E5DA TST use global_random_seed in sklearn/gaussian_process/tests/test_gpc.py by OmarManzoor · Pull Request #24600 · scikit-learn/scikit-learn · GitHub
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TST use global_random_seed in sklearn/gaussian_process/tests/test_gpc.py #24600

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Merged
merged 6 commits into from
Mar 15, 2023
Merged

TST use global_random_seed in sklearn/gaussian_process/tests/test_gpc.py #24600

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d8fb9fe
TST use global_random_seed in sklearn/gaussian_process/tests/test_gpc.py
OmarManzoor Oct 7, 2022
6c607a1
Remove the % 10 conditions
OmarManzoor Nov 18, 2022
3df9bdd
Merge remote-tracking branch 'upstream/main' into global_random_seed_…
OmarManzoor Nov 18, 2022
67cb829
Merge remote-tracking branch 'upstream/main' into global_random_seed_…
OmarManzoor Feb 23, 2023
a9a4e26
Merge remote-tracking branch 'upstream/main' into pr/OmarManzoor/24600
jeremiedbb Mar 15, 2023
caa7030
[azure parallel] [all random seeds]
jeremiedbb Mar 15, 2023
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18 changes: 10 additions & 8 deletions sklearn/gaussian_process/tests/test_gpc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,11 +118,11 @@ def test_lml_gradient(kernel):
assert_almost_equal(lml_gradient, lml_gradient_approx, 3)


def test_random_starts():
def test_random_starts(global_random_seed):
# Test that an increasing number of random-starts of GP fitting only
# increases the log marginal likelihood of the chosen theta.
n_samples, n_features = 25, 2
rng = np.random.RandomState(0)
rng = np.random.RandomState(global_random_seed)
X = rng.randn(n_samples, n_features) * 2 - 1
y = (np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)) > 0

Expand All @@ -132,19 +132,21 @@ def test_random_starts():
last_lml = -np.inf
for n_restarts_optimizer in range(5):
gp = GaussianProcessClassifier(
kernel=kernel, n_restarts_optimizer=n_restarts_optimizer, random_state=0
kernel=kernel,
n_restarts_optimizer=n_restarts_optimizer,
random_state=global_random_seed,
).fit(X, y)
lml = gp.log_marginal_likelihood(gp.kernel_.theta)
assert lml > last_lml - np.finfo(np.float32).eps
last_lml = lml


@pytest.mark.parametrize("kernel", non_fixed_kernels)
def test_custom_optimizer(kernel):
def test_custom_optimizer(kernel, global_random_seed):
# Test that GPC can use externally defined optimizers.
# Define a dummy optimizer that simply tests 10 random hyperparameters
def optimizer(obj_func, initial_theta, bounds):
rng = np.random.RandomState(0)
rng = np.random.RandomState(global_random_seed)
theta_opt, func_min = initial_theta, obj_func(
initial_theta, eval_gradient=False
)
Expand All @@ -160,9 +162,9 @@ def optimizer(obj_func, initial_theta, bounds):
gpc = GaussianProcessClassifier(kernel=kernel, optimizer=optimizer)
gpc.fit(X, y_mc)
# Checks that optimizer improved marginal likelihood
assert gpc.log_marginal_likelihood(gpc.kernel_.theta) > gpc.log_marginal_likelihood(
kernel.theta
)
assert gpc.log_marginal_likelihood(
gpc.kernel_.theta
) >= gpc.log_marginal_likelihood(kernel.theta)


@pytest.mark.parametrize("kernel", kernels)
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