update trimming for external predictions

Author: SvenKlaassen

doubleml/irm/cvar.py

@@ -1,5 +1,4 @@
 import numpy as np
-import copy
 from sklearn.base import clone
 from sklearn.utils import check_X_y
 from sklearn.model_selection import StratifiedKFold, train_test_split
@@ -214,7 +213,10 @@ def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=Fa
                  'targets': np.full(shape=self._dml_data.n_obs, fill_value=np.nan),
                  'preds': np.full(shape=self._dml_data.n_obs, fill_value=np.nan)
                  }
-        m_hat = copy.deepcopy(g_hat)
+        m_hat = {'models': None,
+                 'targets': np.full(shape=self._dml_data.n_obs, fill_value=np.nan),
+                 'preds': np.full(shape=self._dml_data.n_obs, fill_value=np.nan)
+                 }
 
         # initialize models
         fitted_models = {}

doubleml/irm/iivm.py

@@ -320,7 +320,8 @@ def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=Fa
                                     return_models=return_models)
             _check_finite_predictions(m_hat['preds'], self._learner['ml_m'], 'ml_m', smpls)
             _check_is_propensity(m_hat['preds'], self._learner['ml_m'], 'ml_m', smpls, eps=1e-12)
-            m_hat['preds'] = _trimm(m_hat['preds'], self.trimming_rule, self.trimming_threshold)
+        # also trimm external predictions
+        m_hat['preds'] = _trimm(m_hat['preds'], self.trimming_rule, self.trimming_threshold)
 
         # nuisance r
         r0 = external_predictions['ml_r0'] is not None

doubleml/irm/irm.py

@@ -315,7 +315,8 @@ def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=Fa
                                     return_models=return_models)
             _check_finite_predictions(m_hat['preds'], self._learner['ml_m'], 'ml_m', smpls)
             _check_is_propensity(m_hat['preds'], self._learner['ml_m'], 'ml_m', smpls, eps=1e-12)
-            m_hat['preds'] = _trimm(m_hat['preds'], self.trimming_rule, self.trimming_threshold)
+        # also trimm external predictions
+        m_hat['preds'] = _trimm(m_hat['preds'], self.trimming_rule, self.trimming_threshold)
 
         psi_a, psi_b = self._score_elements(y, d,
                                             g_hat0['preds'], g_hat1['preds'], m_hat['preds'],