pymc-devs · zaxtax · Apr 16, 2025 · Apr 1, 2025 · ricardoV94 · Apr 9, 2025
diff --git a/docs/api_reference.rst b/docs/api_reference.rst
@@ -23,7 +23,10 @@ Inference
 .. autosummary::
    :toctree: generated/
 
+   find_MAP
    fit
+   fit_laplace
+   fit_pathfinder
 
 
 Distributions

@@ -15,9 +15,7 @@
 
 from pymc_extras import gp, statespace, utils
 from pymc_extras.distributions import *
-from pymc_extras.inference.find_map import find_MAP
-from pymc_extras.inference.fit import fit
-from pymc_extras.inference.laplace import fit_laplace
+from pymc_extras.inference import find_MAP, fit, fit_laplace, fit_pathfinder
 from pymc_extras.model.marginal.marginal_model import (
     MarginalModel,
     marginalize,

@@ -12,7 +12,9 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-
+from pymc_extras.inference.find_map import find_MAP
 from pymc_extras.inference.fit import fit
+from pymc_extras.inference.laplace import fit_laplace
+from pymc_extras.inference.pathfinder.pathfinder import fit_pathfinder
 
-__all__ = ["fit"]
+__all__ = ["fit", "fit_pathfinder", "fit_laplace", "find_MAP"]
@@ -16,19 +16,20 @@
 
 def fit(method: str, **kwargs) -> az.InferenceData:
     """
-    Fit a model with an inference algorithm
+    Fit a model with an inference algorithm.
+    See :func:`fit_pathfinder` and :func:`fit_laplace` for more details.
 
     Parameters
     ----------
     method : str
         Which inference method to run.
         Supported: pathfinder or laplace
 
-    kwargs are passed on.
+    kwargs: keyword arguments are passed on to the inference method.
 
     Returns
     -------
-    arviz.InferenceData
+    :class:`~arviz.InferenceData`
     """
     if method == "pathfinder":
         from pymc_extras.inference.pathfinder import fit_pathfinder

@@ -509,7 +509,7 @@ def fit_laplace(
 
     Returns
     -------
-    idata: az.InferenceData
+    :class:`~arviz.InferenceData`
         An InferenceData object containing the approximated posterior samples.
 
     Examples

@@ -12,6 +12,7 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+
 import collections
 import logging
 import time
@@ -24,7 +25,6 @@
 
 import arviz as az
 import filelock
-import jax
 import numpy as np
 import pymc as pm
 import pytensor
@@ -43,7 +43,6 @@
     find_rng_nodes,
     reseed_rngs,
 )
-from pymc.sampling.jax import get_jaxified_graph
 from pymc.util import (
     CustomProgress,
     RandomSeed,
@@ -64,6 +63,7 @@
 # TODO: change to typing.Self after Python versions greater than 3.10
 from typing_extensions import Self
 
+from pymc_extras.inference.laplace import add_data_to_inferencedata
 from pymc_extras.inference.pathfinder.importance_sampling import (
     importance_sampling as _importance_sampling,
 )
@@ -99,6 +99,8 @@ def get_jaxified_logp_of_ravel_inputs(model: Model, jacobian: bool = True) -> Ca
         A JAX function that computes the log-probability of a PyMC model with ravelled inputs.
     """
 
+    from pymc.sampling.jax import get_jaxified_graph
+
     # TODO: JAX: test if we should get jaxified graph of dlogp as well
     new_logprob, new_input = pm.pytensorf.join_nonshared_inputs(
         model.initial_point(), (model.logp(jacobian=jacobian),), model.value_vars, ()
@@ -218,6 +220,10 @@ def convert_flat_trace_to_idata(
             result = [res.reshape(num_paths, num_pdraws, *res.shape[2:]) for res in result]
 
     elif inference_backend == "blackjax":
+        import jax
+
+        from pymc.sampling.jax import get_jaxified_graph
+
         jax_fn = get_jaxified_graph(inputs=model.value_vars, outputs=vars_to_sample)
         result = jax.vmap(jax.vmap(jax_fn))(
             *jax.device_put(list(trace.values()), jax.devices(postprocessing_backend)[0])
@@ -1627,6 +1633,7 @@ def fit_pathfinder(
     inference_backend: Literal["pymc", "blackjax"] = "pymc",
     pathfinder_kwargs: dict = {},
     compile_kwargs: dict = {},
+    initvals: dict | None = None,
 ) -> az.InferenceData:
     """
     Fit the Pathfinder Variational Inference algorithm.
@@ -1662,12 +1669,12 @@ def fit_pathfinder(
     importance_sampling : str, None, optional
         Method to apply sampling based on log importance weights (logP - logQ).
         Options are:
-        "psis" : Pareto Smoothed Importance Sampling (default)
-                Recommended for more stable results.
-        "psir" : Pareto Smoothed Importance Resampling
-                Less stable than PSIS.
-        "identity" : Applies log importance weights directly without resampling.
-        None : No importance sampling weights. Returns raw samples of size (num_paths, num_draws_per_path, N) where N is number of model parameters. Other methods return samples of size (num_draws, N).
+
+        - "psis" : Pareto Smoothed Importance Sampling (default). Usually most stable.
+        - "psir" : Pareto Smoothed Importance Resampling. Less stable than PSIS.
+        - "identity" : Applies log importance weights directly without resampling.
+        - None : No importance sampling weights. Returns raw samples of size (num_paths, num_draws_per_path, N) where N is number of model parameters. Other methods return samples of size (num_draws, N).
+
     progressbar : bool, optional
         Whether to display a progress bar (default is True). Setting this to False will likely reduce the computation time.
     random_seed : RandomSeed, optional
@@ -1682,10 +1689,13 @@ def fit_pathfinder(
         Additional keyword arguments for the Pathfinder algorithm.
     compile_kwargs
         Additional keyword arguments for the PyTensor compiler. If not provided, the default linker is "cvm_nogc".
+    initvals: dict | None = None
+        Initial values for the model parameters, as str:ndarray key-value pairs. Paritial initialization is permitted.
+        If None, the model's default initial values are used.
 
     Returns
     -------
-    arviz.InferenceData
+    :class:`~arviz.InferenceData`
         The inference data containing the results of the Pathfinder algorithm.
 
     References
@@ -1695,6 +1705,14 @@ def fit_pathfinder(
 
     model = modelcontext(model)
 
+    if initvals is not None:
+        model = pm.model.fgraph.clone_model(model)  # Create a clone of the model
+        for (
+            rv_name,
+            ivals,
+        ) in initvals.items():  # Set the initial values for the variables in the clone
+            model.set_initval(model.named_vars[rv_name], ivals)
+
     valid_importance_sampling = {"psis", "psir", "identity", None}
 
     if importance_sampling is not None:
@@ -1734,6 +1752,7 @@ def fit_pathfinder(
         pathfinder_samples = mp_result.samples
     elif inference_backend == "blackjax":
         import blackjax
+        import jax
 
         if version.parse(blackjax.__version__).major < 1:
             raise ImportError("fit_pathfinder requires blackjax 1.0 or above")
@@ -1772,4 +1791,7 @@ def fit_pathfinder(
         model=model,
         importance_sampling=importance_sampling,
     )
+
+    idata = add_data_to_inferencedata(idata, progressbar, model, compile_kwargs)
+
     return idata
@@ -200,3 +200,15 @@ def test_pathfinder_importance_sampling(importance_sampling):
         assert idata.posterior["mu"].shape == (1, num_draws)
         assert idata.posterior["tau"].shape == (1, num_draws)
         assert idata.posterior["theta"].shape == (1, num_draws, 8)
+
+
+def test_pathfinder_initvals():
+    # Run a model with an ordered transform that will fail unless initvals are in place
+    with pm.Model() as mdl:
+        pm.Normal("ordered", size=10, transform=pm.distributions.transforms.ordered)
+        idata = pmx.fit_pathfinder(initvals={"ordered": np.linspace(0, 1, 10)})
+
+    # Check that the samples are ordered to make sure transform was applied
+    assert np.all(
+        idata.posterior["ordered"][..., 1:].values > idata.posterior["ordered"][..., :-1].values
+    )