remove old sklearn version of _fit_and_predict

doubleml/tests/_utils_dml_cv_predict.py

@@ -8,21 +8,6 @@
 from sklearn.preprocessing import LabelEncoder
 from sklearn.model_selection._validation import _fit_and_predict, _check_is_permutation
 
-# Adapt _fit_and_predict for earlier sklearn versions
-from distutils.version import LooseVersion
-from sklearn import __version__ as sklearn_version
-
-if LooseVersion(sklearn_version) < LooseVersion("1.4.0"):
-    def _fit_and_predict_adapted(estimator, x, y, train, test, fit_params, method):
-        res = _fit_and_predict(estimator, x, y, train, test,
-                               verbose=0,
-                               fit_params=fit_params,
-                               method=method)
-        return res
-else:
-    def _fit_and_predict_adapted(estimator, x, y, train, test, fit_params, method):
-        return _fit_and_predict(estimator, x, y, train, test, fit_params, method)
-
 
 def _dml_cv_predict_ut_version(estimator, x, y, smpls=None,
                                n_jobs=None, est_params=None, method='predict'):
@@ -42,12 +27,12 @@ def _dml_cv_predict_ut_version(estimator, x, y, smpls=None,
         else:
             predictions = np.full(len(y), np.nan)
         if est_params is None:
-            xx = _fit_and_predict_adapted(
+            xx = _fit_and_predict(
                 clone(estimator),
                 x, y, train_index, test_index, fit_params, method)
         else:
             assert isinstance(est_params, dict)
-            xx = _fit_and_predict_adapted(
+            xx = _fit_and_predict(
                 clone(estimator).set_params(**est_params),
                 x, y, train_index, test_index, fit_params, method)
 
@@ -77,20 +62,20 @@ def _dml_cv_predict_ut_version(estimator, x, y, smpls=None,
                         pre_dispatch=pre_dispatch)
     # FixMe: Find a better way to handle the different combinations of paramters and smpls_is_partition
     if est_params is None:
-        prediction_blocks = parallel(delayed(_fit_and_predict_adapted)(
+        prediction_blocks = parallel(delayed(_fit_and_predict)(
             estimator,
             x, y, train_index, test_index, fit_params, method)
                                      for idx, (train_index, test_index) in enumerate(smpls))
     elif isinstance(est_params, dict):
         # if no fold-specific parameters we redirect to the standard method
         # warnings.warn("Using the same (hyper-)parameters for all folds")
-        prediction_blocks = parallel(delayed(_fit_and_predict_adapted)(
+        prediction_blocks = parallel(delayed(_fit_and_predict)(
             clone(estimator).set_params(**est_params),
             x, y, train_index, test_index, fit_params, method)
                                      for idx, (train_index, test_index) in enumerate(smpls))
     else:
         assert len(est_params) == len(smpls), 'provide one parameter setting per fold'
-        prediction_blocks = parallel(delayed(_fit_and_predict_adapted)(
+        prediction_blocks = parallel(delayed(_fit_and_predict)(
             clone(estimator).set_params(**est_params[idx]),
             x, y, train_index, test_index, fit_params, method)
             for idx, (train_index, test_index) in enumerate(smpls))