extra test

tests/test_fit.py

@@ -97,6 +97,32 @@ def step(self, model: LinearModel, train_data: Dataset) -> float:
     assert trained_model.bias == 1.0
 
 
+@pytest.mark.parametrize("batch_size", [10, 100])
+def test_raises_if_try_to_batch_scipy_optim(batch_size: int) -> None:
+    # Create dataset:
+    key = jr.PRNGKey(123)
+    x = jnp.sort(jr.uniform(key=key, minval=-2.0, maxval=2.0, shape=(10, 1)), axis=0)
+    y = jnp.sin(x) + jr.normal(key=key, shape=x.shape) * 0.1
+    D = Dataset(X=x, y=y)
+
+    # Define GP model:
+    prior = Prior(kernel=RBF(), mean_function=Constant())
+    likelihood = Gaussian(num_datapoints=10)
+    posterior = prior * likelihood
+
+    # Define loss function:
+    mll = ConjugateMLL(negative=True)
+
+    with pytest.raises(ValueError):
+        fit(
+            model=posterior,
+            train_data=D,
+            solver=jaxopt.ScipyMinimize(fun=mll),
+            batch_size=batch_size,
+            key=jr.PRNGKey(123),
+        )
+
+
 @pytest.mark.parametrize("num_iters", [1, 5])
 @pytest.mark.parametrize("n_data", [1, 20])
 @pytest.mark.parametrize("verbose", [True, False])