Source code for torch.distributions.lkj_cholesky
"""
This closely follows the implementation in NumPyro (https://github.com/pyro-ppl/numpyro).
Original copyright notice:
# Copyright: Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
"""
import math
import torch
from torch.distributions import Beta, constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all
__all__ = ["LKJCholesky"]
[docs]class LKJCholesky(Distribution):
r"""
LKJ distribution for lower Cholesky factor of correlation matrices.
The distribution is controlled by ``concentration`` parameter :math:`\eta`
to make the probability of the correlation matrix :math:`M` generated from
a Cholesky factor proportional to :math:`\det(M)^{\eta - 1}`. Because of that,
when ``concentration == 1``, we have a uniform distribution over Cholesky
factors of correlation matrices::
L ~ LKJCholesky(dim, concentration)
X = L @ L' ~ LKJCorr(dim, concentration)
Note that this distribution samples the
Cholesky factor of correlation matrices and not the correlation matrices
themselves and thereby differs slightly from the derivations in [1] for
the `LKJCorr` distribution. For sampling, this uses the Onion method from
[1] Section 3.
Example::
>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
>>> l = LKJCholesky(3, 0.5)
>>> l.sample() # l @ l.T is a sample of a correlation 3x3 matrix
tensor([[ 1.0000, 0.0000, 0.0000],
[ 0.3516, 0.9361, 0.0000],
[-0.1899, 0.4748, 0.8593]])
Args:
dimension (dim): dimension of the matrices
concentration (float or Tensor): concentration/shape parameter of the
distribution (often referred to as eta)
**References**
[1] `Generating random correlation matrices based on vines and extended onion method` (2009),
Daniel Lewandowski, Dorota Kurowicka, Harry Joe.
Journal of Multivariate Analysis. 100. 10.1016/j.jmva.2009.04.008
"""
arg_constraints = {"concentration": constraints.positive}
support = constraints.corr_cholesky
def __init__(self, dim, concentration=1.0, validate_args=None):
if dim < 2:
raise ValueError(
f"Expected dim to be an integer greater than or equal to 2. Found dim={dim}."
)
self.dim = dim
(self.concentration,) = broadcast_all(concentration)
batch_shape = self.concentration.size()
event_shape = torch.Size((dim, dim))
# This is used to draw vectorized samples from the beta distribution in Sec. 3.2 of [1].
marginal_conc = self.concentration + 0.5 * (self.dim - 2)
offset = torch.arange(
self.dim - 1,
dtype=self.concentration.dtype,
device=self.concentration.device,
)
offset = torch.cat([offset.new_zeros((1,)), offset])
beta_conc1 = offset + 0.5
beta_conc0 = marginal_conc.unsqueeze(-1) - 0.5 * offset
self._beta = Beta(beta_conc1, beta_conc0)
super().__init__(batch_shape, event_shape, validate_args)
[docs] def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(LKJCholesky, _instance)
batch_shape = torch.Size(batch_shape)
new.dim = self.dim
new.concentration = self.concentration.expand(batch_shape)
new._beta = self._beta.expand(batch_shape + (self.dim,))
super(LKJCholesky, new).__init__(
batch_shape, self.event_shape, validate_args=False
)
new._validate_args = self._validate_args
return new
[docs] def sample(self, sample_shape=torch.Size()):
# This uses the Onion method, but there are a few differences from [1] Sec. 3.2:
# - This vectorizes the for loop and also works for heterogeneous eta.
# - Same algorithm generalizes to n=1.
# - The procedure is simplified since we are sampling the cholesky factor of
# the correlation matrix instead of the correlation matrix itself. As such,
# we only need to generate `w`.
y = self._beta.sample(sample_shape).unsqueeze(-1)
u_normal = torch.randn(
self._extended_shape(sample_shape), dtype=y.dtype, device=y.device
).tril(-1)
u_hypersphere = u_normal / u_normal.norm(dim=-1, keepdim=True)
# Replace NaNs in first row
u_hypersphere[..., 0, :].fill_(0.0)
w = torch.sqrt(y) * u_hypersphere
# Fill diagonal elements; clamp for numerical stability
eps = torch.finfo(w.dtype).tiny
diag_elems = torch.clamp(1 - torch.sum(w**2, dim=-1), min=eps).sqrt()
w += torch.diag_embed(diag_elems)
return w
[docs] def log_prob(self, value):
# See: https://mc-stan.org/docs/2_25/functions-reference/cholesky-lkj-correlation-distribution.html
# The probability of a correlation matrix is proportional to
# determinant ** (concentration - 1) = prod(L_ii ^ 2(concentration - 1))
# Additionally, the Jacobian of the transformation from Cholesky factor to
# correlation matrix is:
# prod(L_ii ^ (D - i))
# So the probability of a Cholesky factor is propotional to
# prod(L_ii ^ (2 * concentration - 2 + D - i)) = prod(L_ii ^ order_i)
# with order_i = 2 * concentration - 2 + D - i
if self._validate_args:
self._validate_sample(value)
diag_elems = value.diagonal(dim1=-1, dim2=-2)[..., 1:]
order = torch.arange(2, self.dim + 1, device=self.concentration.device)
order = 2 * (self.concentration - 1).unsqueeze(-1) + self.dim - order
unnormalized_log_pdf = torch.sum(order * diag_elems.log(), dim=-1)
# Compute normalization constant (page 1999 of [1])
dm1 = self.dim - 1
alpha = self.concentration + 0.5 * dm1
denominator = torch.lgamma(alpha) * dm1
numerator = torch.mvlgamma(alpha - 0.5, dm1)
# pi_constant in [1] is D * (D - 1) / 4 * log(pi)
# pi_constant in multigammaln is (D - 1) * (D - 2) / 4 * log(pi)
# hence, we need to add a pi_constant = (D - 1) * log(pi) / 2
pi_constant = 0.5 * dm1 * math.log(math.pi)
normalize_term = pi_constant + numerator - denominator
return unnormalized_log_pdf - normalize_term
