Rename fine distributions to BMMs

.github/workflows/build.yml

@@ -15,7 +15,8 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        python-version: ["3.11", "3.12"]
+        # Use only 3.11 as for 3.12 pytype is not supported yet
+        python-version: ["3.11"]
         poetry-version: ["1.3.2"]
 
     steps:

docs/api/fine-distributions.md → docs/api/bmm.md

@@ -1,4 +1,4 @@
-# Fine distributions
+# Bend and Mix Models
 
 ## Core utilities
 
@@ -12,7 +12,7 @@
 
 ::: bmi.samplers._tfp.ProductDistribution
 
-::: bmi.samplers._tfp.FineSampler
+::: bmi.samplers._tfp.BMMSampler
 
 ## Basic distributions
 

docs/api/index.md

@@ -12,8 +12,8 @@
 
 [Samplers](samplers.md) represent joint probability distributions with known mutual information from which one can sample. They are lower level than `Tasks` and can be used to define new tasks by transformations which preserve mutual information.
 
-### Fine distributions
-[Subpackage](fine-distributions.md) implementing distributions in which the ground-truth mutual information may not be known analytically, but can be efficiently approximated using Monte Carlo methods. 
+### Bend and Mix Models
+[Subpackage](bmm.md) implementing distributions known as *Bend and Mix Models*, for which the ground-truth mutual information may not be known analytically, but can be efficiently approximated using Monte Carlo methods. 
 
 ## Interfaces
 [Interfaces](interfaces.md) defines the main interfaces used in the package.

docs/api/samplers.md

@@ -26,9 +26,9 @@ Samplers represent probability distributions with known mutual information.
 
 ::: bmi.samplers.ZeroInflatedPoissonizationSampler
 
-## Fine distributions
+## Bend and Mix Models
 
-See the [fine distributions subpackage API](fine-distributions.md) for more information.
+See the [Bend and Mix Models subpackage API](bmm.md) for more information.
 
 ### Auxiliary
 

mkdocs.yml

@@ -48,7 +48,7 @@ nav:
   - Home: index.md
   - Estimators: estimators.md
   - Benchmark: benchmarking-new-estimator.md
-  - Fine distributions: fine-distributions.md
+  - Bend and Mix Models: bmm.md
   - Contributing: contributing.md
   - API: api/index.md
 

pyproject.toml

@@ -22,6 +22,11 @@ scipy = "^1.10.1"
 tqdm = "^4.64.1"
 tensorflow-probability = {extras = ["jax"], version = "^0.20.1"}
 
+[tool.poetry.group.bayes]
+optional = true
+
+[tool.poetry.group.bayes.dependencies]
+numpyro = "^0.14.0"
 
 [tool.poetry.group.dev]
 optional = true

references.bib

@@ -1,10 +1,11 @@
-@misc{mixtures-neural-critics-2023,
-      title={The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions}, 
+@misc{pmi-profiles-bmms-2023,
+      title={On the Properties and Estimation of Pointwise Mutual Information Profiles}, 
       author={Paweł Czyż and Frederic Grabowski and Julia E. Vogt and Niko Beerenwinkel and Alexander Marx},
       year={2023},
       eprint={2310.10240},
       archivePrefix={arXiv},
-      primaryClass={stat.ML}
+      primaryClass={stat.ML},
+      url={https://arxiv.org/abs/2310.10240}
 }
 
 @inproceedings{beyond-normal-2023,

src/bmi/benchmark/tasks/mixtures.py

@@ -4,7 +4,7 @@
 import bmi.samplers as samplers
 import bmi.transforms as transforms
 from bmi.benchmark.task import Task
-from bmi.samplers import fine
+from bmi.samplers import bmm
 
 _MC_MI_ESTIMATE_SAMPLE = 100_000
 
@@ -15,10 +15,10 @@ def task_x(
 ) -> Task:
     """The X distribution."""
 
-    dist = fine.mixture(
+    dist = bmm.mixture(
         proportions=jnp.array([0.5, 0.5]),
         components=[
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 covariance=samplers.canonical_correlation([x * gaussian_correlation]),
                 mean=jnp.zeros(2),
                 dim_x=1,
@@ -27,7 +27,7 @@ def task_x(
             for x in [-1, 1]
         ],
     )
-    sampler = fine.FineSampler(dist, mi_estimate_sample=mi_estimate_sample)
+    sampler = bmm.BMMSampler(dist, mi_estimate_sample=mi_estimate_sample)
 
     return Task(
         sampler=sampler,
@@ -47,44 +47,44 @@ def task_ai(
     corr = 0.95
     var_x = 0.04
 
-    dist = fine.mixture(
+    dist = bmm.mixture(
         proportions=jnp.full(6, fill_value=1 / 6),
         components=[
             # I components
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 dim_x=1,
                 dim_y=1,
                 mean=jnp.array([1.0, 0.0]),
                 covariance=np.diag([0.01, 0.2]),
             ),
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 dim_x=1,
                 dim_y=1,
                 mean=jnp.array([1.0, 1]),
                 covariance=np.diag([0.05, 0.001]),
             ),
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 dim_x=1,
                 dim_y=1,
                 mean=jnp.array([1.0, -1]),
                 covariance=np.diag([0.05, 0.001]),
             ),
             # A components
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 dim_x=1,
                 dim_y=1,
                 mean=jnp.array([-0.8, -0.2]),
                 covariance=np.diag([0.03, 0.001]),
             ),
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 dim_x=1,
                 dim_y=1,
                 mean=jnp.array([-1.2, 0.0]),
                 covariance=jnp.array(
                     [[var_x, jnp.sqrt(var_x * 0.2) * corr], [jnp.sqrt(var_x * 0.2) * corr, 0.2]]
                 ),
             ),
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 dim_x=1,
                 dim_y=1,
                 mean=jnp.array([-0.4, 0.0]),
@@ -94,7 +94,7 @@ def task_ai(
             ),
         ],
     )
-    sampler = fine.FineSampler(dist, mi_estimate_sample=mi_estimate_sample)
+    sampler = bmm.BMMSampler(dist, mi_estimate_sample=mi_estimate_sample)
 
     return Task(
         sampler=sampler,
@@ -110,10 +110,10 @@ def task_galaxy(
 ) -> Task:
     """The Galaxy distribution."""
 
-    balls_mixt = fine.mixture(
+    balls_mixt = bmm.mixture(
         proportions=jnp.array([0.5, 0.5]),
         components=[
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 covariance=samplers.canonical_correlation([0.0], additional_y=1),
                 mean=jnp.array([x, x, x]) * distance / 2,
                 dim_x=2,
@@ -123,7 +123,7 @@ def task_galaxy(
         ],
     )
 
-    base_sampler = fine.FineSampler(balls_mixt, mi_estimate_sample=mi_estimate_sample)
+    base_sampler = bmm.BMMSampler(balls_mixt, mi_estimate_sample=mi_estimate_sample)
     a = jnp.array([[0, -1], [1, 0]])
     spiral = transforms.Spiral(a, speed=speed)
 
@@ -150,10 +150,10 @@ def task_waves(
 
     assert n_components > 0
 
-    base_dist = fine.mixture(
+    base_dist = bmm.mixture(
         proportions=jnp.ones(n_components) / n_components,
         components=[
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 covariance=jnp.diag(jnp.array([0.1, 1.0, 0.1])),
                 mean=jnp.array([x, 0, x % 4]) * 1.5,
                 dim_x=2,
@@ -162,7 +162,7 @@ def task_waves(
             for x in range(n_components)
         ],
     )
-    base_sampler = fine.FineSampler(base_dist, mi_estimate_sample=mi_estimate_sample)
+    base_sampler = bmm.BMMSampler(base_dist, mi_estimate_sample=mi_estimate_sample)
     aux_sampler = samplers.TransformedSampler(
         base_sampler,
         transform_x=lambda x: x
@@ -193,10 +193,10 @@ def task_concentric_multinormal(
 
     assert n_components > 0
 
-    dist = fine.mixture(
+    dist = bmm.mixture(
         proportions=jnp.ones(n_components) / n_components,
         components=[
-            fine.MultivariateNormalDistribution(
+            bmm.MultivariateNormalDistribution(
                 covariance=jnp.diag(jnp.array(dim_x * [i**2] + [0.0001])),
                 mean=jnp.array(dim_x * [0.0] + [1.0 * i]),
                 dim_x=dim_x,
@@ -205,7 +205,7 @@ def task_concentric_multinormal(
             for i in range(1, 1 + n_components)
         ],
     )
-    sampler = fine.FineSampler(dist, mi_estimate_sample=mi_estimate_sample)
+    sampler = bmm.BMMSampler(dist, mi_estimate_sample=mi_estimate_sample)
 
     return Task(
         sampler=sampler,
@@ -238,23 +238,23 @@ def task_multinormal_sparse_w_inliers(
         eta_x=strength,
     )
 
-    signal_dist = fine.MultivariateNormalDistribution(
+    signal_dist = bmm.MultivariateNormalDistribution(
         dim_x=dim_x,
         dim_y=dim_y,
         covariance=params.correlation,
     )
 
-    noise_dist = fine.ProductDistribution(
+    noise_dist = bmm.ProductDistribution(
         dist_x=signal_dist.dist_x,
         dist_y=signal_dist.dist_y,
     )
 
-    dist = fine.mixture(
+    dist = bmm.mixture(
         proportions=jnp.array([1 - inlier_fraction, inlier_fraction]),
         components=[signal_dist, noise_dist],
     )
 
-    sampler = fine.FineSampler(dist, mi_estimate_sample=mi_estimate_sample)
+    sampler = bmm.BMMSampler(dist, mi_estimate_sample=mi_estimate_sample)
 
     task_id = f"mult-sparse-w-inliers-{dim_x}-{dim_y}-{n_interacting}-{strength}-{inlier_fraction}"
     return Task(

src/bmi/estimators/external/gmm.py

@@ -1,3 +1,13 @@
+"""A Gaussian mixture model estimator, allowing for model-based
+Bayesian estimator of mutual information.
+The full description can be found [here](https://arxiv.org/abs/2310.10240).
+
+Note that to use this estimator you need to install external dependencies:
+```bash
+$ pip install benchmark-mi[bayes]
+```
+"""
+
 try:
     import numpyro  # type: ignore
     import numpyro.distributions as dist  # type: ignore
@@ -12,7 +22,7 @@
 from numpy.typing import ArrayLike
 
 from bmi.interface import BaseModel, IMutualInformationPointEstimator
-from bmi.samplers import fine
+from bmi.samplers import bmm
 from bmi.utils import ProductSpace
 
 
@@ -74,14 +84,14 @@ def model(
     )
 
 
-def sample_into_fine_distribution(
+def sample_into_bmm_distribution(
     means: jnp.ndarray,
     covariances: jnp.ndarray,
     proportions: jnp.ndarray,
     dim_x: int,
     dim_y: int,
-) -> fine.JointDistribution:
-    """Builds a fine distribution from a Gaussian mixture model parameters."""
+) -> bmm.JointDistribution:
+    """Builds a bmm distribution from a Gaussian mixture model parameters."""
     # Check if the dimensions are right
     n_components = proportions.shape[0]
     n_dims = dim_x + dim_y
@@ -90,7 +100,7 @@ def sample_into_fine_distribution(
 
     # Build components
     components = [
-        fine.MultivariateNormalDistribution(
+        bmm.MultivariateNormalDistribution(
             dim_x=dim_x,
             dim_y=dim_y,
             mean=mean,
@@ -100,7 +110,7 @@ def sample_into_fine_distribution(
     ]
 
     # Build a mixture model
-    return fine.mixture(proportions=proportions, components=components)
+    return bmm.mixture(proportions=proportions, components=components)
 
 
 class GMMEstimatorParams(BaseModel):
@@ -185,12 +195,12 @@ def run_mcmc(self, x: ArrayLike, y: ArrayLike):
         self._dim_x = space.dim_x
         self._dim_y = space.dim_y
 
-    def get_fine_distribution(self, idx: int) -> fine.JointDistribution:
+    def get_bmm_distribution(self, idx: int) -> bmm.JointDistribution:
         if self._mcmc is None:
             raise ValueError("You need to run MCMC first. See the `run_mcmc` method.")
 
         samples = self._mcmc.get_samples()
-        return sample_into_fine_distribution(
+        return sample_into_bmm_distribution(
             means=samples["mu"][idx],
             covariances=samples["cov"][idx],
             proportions=samples["pi"][idx],
@@ -204,8 +214,8 @@ def get_sample_mi(self, idx: int, mc_samples: Optional[int] = None, key=None) ->
         if key is None:
             self.key, key = jax.random.split(self.key)
 
-        distribution = self.get_fine_distribution(idx)
-        mi, _ = fine.monte_carlo_mi_estimate(key=key, dist=distribution, n=mc_samples)
+        distribution = self.get_bmm_distribution(idx)
+        mi, _ = bmm.monte_carlo_mi_estimate(key=key, dist=distribution, n=mc_samples)
         return mi
 
     def get_posterior_mi(

src/bmi/samplers/__init__.py

@@ -13,7 +13,7 @@
 )
 
 # isort: on
-import bmi.samplers._tfp as fine
+import bmi.samplers._tfp as bmm
 from bmi.samplers._independent_coordinates import IndependentConcatenationSampler
 from bmi.samplers._split_student_t import SplitStudentT
 from bmi.samplers._splitmultinormal import BivariateNormalSampler, SplitMultinormal
@@ -33,7 +33,7 @@
     "AdditiveUniformSampler",
     "BaseSampler",
     "canonical_correlation",
-    "fine",
+    "bmm",
     "parametrised_correlation_matrix",
     "BivariateNormalSampler",
     "SplitMultinormal",

src/bmi/samplers/_tfp/__init__.py

@@ -19,7 +19,7 @@
 
 # isort: on
 from bmi.samplers._tfp._product import ProductDistribution
-from bmi.samplers._tfp._wrapper import FineSampler
+from bmi.samplers._tfp._wrapper import BMMSampler
 
 __all__ = [
     "JointDistribution",
@@ -30,7 +30,7 @@
     "MultivariateNormalDistribution",
     "MultivariateStudentDistribution",
     "ProductDistribution",
-    "FineSampler",
+    "BMMSampler",
     "construct_multivariate_normal_distribution",
     "construct_multivariate_student_distribution",
 ]