TST Make test_neighbors_metric robust to rng (scikit-learn#28888)

sklearn/neighbors/tests/test_neighbors.py

@@ -1644,8 +1644,16 @@ def test_nearest_neighbors_validate_params():
  + DISTANCE_METRIC_OBJS,
 )
 def test_neighbors_metrics(
- global_dtype, metric, n_samples=20, n_features=3, n_query_pts=2, n_neighbors=5
+ global_dtype,
+ global_random_seed,
+ metric,
+ n_samples=20,
+ n_features=3,
+ n_query_pts=2,
+ n_neighbors=5,
 ):
+ rng = np.random.RandomState(global_random_seed)
+
  metric = _parse_metric(metric, global_dtype)
 
  # Test computing the neighbors for various metrics
@@ -1697,15 +1705,19 @@ def test_neighbors_metrics(
  brute_dst, brute_idx = results["brute"]
  ball_tree_dst, ball_tree_idx = results["ball_tree"]
 
- assert_allclose(brute_dst, ball_tree_dst)
+ # The returned distances are always in float64 regardless of the input dtype
+ # We need to adjust the tolerance w.r.t the input dtype
+ rtol = 1e-7 if global_dtype == np.float64 else 1e-4
+
+ assert_allclose(brute_dst, ball_tree_dst, rtol=rtol)
  assert_array_equal(brute_idx, ball_tree_idx)
 
  if not exclude_kd_tree:
  kd_tree_dst, kd_tree_idx = results["kd_tree"]
- assert_allclose(brute_dst, kd_tree_dst)
+ assert_allclose(brute_dst, kd_tree_dst, rtol=rtol)
  assert_array_equal(brute_idx, kd_tree_idx)
 
- assert_allclose(ball_tree_dst, kd_tree_dst)
+ assert_allclose(ball_tree_dst, kd_tree_dst, rtol=rtol)
  assert_array_equal(ball_tree_idx, kd_tree_idx)