordingly. "
-                           "The provided parameters are set for ml_l. "
-                           "The redirection will be removed in a future version."),
-                          DeprecationWarning, stacklevel=2)
-            learner = 'ml_l'
-        super(DoubleMLPLR, self).set_ml_nuisance_params(learner, treat_var, params)
-
     def _nuisance_est(self, smpls, n_jobs_cv, return_models=False):
         x, y = check_X_y(self._dml_data.x, self._dml_data.y,
                          force_all_finite=False)
@@ -252,40 +241,6 @@ def _score_elements(self, y, d, l_hat, m_hat, g_hat, smpls):
 
         return psi_a, psi_b
 
-    # To be removed in version 0.6.0
-    def tune(self,
-             param_grids,
-             tune_on_folds=False,
-             scoring_methods=None,  # if None the estimator's score method is used
-             n_folds_tune=5,
-             search_mode='grid_search',
-             n_iter_randomized_search=100,
-             n_jobs_cv=None,
-             set_as_params=True,
-             return_tune_res=False):
-
-        if isinstance(self.score, str) and (self.score == 'partialling out') and (param_grids is not None) and \
-                ('ml_g' in param_grids) and ('ml_l' not in param_grids):
-            warnings.warn(("Learner ml_g was renamed to ml_l. "
-                           "Please adapt the key of param_grids accordingly. "
-                           "The provided param_grids for ml_g are set for ml_l. "
-                           "The redirection will be removed in a future version."),
-                          DeprecationWarning, stacklevel=2)
-            param_grids['ml_l'] = param_grids.pop('ml_g')
-
-        if isinstance(self.score, str) and (self.score == 'partialling out') and (scoring_methods is not None) and \
-                ('ml_g' in scoring_methods) and ('ml_l' not in scoring_methods):
-            warnings.warn(("Learner ml_g was renamed to ml_l. "
-                           "Please adapt the key of scoring_methods accordingly. "
-                           "The provided scoring_methods for ml_g are set for ml_l. "
-                           "The redirection will be removed in a future version."),
-                          DeprecationWarning, stacklevel=2)
-            scoring_methods['ml_l'] = scoring_methods.pop('ml_g')
-
-        tune_res = super(DoubleMLPLR, self).tune(param_grids, tune_on_folds, scoring_methods, n_folds_tune, search_mode,
-                                                 n_iter_randomized_search, n_jobs_cv, set_as_params, return_tune_res)
-        return tune_res
-
     def _nuisance_tuning(self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv,
                          search_mode, n_iter_randomized_search):
         x, y = check_X_y(self._dml_data.x, self._dml_data.y,

doubleml/tests/test_doubleml_exceptions.py

@@ -726,29 +726,6 @@ def test_doubleml_exception_tune():
         dml_plr_iv_type.tune({'ml_g': {'alpha': [0.05, 0.5]},
                               'ml_m': {'alpha': [0.05, 0.5]}})
 
-    msg = 'Learner ml_g was renamed to ml_l. '
-    with pytest.warns(DeprecationWarning, match=msg):
-        dml_plr.tune({'ml_g': {'alpha': [0.05, 0.5]},
-                      'ml_m': {'alpha': [0.05, 0.5]}})
-    with pytest.warns(DeprecationWarning, match=msg):
-        dml_plr.tune({'ml_l': {'alpha': [0.05, 0.5]},
-                      'ml_m': {'alpha': [0.05, 0.5]}},
-                     scoring_methods={'ml_g': 'explained_variance',
-                                      'ml_m': 'explained_variance'})
-
-    msg = 'Learner ml_g was renamed to ml_l. '
-    with pytest.warns(DeprecationWarning, match=msg):
-        dml_pliv.tune({'ml_g': {'alpha': [0.05, 0.5]},
-                       'ml_m': {'alpha': [0.05, 0.5]},
-                       'ml_r': {'alpha': [0.05, 0.5]}})
-    with pytest.warns(DeprecationWarning, match=msg):
-        dml_pliv.tune({'ml_l': {'alpha': [0.05, 0.5]},
-                       'ml_m': {'alpha': [0.05, 0.5]},
-                       'ml_r': {'alpha': [0.05, 0.5]}},
-                      scoring_methods={'ml_g': 'explained_variance',
-                                       'ml_m': 'explained_variance',
-                                       'ml_r': 'explained_variance'})
-
     param_grids = {'ml_l': {'alpha': [0.05, 0.5]}, 'ml_m': {'alpha': [0.05, 0.5]}}
     msg = ('Invalid scoring_methods neg_mean_absolute_error. '
            'scoring_methods must be a dictionary. '
@@ -939,14 +916,6 @@ def test_doubleml_exception_learner():
         dml_iivm_hidden_classifier.set_ml_nuisance_params('ml_g0', 'd', {'max_iter': 314})
         dml_iivm_hidden_classifier.fit()
 
-    msg = 'Learner ml_g was renamed to ml_l. '
-    with pytest.warns(DeprecationWarning, match=msg):
-        dml_plr.set_ml_nuisance_params('ml_g', 'd', {'max_iter': 314})
-
-    msg = 'Learner ml_g was renamed to ml_l. '
-    with pytest.warns(DeprecationWarning, match=msg):
-        dml_pliv.set_ml_nuisance_params('ml_g', 'd', {'max_iter': 314})
-
 
 @pytest.mark.ci
 @pytest.mark.filterwarnings("ignore:Learner provided for")