Add Bernoulli-gamma likelihood

neuralprocesses/architectures/agnp.py

@@ -31,13 +31,13 @@ def construct_agnp(*args, nps=nps, num_heads=8, **kw_args):
         width (int, optional): Widths of all intermediate MLPs. Defaults to 512.
         nonlinearity (Callable or str, optional): Nonlinearity. Can also be specified
             as a string: `"ReLU"` or `"LeakyReLU"`. Defaults to ReLUs.
-        likelihood (str, optional): Likelihood. Must be one of `"het"` or `"lowrank"`.
-            Defaults to `"lowrank"`.
+        likelihood (str, optional): Likelihood. Must be one of `"het"`, `"lowrank"`,
+            `"spikes-beta"`, or `"bernoulli-gamma"`. Defaults to `"lowrank"`.
         num_basis_functions (int, optional): Number of basis functions for the
             low-rank likelihood. Defaults to 512.
         dim_lv (int, optional): Dimensionality of the latent variable. Defaults to 0.
         lv_likelihood (str, optional): Likelihood of the latent variable. Must be one of
-            `"het"`, `"dense"`, or `"spikes-beta"`. Defaults to `"het"`.
+            `"het"` or `"dense"`. Defaults to `"het"`.
         transform (str or tuple[float, float]): Bijection applied to the
             output of the model. This can help deal with positive of bounded data.
             Must be either `"positive"`, `"exp"`, `"softplus"`, or

neuralprocesses/architectures/climate.py

@@ -27,8 +27,8 @@ def construct_climate_convgnp_mlp(
             to 128.
         lr_deg (float, optional): Resolution of the low-resolution grid. Defaults to
             0.75.
-        likelihood (str, optional): Likelihood. Must be one of `"het"` or `"lowrank".
-            Defaults to `"lowrank"`.
+        likelihood (str, optional): Likelihood. Must be one of `"het"`, `"lowrank"`,
+            `"spikes-beta"`, or `"bernoulli-gamma"`. Defaults to `"lowrank"`.
         dtype (dtype, optional): Data type.
     """
     mlp_width = 128

neuralprocesses/architectures/convgnp.py

@@ -150,7 +150,7 @@ def construct_convgnp(
         margin (float, optional): Margin of the internal discretisation. Defaults to
             0.1.
         likelihood (str, optional): Likelihood. Must be one of `"het"`, `"lowrank"`,
-            or `"spikes-beta"`. Defaults to `"lowrank"`.
+            `"spikes-beta"`, or `"bernoulli-gamma"`. Defaults to `"lowrank"`.
         conv_arch (str, optional): Convolutional architecture to use. Must be one of
             `"unet[-res][-sep]"` or `"conv[-res][-sep]"`. Defaults to `"unet"`.
         unet_channels (tuple[int], optional): Channels of every layer of the UNet.

neuralprocesses/architectures/gnp.py

@@ -57,13 +57,13 @@ def construct_gnp(
         width (int, optional): Widths of all intermediate MLPs. Defaults to 512.
         nonlinearity (Callable or str, optional): Nonlinearity. Can also be specified
             as a string: `"ReLU"` or `"LeakyReLU"`. Defaults to ReLUs.
-        likelihood (str, optional): Likelihood. Must be one of `"het"` or `"lowrank"`.
-            Defaults to `"lowrank"`.
+        likelihood (str, optional): Likelihood. Must be one of `"het"`, `"lowrank"`,
+            `"spikes-beta"`, or `"bernoulli-gamma"`. Defaults to `"lowrank"`.
         num_basis_functions (int, optional): Number of basis functions for the
             low-rank likelihood. Defaults to 512.
         dim_lv (int, optional): Dimensionality of the latent variable. Defaults to 0.
         lv_likelihood (str, optional): Likelihood of the latent variable. Must be one of
-            `"het"`, `"dense"`, or `"spikes-beta"`. Defaults to `"het"`.
+            `"het"` or `"dense"`. Defaults to `"het"`.
         transform (str or tuple[float, float]): Bijection applied to the
             output of the model. This can help deal with positive of bounded data.
             Must be either `"positive"`, `"exp"`, `"softplus"`, or

neuralprocesses/architectures/util.py

@@ -12,8 +12,8 @@ def construct_likelihood(nps=nps, *, spec, dim_y, num_basis_functions, dtype):
     Args:
         nps (module): Appropriate backend-specific module.
         spec (str, optional): Specification. Must be one of `"het"`, `"lowrank"`,
-            `"dense"`, or `"spikes-beta"`. Defaults to `"lowrank"`. Must be given as
-            a keyword argument.
+            `"dense"`, `"spikes-beta"`, or `"bernoulli-gamma"`. Defaults to `"lowrank"`.
+            Must be given as a keyword argument.
         dim_y (int): Dimensionality of the outputs. Must be given as a keyword argument.
         num_basis_functions (int): Number of basis functions for the low-rank
             likelihood. Must be given as a keyword argument.
@@ -52,6 +52,10 @@ def construct_likelihood(nps=nps, *, spec, dim_y, num_basis_functions, dtype):
         num_channels = (2 + 3) * dim_y  # Alpha, beta, and three log-probabilities
         selector = nps.SelectFromChannels(dim_y, dim_y, dim_y, dim_y, dim_y)
         lik = nps.SpikesBetaLikelihood()
+    elif spec == "bernoulli-gamma":
+        num_channels = (2 + 2) * dim_y  # Shape, scale, and two log-probabilities
+        selector = nps.SelectFromChannels(dim_y, dim_y, dim_y, dim_y)
+        lik = nps.BernoulliGammaLikelihood()
 
     else:
         raise ValueError(f'Incorrect likelihood specification "{spec}".')

neuralprocesses/dist/__init__.py

@@ -1,6 +1,7 @@
 from .beta import *
 from .dirac import *
 from .dist import *
+from .gamma import *
 from .geom import *
 from .normal import *
 from .spikeslab import *

neuralprocesses/likelihood.py

@@ -7,7 +7,7 @@
 from . import _dispatch
 from .aggregate import Aggregate, AggregateInput
 from .datadims import data_dims
-from .dist import Beta, Dirac, MultiOutputNormal, SpikesSlab
+from .dist import Beta, Dirac, Gamma, MultiOutputNormal, SpikesSlab
 from .parallel import Parallel
 from .util import register_module, split, split_dimension
 
@@ -16,6 +16,8 @@
     "HeterogeneousGaussianLikelihood",
     "LowRankGaussianLikelihood",
     "DenseGaussianLikelihood",
+    "SpikesBetaLikelihood",
+    "BernoulliGammaLikelihood",
 ]
 
 
@@ -359,7 +361,7 @@ def _dense_var(coder: DenseGaussianLikelihood, xz, z: B.Numeric):
 
 @register_module
 class SpikesBetaLikelihood(AbstractLikelihood):
-    """Gaussian likelihood with heterogeneous noise.
+    """Mixture of a beta distribution, a Dirac delta at zero, and a Dirac delta at one.
 
     Args:
         epsilon (float, optional): Tolerance for equality checking. Defaults to `1e-6`.
@@ -451,3 +453,94 @@ def _spikesbeta(coder: SpikesBetaLikelihood, xz, z: B.Numeric):
     logps = z_logps
 
     return alpha, beta, logp0, logp1, logps, d + 1
+
+
+@register_module
+class BernoulliGammaLikelihood(AbstractLikelihood):
+    """Mixture of a gamma distribution and a Dirac delta at zero.
+
+    Args:
+        epsilon (float, optional): Tolerance for equality checking. Defaults to `1e-6`.
+
+    Args:
+        epsilon (float): Tolerance for equality checking.
+    """
+
+    @_dispatch
+    def __init__(self, epsilon: float = 1e-6):
+        self.epsilon = epsilon
+
+    def __str__(self):
+        return repr(self)
+
+    def __repr__(self):
+        return f"BernoulliGammaLikelihood(epsilon={self.epsilon!r})"
+
+
+@_dispatch
+def code(
+    coder: BernoulliGammaLikelihood,
+    xz,
+    z,
+    x,
+    *,
+    dtype_lik=None,
+    **kw_args,
+):
+    k, scale, logp0, logps, d = _bernoulligamma(coder, xz, z)
+
+    # Cast parameters to the right data type.
+    if dtype_lik:
+        k = B.cast(dtype_lik, k)
+        scale = B.cast(dtype_lik, scale)
+        logp0 = B.cast(dtype_lik, logp0)
+        logps = B.cast(dtype_lik, logps)
+
+    # Create the spikes vector.
+    with B.on_device(z):
+        dtype = dtype_lik or B.dtype(z)
+        spikes = B.stack(B.zero(dtype))
+
+    return xz, SpikesSlab(
+        spikes,
+        Gamma(k, scale, d),
+        B.stack(logp0, logps, axis=-1),
+        d,
+        epsilon=coder.epsilon,
+    )
+
+
+@_dispatch
+def _bernoulligamma(
+    coder: BernoulliGammaLikelihood,
+    xz: AggregateInput,
+    z: Aggregate,
+):
+    ks, scales, logp0s, logpss, ds = zip(
+        *[_bernoulligamma(coder, xzi, zi) for (xzi, _), zi in zip(xz, z)]
+    )
+
+    # Concatenate into one big distribution.
+    k = Aggregate(*ks)
+    scale = Aggregate(*scales)
+    logp0 = Aggregate(*logp0s)
+    logps = Aggregate(*logpss)
+    d = Aggregate(*ds)
+
+    return k, scale, logp0, logps, d
+
+
+@_dispatch
+def _bernoulligamma(coder: BernoulliGammaLikelihood, xz, z: B.Numeric):
+    d = data_dims(xz)
+    dim_y = B.shape(z, -d - 1) // 4
+
+    z_k, z_scale, z_logp0, z_logps = split(z, (dim_y, dim_y, dim_y, dim_y), -d - 1)
+
+    # Transform into parameters.
+    k = 1e-3 + B.softplus(z_k)
+    scale = 1e-3 + B.softplus(z_scale)
+    logp0 = z_logp0
+    logps = z_logps
+
+    return k, scale, logp0, logps, d + 1

tests/test_architectures.py

@@ -66,7 +66,7 @@ def product_kw_args(config, **kw_args):
         },
         dim_x=[1, 2],
         dim_y=[1, 2],
-        likelihood=["het", "lowrank", "spikes-beta"],
+        likelihood=["het", "lowrank", "spikes-beta", "bernoulli-gamma"],
     )
     # NP:
     + product_kw_args(
@@ -79,7 +79,7 @@ def product_kw_args(config, **kw_args):
         },
         dim_x=[1, 2],
         dim_y=[1, 2],
-        likelihood=["het", "lowrank", "spikes-beta"],
+        likelihood=["het", "lowrank", "spikes-beta", "bernoulli-gamma"],
         lv_likelihood=["het", "dense"],
     )
     # ACNP:
@@ -94,7 +94,7 @@ def product_kw_args(config, **kw_args):
         },
         dim_x=[1, 2],
         dim_y=[1, 2],
-        likelihood=["het", "lowrank", "spikes-beta"],
+        likelihood=["het", "lowrank", "spikes-beta", "bernoulli-gamma"],
     )
     # ANP:
     + product_kw_args(
@@ -108,7 +108,7 @@ def product_kw_args(config, **kw_args):
         },
         dim_x=[1, 2],
         dim_y=[1, 2],
-        likelihood=["het", "lowrank", "spikes-beta"],
+        likelihood=["het", "lowrank", "spikes-beta", "bernoulli-gamma"],
         lv_likelihood=["het", "dense"],
     )
     # ConvCNP and ConvGNP:
@@ -122,7 +122,7 @@ def product_kw_args(config, **kw_args):
             },
             dim_x=[1, 2],
             dim_y=[1, 2],
-            likelihood=["het", "lowrank", "spikes-beta"],
+            likelihood=["het", "lowrank", "spikes-beta", "bernoulli-gamma"],
             encoder_scales_learnable=[True, False],
             decoder_scale_learnable=[True, False],
         )
@@ -138,7 +138,7 @@ def product_kw_args(config, **kw_args):
             },
             dim_x=[1, 2],
             dim_y=[1, 2],
-            likelihood=["het", "lowrank", "spikes-beta"],
+            likelihood=["het", "lowrank", "spikes-beta", "bernoulli-gamma"],
             lv_likelihood=["het", "lowrank"],
         )
     )
@@ -219,7 +219,7 @@ def construct_model():
 
     def sample():
         if "likelihood" in config:
-            binary = config["likelihood"] == "spikes-beta"
+            binary = config["likelihood"] in {"spikes-beta", "bernoulli-gamma"}
         else:
             binary = False
         return generate_data(