TST use global_random_seed in sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py
@@ -154,9 +154,9 @@ def test_sample_weight_deviance():
@pytest.mark.parametrize("n_classes, n_samples", [(3, 100), (5, 57), (7, 13)])
-def test_multinomial_deviance(n_classes, n_samples):
+def test_multinomial_deviance(n_classes, n_samples, global_random_seed):
# Check multinomial deviance with and without sample weights.
- rng = np.random.RandomState(13)
+ rng = np.random.RandomState(global_random_seed)
sample_weight = np.ones(n_samples)
y_true = rng.randint(0, n_classes, size=n_samples)
y_pred = np.zeros((n_samples, n_classes), dtype=np.float64)
@@ -230,10 +230,10 @@ def test_init_raw_predictions_shapes():
assert raw_predictions.dtype == np.float64
-def test_init_raw_predictions_values():
+def test_init_raw_predictions_values(global_random_seed):
# Make sure the get_init_raw_predictions() returns the expected values for
# each loss.
- rng = np.random.RandomState(0)
+ rng = np.random.RandomState(global_random_seed)
n_samples = 100
X = rng.normal(size=(n_samples, 5))
@@ -294,15 +294,14 @@ def test_init_raw_predictions_values():
assert_allclose(raw_predictions[:, k], np.log(p))
-@pytest.mark.parametrize("seed", range(5))
@pytest.mark.parametrize("alpha", [0.4, 0.5, 0.6])
-def test_lad_equals_quantiles(seed, alpha):
+def test_lad_equals_quantiles(global_random_seed, alpha):
# Make sure quantile loss with alpha = .5 is equivalent to LAD
lad = LeastAbsoluteError()
ql = QuantileLossFunction(alpha=alpha)
n_samples = 50
- rng = np.random.RandomState(seed)
+ rng = np.random.RandomState(global_random_seed)
raw_predictions = rng.normal(size=(n_samples))
y_true = rng.normal(size=(n_samples))
