MNT fix MacOS failure with more stable estimator_checks (#18667)

* More stable estimator_checks * Revert back to default check_estimators_dtypes * delayed wrapper does not expose check_pickle kwarg
scikit-learn · Oct 22, 2020 · 8d10285 · 8d10285
1 parent d933c20
commit 8d10285
Show file tree

Hide file tree

Showing 2 changed files with 3 additions and 8 deletions.
diff --git a/sklearn/neighbors/_base.py b/sklearn/neighbors/_base.py
@@ -713,13 +713,11 @@ class from an array representing our data set and ask who's
                     parse_version(joblib.__version__) < parse_version('0.12'))
             if old_joblib:
                 # Deal with change of API in joblib
-                delayed_query = delayed(_tree_query_parallel_helper)
                 parallel_kwargs = {"backend": "threading"}
             else:
-                delayed_query = delayed(_tree_query_parallel_helper)
                 parallel_kwargs = {"prefer": "threads"}
             chunked_results = Parallel(n_jobs, **parallel_kwargs)(
-                delayed_query(
+                delayed(_tree_query_parallel_helper)(
                     self._tree, X[s], n_neighbors, return_distance)
                 for s in gen_even_slices(X.shape[0], n_jobs)
             )
@@ -1038,13 +1036,11 @@ class from an array representing our data set and ask who's
                     "or set algorithm='brute'" % self._fit_method)
 
             n_jobs = effective_n_jobs(self.n_jobs)
+            delayed_query = delayed(_tree_query_radius_parallel_helper)
             if parse_version(joblib.__version__) < parse_version('0.12'):
                 # Deal with change of API in joblib
-                delayed_query = delayed(_tree_query_radius_parallel_helper,
-                                        check_pickle=False)
                 parallel_kwargs = {"backend": "threading"}
             else:
-                delayed_query = delayed(_tree_query_radius_parallel_helper)
                 parallel_kwargs = {"prefer": "threads"}
 
             chunked_results = Parallel(n_jobs, **parallel_kwargs)(

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -26,7 +26,6 @@
 from ._testing import raises
 from . import is_scalar_nan
 
-from ..discriminant_analysis import LinearDiscriminantAnalysis
 from ..linear_model import LogisticRegression
 from ..linear_model import Ridge
 
@@ -346,7 +345,7 @@ def _construct_instance(Estimator):
             if issubclass(Estimator, RegressorMixin):
                 estimator = Estimator(Ridge())
             else:
-                estimator = Estimator(LinearDiscriminantAnalysis())
+                estimator = Estimator(LogisticRegression(C=1))
         elif required_parameters in (['estimators'],):
             # Heterogeneous ensemble classes (i.e. stacking, voting)
             if issubclass(Estimator, RegressorMixin):