Add NestedToMCMCAdapter to enable compatibility with ArviZ and MCMC workflows (#2391)

Author: SamuelSonoiki

arviz/data/io_numpyro.py

@@ -13,6 +13,101 @@
 _log = logging.getLogger(__name__)
 
 
+class NestedToMCMCAdapter:
+    """
+    Adapter to convert a NestedSampler object into an MCMC-compatible interface.
+
+    This class reshapes posterior samples from a NestedSampler into a chain-and-draw
+    structure expected by MCMC workflows, providing compatibility with downstream
+    tools like ArviZ for posterior analysis.
+
+    Parameters
+    ----------
+    nested_sampler : numpyro.contrib.nested_sampling.NestedSampler
+        The NestedSampler object containing posterior samples.
+    rng_key : jax.random.PRNGKey
+        The random key used for sampling.
+    num_samples : int
+        The total number of posterior samples to draw.
+    num_chains : int, optional
+        The number of artificial chains to create for MCMC compatibility (default is 1).
+    *args : tuple
+        Additional positional arguments required by the model (e.g., data, labels).
+    **kwargs : dict
+        Additional keyword arguments required by the model.
+
+    Attributes
+    ----------
+    samples : dict
+        Reshaped posterior samples organized by variable name.
+    thinning : int
+        Dummy thinning attribute for compatibility with MCMC.
+    sampler : NestedToMCMCAdapter
+        Mimics the sampler attribute of an MCMC object.
+    model : callable
+        The probabilistic model used in the NestedSampler.
+    _args : tuple
+        Positional arguments passed to the model.
+    _kwargs : dict
+        Keyword arguments passed to the model.
+
+    Methods
+    -------
+    get_samples(group_by_chain=True)
+        Returns posterior samples reshaped by chain or flattened if `group_by_chain` is False.
+    get_extra_fields(group_by_chain=True)
+        Provides dummy sampling statistics like accept probabilities, step sizes, and num_steps.
+    """
+
+    def __init__(self, nested_sampler, rng_key, num_samples, *args, num_chains=1, **kwargs):
+        self.nested_sampler = nested_sampler
+        self.rng_key = rng_key
+        self.num_samples = num_samples
+        self.num_chains = num_chains
+        self.samples = self._reshape_samples()
+        self.thinning = 1
+        self.sampler = self
+        self.model = nested_sampler.model
+        self._args = args
+        self._kwargs = kwargs
+
+    def _reshape_samples(self):
+        raw_samples = self.nested_sampler.get_samples(self.rng_key, self.num_samples)
+        samples_per_chain = self.num_samples // self.num_chains
+        return {
+            k: np.reshape(
+                v[: samples_per_chain * self.num_chains],
+                (self.num_chains, samples_per_chain, *v.shape[1:]),
+            )
+            for k, v in raw_samples.items()
+        }
+
+    def get_samples(self, group_by_chain=True):
+        if group_by_chain:
+            return self.samples
+        else:
+            # Flatten chains into a single dimension
+            return {k: v.reshape(-1, *v.shape[2:]) for k, v in self.samples.items()}
+
+    def get_extra_fields(self, group_by_chain=True):
+        # Generate dummy fields since NestedSampler does not produce these
+        n_chains = self.num_chains
+        n_samples = self.num_samples // self.num_chains
+
+        # Create dummy values for extra fields
+        extra_fields = {
+            "accept_prob": np.full((n_chains, n_samples), 1.0),  # Assume all proposals are accepted
+            "step_size": np.full((n_chains, n_samples), 0.1),  # Dummy step size
+            "num_steps": np.full((n_chains, n_samples), 10),  # Dummy number of steps
+        }
+
+        if not group_by_chain:
+            # Flatten the chains into a single dimension
+            extra_fields = {k: v.reshape(-1, *v.shape[2:]) for k, v in extra_fields.items()}
+
+        return extra_fields
+
+
 class NumPyroConverter:
     """Encapsulate NumPyro specific logic."""
 
@@ -37,6 +132,10 @@ def __init__(
         dims=None,
         pred_dims=None,
         num_chains=1,
+        rng_key=None,
+        num_samples=1000,
+        data=None,
+        labels=None,
     ):
         """Convert NumPyro data into an InferenceData object.
 
@@ -68,6 +167,15 @@ def __init__(
         import numpyro
 
         self.posterior = posterior
+        self.rng_key = rng_key
+        self.num_samples = num_samples
+
+        if isinstance(posterior, numpyro.contrib.nested_sampling.NestedSampler):
+            posterior = NestedToMCMCAdapter(
+                posterior, rng_key, num_samples, num_chains=num_chains, data=data, labels=labels
+            )
+            self.posterior = posterior
+
         self.prior = jax.device_get(prior)
         self.posterior_predictive = jax.device_get(posterior_predictive)
         self.predictions = predictions
@@ -340,6 +448,10 @@ def from_numpyro(
     dims=None,
     pred_dims=None,
     num_chains=1,
+    rng_key=None,
+    num_samples=1000,
+    data=None,
+    labels=None,
 ):
     """Convert NumPyro data into an InferenceData object.
 
@@ -383,4 +495,8 @@ def from_numpyro(
         dims=dims,
         pred_dims=pred_dims,
         num_chains=num_chains,
+        rng_key=rng_key,
+        num_samples=num_samples,
+        data=data,
+        labels=labels,
     ).to_inference_data()