TST add a non-regression test for parallel backends in neighbors

tomMoral · tomMoral · commit bbb6309f50ed · 2018-09-26T17:05:37.000+02:00
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
@@ -27,6 +27,8 @@
 from sklearn.utils.testing import ignore_warnings
 from sklearn.utils.validation import check_random_state
 
+from sklearn.externals.joblib import parallel_backend
+
 rng = np.random.RandomState(0)
 # load and shuffle iris dataset
 iris = datasets.load_iris()
@@ -1315,6 +1317,24 @@ def test_same_radius_neighbors_parallel(algorithm):
         assert_array_equal(ind[i], ind_parallel[i])
     assert_array_almost_equal(graph, graph_parallel)
 
+@pytest.mark.parametrize('backend', ['loky', 'multiprocessing', 'threading'])
+@pytest.mark.parametrize('algorithm', ALGORITHMS)
+def test_knn_forcing_backend(backend, algorithm):
+    # Non-regression test which ensure the knn is properly working
+    # even when forcing the global joblib backend
+    with parallel_backend(backend):
+        X, y = datasets.make_classification(n_samples=30, n_features=5,
+                                            n_redundant=0, random_state=0)
+        X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+        clf = neighbors.KNeighborsClassifier(n_neighbors=3,
+                                             algorithm=algorithm,
+                                             n_jobs=3)
+        clf.fit(X_train, y_train)
+        y = clf.predict(X_test)
+        dist, ind = clf.kneighbors(X_test)
+        graph = clf.kneighbors_graph(X_test, mode='distance').toarray()
+
 
 def test_dtype_convert():
     classifier = neighbors.KNeighborsClassifier(n_neighbors=1)
