Added from_numpyro_svi converter (#95)

* added from_numpyro_svi support

updated typing for docstub

fixed test_inferred_dims_univariate

improved load_cached_models

fixed svi docstring and SVIWrapper

changed numpyro svi signature

fixed from_numpyro_svi docstring

added custom guide support and test

added test support for custom guides

* changed input for form_numpyro_svi from guide to SVI instance

* improved comments, imports

* added tests for SVIWrapper

* added NumPyro conversion guide

* stripped notebook output

* removed thinning, updated docstrings and refs

* updated sample_dims

* fix typo in io_numpyro docstring

Co-authored-by: Tomás Capretto <[email protected]>

* regenerate stub files after rebase

* updated conversion guide

* fixed issue in numpyro conversion guide with event dim labelling

---------

Co-authored-by: Tomás Capretto <[email protected]>

Author: kylejcaron

docs/source/how_to/ConversionGuideNumPyro.ipynb

@@ -67,6 +67,7 @@ Thus, to install all user facing optional dependencies you should use `arviz-bas
 tutorial/WorkingWithDataTree
 tutorial/label_guide
 how_to/ConversionGuideEmcee
+how_to/ConversionGuideNumPyro
 ArviZ in Context <https://arviz-devs.github.io/EABM/>
 :::
 

docs/source/index.md

@@ -109,8 +109,30 @@ def _numpyro_noncentered_model(J, sigma, y=None):
         return numpyro.sample("obs", dist.Normal(theta, sigma), obs=y)
 
 
+def _numpyro_noncentered_guide(J, sigma, y=None):
+    import jax
+    import numpyro
+    import numpyro.distributions as dist
+
+    # Variational parameters for mu
+    mu_loc = numpyro.param("mu_loc", 0.0)
+    mu_scale = numpyro.param("mu_scale", 1.0, constraint=dist.constraints.positive)
+    numpyro.sample("mu", dist.Normal(mu_loc, mu_scale))
+
+    # Variational parameters for tau (positive support)
+    tau_loc = numpyro.param("tau_loc", 1.0)
+    tau_scale = numpyro.param("tau_scale", 0.5, constraint=dist.constraints.positive)
+    numpyro.sample("tau", dist.LogNormal(jax.numpy.log(tau_loc), tau_scale))
+
+    # Variational parameters for eta
+    eta_loc = numpyro.param("eta_loc", jax.numpy.zeros(J))
+    eta_scale = numpyro.param("eta_scale", jax.numpy.ones(J), constraint=dist.constraints.positive)
+    with numpyro.plate("J", J):
+        numpyro.sample("eta", dist.Normal(eta_loc, eta_scale))
+
+
 def numpyro_schools_model(data, draws, chains):
-    """Centered eight schools implementation in NumPyro."""
+    """Non-centered eight schools implementation in NumPyro."""
     from jax.random import PRNGKey
     from numpyro.infer import MCMC, NUTS
 
@@ -133,6 +155,35 @@ def numpyro_schools_model(data, draws, chains):
     return mcmc
 
 
+def numpyro_schools_model_svi(data, draws, chains):
+    """Non-centered eight schools implementation in NumPyro."""
+    from jax.random import PRNGKey
+    from numpyro.infer import SVI, Trace_ELBO, init_to_sample
+    from numpyro.infer.autoguide import AutoNormal
+    from numpyro.optim import Adam
+
+    guide = AutoNormal(_numpyro_noncentered_model, init_loc_fn=init_to_sample())
+    svi = SVI(_numpyro_noncentered_model, guide=guide, optim=Adam(0.05), loss=Trace_ELBO())
+    svi_result = svi.run(PRNGKey(0), 4000, **data)
+    return {"svi": svi, "svi_result": svi_result, "model_kwargs": data}
+
+
+def numpyro_schools_model_svi_custom_guide(data, draws, chains):
+    """Non-centered eight schools implementation in NumPyro."""
+    from jax.random import PRNGKey
+    from numpyro.infer import SVI, Trace_ELBO
+    from numpyro.optim import Adam
+
+    guide = _numpyro_noncentered_guide
+    svi = SVI(_numpyro_noncentered_model, guide=guide, optim=Adam(0.05), loss=Trace_ELBO())
+    svi_result = svi.run(PRNGKey(0), 4000, **data)
+    return {
+        "svi": svi,
+        "svi_result": svi_result,
+        "model_kwargs": data,
+    }
+
+
 def pystan_noncentered_schools(data, draws, chains):
     """Non-centered eight schools implementation for pystan."""
     schools_code = """
@@ -188,10 +239,12 @@ def load_cached_models(eight_schools_data, draws, chains, libs=None):
     """Load pystan, emcee, and pyro models from pickle."""
     here = os.path.dirname(os.path.abspath(__file__))
     supported = (
-        # ("pystan", pystan_noncentered_schools),
-        ("emcee", emcee_schools_model),
-        # ("pyro", pyro_noncentered_schools),
-        ("numpyro", numpyro_schools_model),
+        # ("pystan", pystan_noncentered_schools, None),
+        ("emcee", emcee_schools_model, None),
+        # ("pyro", pyro_noncentered_schools, None),
+        ("numpyro", numpyro_schools_model, None),
+        ("numpyro", numpyro_schools_model_svi, "numpyro_svi"),
+        ("numpyro", numpyro_schools_model_svi_custom_guide, "numpyro_svi_custom_guide"),
     )
     data_directory = os.path.join(here, "saved_models")
     if not os.path.isdir(data_directory):
@@ -201,7 +254,8 @@ def load_cached_models(eight_schools_data, draws, chains, libs=None):
     if isinstance(libs, str):
         libs = [libs]
 
-    for library_name, func in supported:
+    for library_name, func, addl_model_key in supported:
+        model_key = addl_model_key or library_name
         if libs is not None and library_name not in libs:
             continue
         library = library_handle(library_name)
@@ -214,7 +268,7 @@ def load_cached_models(eight_schools_data, draws, chains, libs=None):
 
         py_version = sys.version_info
         fname = (
-            f"{py_version.major}.{py_version.minor}_{library.__name__}_{library.__version__}"
+            f"{py_version.major}.{py_version.minor}_{model_key}_{library.__version__}"
             f"_{sys.platform}_{draws}_{chains}.pkl.gzip"
         )
 
@@ -225,11 +279,11 @@ def load_cached_models(eight_schools_data, draws, chains, libs=None):
                     _log.info("Generating and caching %s", fname)
                     cloudpickle.dump(func(eight_schools_data, draws, chains), buff)
                 except AttributeError as err:
-                    raise AttributeError(f"Failed caching {library_name}") from err
+                    raise AttributeError(f"Failed caching {model_key}") from err
 
         with gzip.open(path, "rb") as buff:
             _log.info("Loading %s from cache", fname)
-            models[library.__name__] = cloudpickle.load(buff)
+            models[model_key] = cloudpickle.load(buff)
 
     return models