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[MRG] TST use global_random_seed in sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py #23559

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Merged
merged 7 commits into from
Sep 25, 2022
Merged

[MRG] TST use global_random_seed in sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py #23559

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10e396b
Add global_random_seed to test_multinomial_deviance
haochunchang Jun 7, 2022
9bda914
Add global_random_seed to test_init_raw_predictions_shapes
haochunchang Jun 7, 2022
b1d46fe
Add global_random_seed to test_init_raw_predictions_values
haochunchang Jun 7, 2022
15519e9
Change seed of test_lad_equals_quantiles to 0-99
haochunchang Jun 7, 2022
da14ad8
Use global_random_seed in ensemble/test_gradient_boosting_loss_functi…
haochunchang Jul 1, 2022
c4ca78e
Merge remote-tracking branch 'upstream/main' into pr/haochunchang/23559
jeremiedbb Sep 25, 2022
dbba3ae
check [all random seeds]
jeremiedbb Sep 25, 2022
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13 changes: 6 additions & 7 deletions sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -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))

Expand Down