refactor sbi tests.

tests/algorithms/sbi/test_sbi_run_methods.py

@@ -0,0 +1,45 @@
+import pytest
+
+import sbibm
+from sbibm.algorithms.sbi import mcabc, smcabc, snle, snpe, snre
+
+
+# a fast test
+@pytest.mark.parametrize(
+    "run_method", (mcabc, smcabc, snle, snpe, snre)
+)
+@pytest.mark.parametrize("task_name", ("gaussian_linear",))
+@pytest.mark.parametrize("num_observation", (1, 3))
+def test_sbi_api(
+    run_method, task_name, num_observation, num_simulations=2_000, num_samples=100
+):
+    task = sbibm.get_task(task_name)
+
+    if run_method in (mcabc, smcabc):  # abc algorithms
+        kwargs = dict()
+    else:  # neural algorithms
+        kwargs = dict(
+            num_rounds=1,
+            max_num_epochs=2,
+            neural_net="mlp" if run_method == snre else "mdn",
+        )
+    if run_method in (snle, snre):
+        kwargs["mcmc_parameters"] = dict(
+            num_chains=100, warmup_steps=10, thin=1, init_strategy="proposal"
+        )
+
+    predicted, _, _ = run_method(
+        task=task,
+        num_observation=num_observation,
+        num_simulations=num_simulations,
+        num_samples=num_samples,
+        **kwargs,
+    )
+
+    reference_samples = task.get_reference_posterior_samples(
+        num_observation=num_observation
+    )
+
+    expected = reference_samples[:num_samples, :]
+
+    assert expected.shape == predicted.shape