Adapt to major PyMC changes

conda-envs/environment-test.yml

@@ -10,7 +10,7 @@ dependencies:
 - xhistogram
 - statsmodels
 - pip:
-  - pymc>=5.17.0  # CI was failing to resolve
+  - pymc>=5.19.1  # CI was failing to resolve
   - blackjax
   - scikit-learn
   - better_optimize>=0.0.10

conda-envs/windows-environment-test.yml

@@ -10,7 +10,7 @@ dependencies:
 - xhistogram
 - statsmodels
 - pip:
-  - pymc>=5.17.0  # CI was failing to resolve
+  - pymc>=5.19.1  # CI was failing to resolve
   - blackjax
   - scikit-learn
   - better_optimize>=0.0.10

pymc_experimental/distributions/timeseries.py

@@ -281,10 +281,20 @@ def discrete_mc_logp(op, values, P, steps, init_dist, state_rng, **kwargs):
 class DiscreteMarkovChainGibbsMetropolis(CategoricalGibbsMetropolis):
     name = "discrete_markov_chain_gibbs_metropolis"
 
-    def __init__(self, vars, proposal="uniform", order="random", model=None):
+    def __init__(
+        self,
+        vars,
+        proposal="uniform",
+        order="random",
+        model=None,
+        initial_point=None,
+        compile_kwargs: dict | None = None,
+        **kwargs,
+    ):
         model = pm.modelcontext(model)
         vars = get_value_vars_from_user_vars(vars, model)
-        initial_point = model.initial_point()
+        if initial_point is None:
+            initial_point = model.initial_point()
 
         dimcats = []
         # The above variable is a list of pairs (aggregate dimension, number
@@ -332,7 +342,9 @@ def __init__(self, vars, proposal="uniform", order="random", model=None):
         self.tune = True
 
         # We bypass CategoryGibbsMetropolis's __init__ to avoid it's specialiazed initialization logic
-        ArrayStep.__init__(self, vars, [model.compile_logp()])
+        if compile_kwargs is None:
+            compile_kwargs = {}
+        ArrayStep.__init__(self, vars, [model.compile_logp(**compile_kwargs)], **kwargs)
 
     @staticmethod
     def competence(var):

pymc_experimental/inference/laplace.py

@@ -391,14 +391,14 @@ def sample_laplace_posterior(
 
     else:
         info = mu.point_map_info
-        flat_shapes = [np.prod(shape).astype(int) for _, shape, _ in info]
+        flat_shapes = [size for _, _, size, _ in info]
         slices = [
             slice(sum(flat_shapes[:i]), sum(flat_shapes[: i + 1])) for i in range(len(flat_shapes))
         ]
 
         posterior_draws = [
             posterior_draws[..., idx].reshape((chains, draws, *shape)).astype(dtype)
-            for idx, (name, shape, dtype) in zip(slices, info)
+            for idx, (name, shape, _, dtype) in zip(slices, info)
         ]
 
     idata = laplace_draws_to_inferencedata(posterior_draws, model)

pymc_experimental/inference/smc/sampling.py

@@ -206,13 +206,16 @@ def arviz_from_particles(model, particles):
     -------
     """
     n_particles = jax.tree_util.tree_flatten(particles)[0][0].shape[0]
-    by_varname = {k.name: v.squeeze()[np.newaxis, :] for k, v in zip(model.value_vars, particles)}
+    by_varname = {
+        k.name: v.squeeze()[np.newaxis, :].astype(k.dtype)
+        for k, v in zip(model.value_vars, particles)
+    }
     varnames = [v.name for v in model.value_vars]
     with model:
         strace = NDArray(name=model.name)
         strace.setup(n_particles, 0)
     for particle_index in range(0, n_particles):
-        strace.record(point={k: by_varname[k][0][particle_index] for k in varnames})
+        strace.record(point={k: np.asarray(by_varname[k][0][particle_index]) for k in varnames})
         multitrace = MultiTrace((strace,))
     return to_inference_data(multitrace, log_likelihood=False)
 

pymc_experimental/version.txt

@@ -1 +1 @@
-0.1.3
+0.1.4

requirements.txt

@@ -1,2 +1,2 @@
-pymc>=5.17.0
+pymc>=5.19.1
 scikit-learn

tests/distributions/test_discrete_markov_chain.py

@@ -225,16 +225,18 @@ def test_change_size_univariate(self):
     def test_mcmc_sampling(self):
         with pm.Model(coords={"step": range(100)}) as model:
             init_dist = Categorical.dist(p=[0.5, 0.5])
-            DiscreteMarkovChain(
+            markov_chain = DiscreteMarkovChain(
                 "markov_chain",
                 P=[[0.1, 0.9], [0.1, 0.9]],
                 init_dist=init_dist,
                 shape=(100,),
                 dims="step",
             )
 
-            step_method = assign_step_methods(model)
-            assert isinstance(step_method, DiscreteMarkovChainGibbsMetropolis)
+            _, assigned_step_methods = assign_step_methods(model)
+            assert assigned_step_methods[DiscreteMarkovChainGibbsMetropolis] == [
+                model.rvs_to_values[markov_chain]
+            ]
 
             # Sampler needs no tuning
             idata = pm.sample(