test CI: add static tests

tests/mcmc/test_sampling.py

@@ -122,6 +122,7 @@ def run_mclmc(
         (
             blackjax_state_after_tuning,
             blackjax_mclmc_sampler_params,
+            num_tuning_integrator_steps,
         ) = blackjax.mclmc_find_L_and_step_size(
             mclmc_kernel=kernel,
             num_steps=num_steps,
@@ -178,11 +179,12 @@ def run_adjusted_mclmc_dynamic(
             logdensity_fn=logdensity_fn,
         )
 
-        target_acc_rate = 0.65
+        target_acc_rate = 0.9
 
         (
             blackjax_state_after_tuning,
             blackjax_mclmc_sampler_params,
+            num_tuning_integrator_steps,
         ) = blackjax.adjusted_mclmc_find_L_and_step_size(
             mclmc_kernel=kernel,
             num_steps=num_steps,
@@ -219,6 +221,74 @@ def run_adjusted_mclmc_dynamic(
 
         return out
 
+    def run_adjusted_mclmc(
+        self,
+        logdensity_fn,
+        num_steps,
+        initial_position,
+        key,
+        diagonal_preconditioning=False,
+    ):
+        integrator = isokinetic_mclachlan
+
+        init_key, tune_key, run_key = jax.random.split(key, 3)
+
+        initial_state = blackjax.mcmc.adjusted_mclmc.init(
+            position=initial_position,
+            logdensity_fn=logdensity_fn,
+        )
+
+        kernel = lambda rng_key, state, avg_num_integration_steps, step_size, inverse_mass_matrix: blackjax.mcmc.adjusted_mclmc.build_kernel(
+            integrator=integrator,
+            inverse_mass_matrix=inverse_mass_matrix,
+            logdensity_fn=logdensity_fn,
+        )(
+            rng_key=rng_key,
+            state=state,
+            step_size=step_size,
+            num_integration_steps=avg_num_integration_steps,
+        )
+
+        target_acc_rate = 0.9
+
+        (
+            blackjax_state_after_tuning,
+            blackjax_mclmc_sampler_params,
+            num_tuning_integrator_steps,
+        ) = blackjax.adjusted_mclmc_find_L_and_step_size(
+            mclmc_kernel=kernel,
+            num_steps=num_steps,
+            state=initial_state,
+            rng_key=tune_key,
+            target=target_acc_rate,
+            frac_tune1=0.1,
+            frac_tune2=0.1,
+            frac_tune3=0.1,
+            diagonal_preconditioning=diagonal_preconditioning,
+        )
+
+        step_size = blackjax_mclmc_sampler_params.step_size
+        L = blackjax_mclmc_sampler_params.L
+
+        alg = blackjax.adjusted_mclmc(
+            logdensity_fn=logdensity_fn,
+            step_size=step_size,
+            num_integration_steps=L / step_size,
+            integrator=integrator,
+            inverse_mass_matrix=blackjax_mclmc_sampler_params.inverse_mass_matrix,
+        )
+
+        _, out = run_inference_algorithm(
+            rng_key=run_key,
+            initial_state=blackjax_state_after_tuning,
+            inference_algorithm=alg,
+            num_steps=num_steps,
+            transform=lambda state, _: state.position,
+            progress_bar=False,
+        )
+
+        return out
+
     @parameterized.parameters(
         itertools.product(
             regression_test_cases, [True, False], window_adaptation_filters
@@ -335,7 +405,11 @@ def test_mclmc(self):
         np.testing.assert_allclose(np.mean(scale_samples), 1.0, rtol=1e-2, atol=1e-1)
         np.testing.assert_allclose(np.mean(coefs_samples), 3.0, rtol=1e-2, atol=1e-1)
 
-    def test_adjusted_mclmc(self):
+    @parameterized.parameters([True, False])
+    def test_adjusted_mclmc_dynamic(
+        self,
+        diagonal_preconditioning,
+    ):
         """Test the MCLMC kernel."""
 
         init_key0, init_key1, inference_key = jax.random.split(self.key, 3)
@@ -352,6 +426,34 @@ def test_adjusted_mclmc(self):
             logdensity_fn=logdensity_fn,
             key=inference_key,
             num_steps=10000,
+            diagonal_preconditioning=diagonal_preconditioning,
+        )
+
+        coefs_samples = states["coefs"][3000:]
+        scale_samples = np.exp(states["log_scale"][3000:])
+
+        np.testing.assert_allclose(np.mean(scale_samples), 1.0, atol=1e-2)
+        np.testing.assert_allclose(np.mean(coefs_samples), 3.0, atol=1e-2)
+
+    @parameterized.parameters([True, False])
+    def test_adjusted_mclmc(self, diagonal_preconditioning):
+        """Test the MCLMC kernel."""
+
+        init_key0, init_key1, inference_key = jax.random.split(self.key, 3)
+        x_data = jax.random.normal(init_key0, shape=(1000, 1))
+        y_data = 3 * x_data + jax.random.normal(init_key1, shape=x_data.shape)
+
+        logposterior_fn_ = functools.partial(
+            self.regression_logprob, x=x_data, preds=y_data
+        )
+        logdensity_fn = lambda x: logposterior_fn_(**x)
+
+        states = self.run_adjusted_mclmc(
+            initial_position={"coefs": 1.0, "log_scale": 1.0},
+            logdensity_fn=logdensity_fn,
+            key=inference_key,
+            num_steps=10000,
+            diagonal_preconditioning=diagonal_preconditioning,
         )
 
         coefs_samples = states["coefs"][3000:]
@@ -417,10 +519,7 @@ def get_inverse_mass_matrix():
                 inverse_mass_matrix=inverse_mass_matrix,
             )
 
-            (
-                _,
-                blackjax_mclmc_sampler_params,
-            ) = blackjax.mclmc_find_L_and_step_size(
+            (_, blackjax_mclmc_sampler_params, _) = blackjax.mclmc_find_L_and_step_size(
                 mclmc_kernel=kernel,
                 num_steps=num_steps,
                 state=initial_state,