Update gmm and dmm examples

README.md

@@ -6,4 +6,32 @@
 
 Coix (COmbinators In jaX) is a flexible and backend-agnostic implementation of inference combinators [(Stites and Zimmermann et al., 2021)](https://arxiv.org/abs/2103.00668), a set of program transformations for compositional inference with probabilistic programs. Coix ships with backends for numpyro and oryx, and a set of pre-implemented losses and utility functions that allows to implement and run a wide variety of inference algorithms out-of-the-box.
 
+Coix is a lightweight framework which includes the following main components:
+
+- **coix.api:** Implementation of the program combinators.
+- **coix.core:** Basic program transformations which are used to modify behavior of a stochastic program.
+- **coix.loss:** Common objectives for variational inference.
+- **coix.algo:** Example inference algorithms.
+
+Currently, we support [numpyro](https://github.com/pyro-ppl/numpyro) and [oryx](https://github.com/jax-ml/oryx) backends. But other backends can be easily added via the [coix.register_backend](https://coix.readthedocs.io/en/latest/core.html#coix.core.register_backend) utility.
+
 *This is not an officially supported Google product.*
+
+## Installation
+
+To install Coix, you can use pip:
+
+```
+pip install coix
+```
+
+or you can clone the repository:
+
+```
+git clone https://github.com/jax-ml/coix.git
+cd coix
+pip install -e .[dev,doc]
+```
+
+Many examples would run faster on accelerators. You can follow the [JAX installation](https://jax.readthedocs.io/en/latest/installation.html) instruction for how to install JAX with GPU or TPU support.
+

coix/core.py

@@ -137,10 +137,12 @@ def wrapped(*args, **kwargs):
 
 
 def empirical(out, trace, metrics):
+  """Creates an empirical program given a trace."""
   return get_backend()["empirical"](out, trace, metrics)
 
 
 def suffix(p):
+  """Adds suffix `_PREV_` to variable names of `p`."""
   fn = get_backend()["suffix"]
   if fn is not None:
     return fn(p)
@@ -149,6 +151,7 @@ def suffix(p):
 
 
 def detach(p):
+  """Makes random variables in `p` become non-reparameterized."""
   fn = get_backend()["detach"]
   if fn is not None:
     return fn(p)
@@ -157,6 +160,7 @@ def detach(p):
 
 
 def stick_the_landing(p):
+  """Stops gradient of distributions' parameters before computing log prob."""
   fn = get_backend()["stick_the_landing"]
   if fn is not None:
     return fn(p)
@@ -165,6 +169,7 @@ def stick_the_landing(p):
 
 
 def prng_key():
+  """Generates a random JAX PRNGKey."""
   fn = get_backend()["prng_key"]
   if fn is not None:
     return fn()

coix/numpyro.py

@@ -51,7 +51,12 @@ def wrapped(*args, **kwargs):
       if site["type"] == "sample":
         value = site["value"]
         log_prob = site["fn"].log_prob(value)
-        trace[name] = {"value": value, "log_prob": log_prob}
+        event_dim_holder = jnp.empty([1] * site["fn"].event_dim)
+        trace[name] = {
+            "value": value,
+            "log_prob": log_prob,
+            "_event_dim_holder": event_dim_holder,
+        }
         if site.get("is_observed", False):
           trace[name]["is_observed"] = True
     metrics = {
@@ -83,7 +88,7 @@ def wrapped(*args, **kwargs):
     del args, kwargs
     for name, site in trace.items():
       value, lp = site["value"], site["log_prob"]
-      event_dim = jnp.ndim(value) - jnp.ndim(lp)
+      event_dim = jnp.ndim(site["_event_dim_holder"])
       obs = value if "is_observed" in site else None
       numpyro.sample(name, dist.Delta(value, lp, event_dim=event_dim), obs=obs)
     for name, value in metrics.items():

docs/conf.py

@@ -207,11 +207,13 @@
 ):
   toctree_path = "notebooks/" if src_file.endswith("ipynb") else "examples/"
   filename = os.path.splitext(src_file.split("/")[-1])[0]
-  png_path = "_static/" + filename + ".png"
+  img_path = "_static/" + filename + ".png"
   # use Coix logo if not exist png file
-  if not os.path.exists(png_path):
-    png_path = "_static/coix_logo.png"
-  nbsphinx_thumbnails[toctree_path + filename] = png_path
+  if not os.path.exists(img_path):
+    img_path = "_static/" + filename + ".gif"
+    if not os.path.exists(img_path):
+      img_path = "_static/coix_logo.png"
+  nbsphinx_thumbnails[toctree_path + filename] = img_path
 
 
 # -- Options for HTML output -------------------------------------------------

docs/index.rst

@@ -32,9 +32,9 @@ Coix Documentation
    examples/gmm
    examples/dmm
    examples/bmnist
+   examples/anneal_oryx
    examples/gmm_oryx
    examples/dmm_oryx
-   examples/anneal_oryx
 
 Indices and tables
 ==================

examples/anneal.py

@@ -24,6 +24,9 @@
 
     1. Zimmermann, Heiko, et al. "Nested variational inference." NeuRIPS 2021.
 
+.. image:: ../_static/anneal.png
+    :align: center
+
 """
 
 import argparse
@@ -199,7 +202,7 @@ def eval_program(seed):
 
   plt.figure(figsize=(8, 8))
   x = trace["x"]["value"].reshape((-1, 2))
-  H, xedges, yedges = np.histogram2d(x[:, 0], x[:, 1], bins=100)
+  H, _, _ = np.histogram2d(x[:, 0], x[:, 1], bins=100)
   plt.imshow(H.T)
   plt.show()
 

examples/anneal_oryx.py

@@ -24,6 +24,9 @@
 
     1. Zimmermann, Heiko, et al. "Nested variational inference." NeuRIPS 2021.
 
+.. image:: ../_static/anneal_oryx.png
+    :align: center
+
 """
 
 import argparse
@@ -119,7 +122,7 @@ def __call__(self, x):
 def anneal_target(network, key, k=0):
   key_out, key = random.split(key)
   x = coryx.rv(dist.Normal(0, 5).expand([2]).mask(False), name="x")(key)
-  coix.factor(network.anneal_density(x, index=k), name="anneal_density")
+  coryx.factor(network.anneal_density(x, index=k), name="anneal_density")
   return key_out, {"x": x}
 
 
@@ -192,7 +195,7 @@ def main(args):
 
   plt.figure(figsize=(8, 8))
   x = trace["x"]["value"].reshape((-1, 2))
-  H, xedges, yedges = np.histogram2d(x[:, 0], x[:, 1], bins=100)
+  H, _, _ = np.histogram2d(x[:, 0], x[:, 1], bins=100)
   plt.imshow(H.T)
   plt.show()