TST ensure return type of radius_neighbors is object

jnothman · jnothman · commit dd5c2e71f900 · 2015-03-04T07:46:38.000+11:00
and Pep8
diff --git a/sklearn/neighbors/tests/test_approximate.py b/sklearn/neighbors/tests/test_approximate.py
@@ -170,8 +170,9 @@ def test_radius_neighbors():
         mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
         neighbors = lshf.radius_neighbors(query, radius=mean_dist,
                                           return_distance=False)
-        # At least one neighbor should be returned.
-        assert_greater(neighbors.shape[0], 0)
+        assert_equal(neighbors.shape, (1,))
+        assert_equal(neighbors.dtype, object)
+        assert_greater(neighbors[0].shape[0], 0)
         # All distances should be less than mean_dist
         distances, neighbors = lshf.radius_neighbors(query,
                                                      radius=mean_dist,
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
@@ -41,6 +41,7 @@
 neighbors.radius_neighbors_graph = ignore_warnings(
     neighbors.radius_neighbors_graph)
 
+
 def _weight_func(dist):
     """ Weight function to replace lambda d: d ** -2.
     The lambda function is not valid because:
@@ -351,7 +352,11 @@ def test_neighbors_regressors_zero_distance():
 
 
 def test_radius_neighbors_boundary_handling():
-    """Test whether points lying on boundary are handled consistently"""
+    """Test whether points lying on boundary are handled consistently
+
+    Also ensures that even with only one query point, an object array
+    is returned rather than a 2d array.
+    """
 
     X = np.array([[1.5], [3.0], [3.01]])
     radius = 3.0
@@ -941,7 +946,8 @@ def test_non_euclidean_kneighbors():
         nbrs_graph = neighbors.radius_neighbors_graph(
             X, radius, metric=metric).toarray()
         nbrs1 = neighbors.NearestNeighbors(metric=metric, radius=radius).fit(X)
-        assert_array_equal(nbrs_graph, nbrs1.radius_neighbors_graph(X).toarray())
+        assert_array_equal(nbrs_graph,
+                           nbrs1.radius_neighbors_graph(X).toarray())
 
     # Raise error when wrong parameters are supplied,
     X_nbrs = neighbors.NearestNeighbors(3, metric='manhattan')
@@ -1042,7 +1048,8 @@ def test_k_and_radius_neighbors_duplicates():
         rng = nn.radius_neighbors_graph([[0], [1]], radius=1.5)
         assert_array_equal(rng.A, np.ones((2, 2)))
 
-        rng = nn.radius_neighbors_graph([[0], [1]], radius=1.5, mode='distance')
+        rng = nn.radius_neighbors_graph([[0], [1]], radius=1.5,
+                                        mode='distance')
         assert_array_equal(rng.A, [[0, 1], [1, 0]])
         assert_array_equal(rng.indices, [0, 1, 0, 1])
         assert_array_equal(rng.data, [0, 1, 1, 0])
