test CI: old tests

tests/mcmc/test_sampling.py

@@ -122,7 +122,6 @@ 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,
@@ -148,7 +147,7 @@ def run_mclmc(
 
         return samples
 
-    def run_adjusted_mclmc_dynamic(
+    def run_adjusted_mclmc(
         self,
         logdensity_fn,
         num_steps,
@@ -179,12 +178,11 @@ def run_adjusted_mclmc_dynamic(
             logdensity_fn=logdensity_fn,
         )
 
-        target_acc_rate = 0.9
+        target_acc_rate = 0.65
 
         (
             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,
@@ -221,74 +219,6 @@ 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
@@ -405,38 +335,7 @@ 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)
 
-    @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)
-        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_dynamic(
-            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:]
-        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):
+    def test_adjusted_mclmc(self):
         """Test the MCLMC kernel."""
 
         init_key0, init_key1, inference_key = jax.random.split(self.key, 3)
@@ -453,7 +352,6 @@ def test_adjusted_mclmc(self, diagonal_preconditioning):
             logdensity_fn=logdensity_fn,
             key=inference_key,
             num_steps=10000,
-            diagonal_preconditioning=diagonal_preconditioning,
         )
 
         coefs_samples = states["coefs"][3000:]
@@ -519,7 +417,10 @@ 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,