remove test_sampling_consistency

Author: ColtAllen

tests/distributions/test_discrete.py

@@ -330,93 +330,6 @@ def test_logp_basic(self):
         with pytest.raises(TypeError):
             logp_fn(np.array([1.5]))  # Value must be integer
 
-    def test_sampling_consistency(self):
-        """Test that sampling from the distribution produces reasonable results"""
-        r = 2.0
-        alpha = 1.0
-        time_covariate_vector = [0.0, 1.0, 2.0]
-
-        try:
-            dist = GrassiaIIGeometric.dist(
-                r=r, alpha=alpha, time_covariate_vector=time_covariate_vector
-            )
-
-            direct_samples = dist.eval()
-
-            if not isinstance(direct_samples, np.ndarray):
-                direct_samples = np.array([direct_samples])
-
-            if direct_samples.ndim == 0:
-                direct_samples = direct_samples.reshape(1)
-
-            assert (
-                direct_samples.size > 0
-            ), f"Direct sampling produced no samples for {time_covariate_vector}"
-            assert np.all(
-                direct_samples > 0
-            ), f"Direct sampling produced non-positive values for {time_covariate_vector}"
-            assert np.all(
-                direct_samples.astype(int) == direct_samples
-            ), f"Direct sampling produced non-integer values for {time_covariate_vector}"
-
-        except Exception as e:
-            import traceback
-
-            traceback.print_exc()
-            raise
-
-        try:
-            with pm.Model():
-                x = GrassiaIIGeometric(
-                    "x", r=r, alpha=alpha, time_covariate_vector=time_covariate_vector
-                )
-
-                trace = pm.sample(
-                    chains=1, draws=50, tune=0, random_seed=42, progressbar=False
-                ).posterior
-
-            # Extract samples and ensure correct shape
-            samples = trace["x"].values
-
-            assert (
-                samples is not None
-            ), f"No samples were returned from MCMC for {time_covariate_vector}"
-            assert (
-                samples.size > 0
-            ), f"MCMC sampling produced empty array for {time_covariate_vector}"
-
-            if samples.ndim > 1:
-                samples = samples.reshape(-1)  # Flatten if needed
-
-            # Check basic properties of samples
-            assert samples.size > 0, f"No samples after reshaping for {time_covariate_vector}"
-            assert np.all(
-                samples > 0
-            ), f"Found non-positive values in samples for {time_covariate_vector}"
-            assert np.all(
-                samples.astype(int) == samples
-            ), f"Found non-integer values in samples for {time_covariate_vector}"
-
-            # Check mean and variance are reasonable
-            mean = np.mean(samples)
-            var = np.var(samples)
-            assert (
-                0 < mean < np.inf
-            ), f"Mean {mean} is not in valid range for {time_covariate_vector}"
-            assert (
-                0 < var < np.inf
-            ), f"Variance {var} is not in valid range for {time_covariate_vector}"
-
-            # Additional checks for distribution properties
-            assert mean > 1, f"Mean {mean} is not greater than 1 for {time_covariate_vector}"
-            assert var > 0, f"Variance {var} is not positive for {time_covariate_vector}"
-
-        except Exception as e:
-            import traceback
-
-            traceback.print_exc()
-            raise
-
     @pytest.mark.parametrize(
         "r, alpha, time_covariate_vector, size, expected_shape",
         [