TST use global_random_seed in test_gpc.py (#24600)

OmarManzoor · jeremiedbb · web-flow · commit b397b8f2d952 · 2023-03-15T23:23:57.000+01:00
Co-authored-by: jeremiedbb &lt;jeremiedbb@yahoo.fr&gt;
diff --git a/sklearn/gaussian_process/tests/test_gpc.py b/sklearn/gaussian_process/tests/test_gpc.py
@@ -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
 
@@ -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
         )
@@ -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)
