#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Bivariate conditioning algorithm for approximating Gaussian probabilities,
see [Genz2016numerical]_ and [Trinh2015bivariate]_.
.. [Trinh2015bivariate]
G. Trinh and A. Genz. Bivariate conditioning approximations for
multivariate normal probabilities. Statistics and Computing, 2015.
.. [Genz2016numerical]
A. Genz and G. Tring. Numerical Computation of Multivariate Normal Probabilities
using Bivariate Conditioning. Monte Carlo and Quasi-Monte Carlo Methods, 2016.
.. [Gibson1994monte]
GJ. Gibson, CA Galsbey, and DA Elston. Monte Carlo evaluation of multivariate normal
integrals and sensitivity to variate ordering. Advances in Numerical Methods and
Applications. 1994.
"""
from __future__ import annotations
from typing import Any, Optional, TypedDict
from warnings import warn
import torch
from botorch.utils.probability.bvn import bvn, bvnmom
from botorch.utils.probability.linalg import (
augment_cholesky,
block_matrix_concat,
PivotedCholesky,
)
from botorch.utils.probability.utils import (
case_dispatcher,
get_constants_like,
ndtr as Phi,
phi,
STANDARDIZED_RANGE,
swap_along_dim_,
)
from botorch.utils.safe_math import log as safe_log, mul as safe_mul
from linear_operator.utils.cholesky import psd_safe_cholesky
from linear_operator.utils.errors import NotPSDError
from torch import LongTensor, Tensor
from torch.nn.functional import pad
[docs]class mvnxpbState(TypedDict):
step: int
perm: LongTensor
bounds: Tensor
piv_chol: PivotedCholesky
plug_ins: Tensor
log_prob: Tensor
log_prob_extra: Optional[Tensor]
[docs]class MVNXPB:
r"""An algorithm for approximating Gaussian probabilities `P(X \in bounds)`, where
`X ~ N(0, covariance_matrix)`.
"""
def __init__(self, covariance_matrix: Tensor, bounds: Tensor) -> None:
r"""Initializes an MVNXPB instance.
Args:
covariance_matrix: Covariance matrices of shape `batch_shape x [n, n]`.
bounds: Tensor of lower and upper bounds, `batch_shape x [n, 2]`. These
bounds are standardized internally and clipped to STANDARDIZED_RANGE.
"""
*batch_shape, _, n = covariance_matrix.shape
device = covariance_matrix.device
dtype = covariance_matrix.dtype
perm = torch.arange(0, n, device=device).expand(*batch_shape, n).contiguous()
# Standardize covariance matrices and bounds
var = covariance_matrix.diagonal(dim1=-2, dim2=-1).unsqueeze(-1)
std = var.sqrt()
istd = var.rsqrt()
matrix = istd * covariance_matrix * istd.transpose(-1, -2)
# Clip first to avoid differentiating through `istd * inf`
bounds = istd * bounds.clip(*(std * lim for lim in STANDARDIZED_RANGE))
# Initialize partial pivoted Cholesky
piv_chol = PivotedCholesky(
step=0,
perm=perm.clone(),
diag=std.squeeze(-1).clone(),
tril=matrix.tril(),
)
self.step = 0
self.perm = perm
self.bounds = bounds
self.piv_chol = piv_chol
self.plug_ins = torch.full(
batch_shape + [n], float("nan"), device=device, dtype=dtype
)
self.log_prob = torch.zeros(batch_shape, device=device, dtype=dtype)
self.log_prob_extra: Optional[Tensor] = None
[docs] @classmethod
def build(
cls,
step: int,
perm: Tensor,
bounds: Tensor,
piv_chol: PivotedCholesky,
plug_ins: Tensor,
log_prob: Tensor,
log_prob_extra: Optional[Tensor] = None,
) -> MVNXPB:
r"""Creates an MVNXPB instance from raw arguments. Unlike MVNXPB.__init__,
this methods does not preprocess or copy terms.
Args:
step: Integer used to track the solver's progress.
bounds: Tensor of lower and upper bounds, `batch_shape x [n, 2]`.
piv_chol: A PivotedCholesky instance for the system.
plug_ins: Tensor of plug-in estimators used to update lower and upper bounds
on random variables that have yet to be integrated out.
log_prob: Tensor of log probabilities.
log_prob_extra: Tensor of conditional log probabilities for the next random
variable. Used when integrating over an odd number of random variables.
"""
new = cls.__new__(cls)
new.step = step
new.perm = perm
new.bounds = bounds
new.piv_chol = piv_chol
new.plug_ins = plug_ins
new.log_prob = log_prob
new.log_prob_extra = log_prob_extra
return new
[docs] def solve(self, num_steps: Optional[int] = None, eps: float = 1e-10) -> Tensor:
r"""Runs the MVNXPB solver instance for a fixed number of steps.
Calculates a bivariate conditional approximation to P(X \in bounds), where
X ~ N(0, Σ). For details, see [Genz2016numerical] or [Trinh2015bivariate]_.
"""
if self.step > self.piv_chol.step:
raise ValueError("Invalid state: solver ran ahead of matrix decomposition.")
# Unpack some terms
start = self.step
bounds = self.bounds
piv_chol = self.piv_chol
L = piv_chol.tril
y = self.plug_ins
# Subtract marginal log probability of final term from previous result if
# it did not fit in a block.
ndim = y.shape[-1]
if ndim > start and start % 2:
self.log_prob = self.log_prob - self.log_prob_extra
self.log_prob_extra = None
# Iteratively compute bivariate conditional approximation
zero = get_constants_like(0, L) # needed when calling `torch.where` below
num_steps = num_steps or ndim - start
for i in range(start, start + num_steps):
should_update_chol = self.step == piv_chol.step
# Determine next pivot element
if should_update_chol:
pivot = self.select_pivot()
else: # pivot using order specified by precomputed pivoted Cholesky step
mask = self.perm[..., i:] == piv_chol.perm[..., i : i + 1]
pivot = i + torch.nonzero(mask, as_tuple=True)[-1]
if pivot is not None and torch.any(pivot > i):
self.pivot_(pivot=pivot)
# Initialize `i`-th plug-in value as univariate conditional expectation
Lii = L[..., i, i].clone()
if should_update_chol:
Lii = Lii.clip(min=0).sqrt()
inv_Lii = Lii.reciprocal()
if i == 0:
lb, ub = bounds[..., i, :].clone().unbind(dim=-1)
else:
db = (L[..., i, :i].clone() * y[..., :i].clone()).sum(-1, keepdim=True)
lb, ub = (bounds[..., i, :].clone() - db).unbind(dim=-1)
Phi_i = Phi(inv_Lii * ub) - Phi(inv_Lii * lb)
small = Phi_i <= i * eps
y[..., i] = case_dispatcher( # used to select next pivot
out=(phi(lb) - phi(ub)) / Phi_i,
cases=( # fallback cases for enhanced numerical stability
(lambda: small & (lb < -9), lambda m: ub[m]),
(lambda: small & (lb > 9), lambda m: lb[m]),
(lambda: small, lambda m: 0.5 * (lb[m] + ub[m])),
),
)
# Maybe finalize the current block
if i and i % 2:
h = i - 1
blk = slice(h, i + 1)
Lhh = L[..., h, h].clone()
Lih = L[..., i, h].clone()
std_i = (Lii.square() + Lih.square()).sqrt()
istds = 1 / torch.stack([Lhh, std_i], -1)
blk_bounds = bounds[..., blk, :].clone()
if i > 1:
blk_bounds = blk_bounds - (
L[..., blk, : i - 1].clone() @ y[..., : i - 1, None].clone()
)
blk_lower, blk_upper = (
pair.unbind(-1) # pair of bounds for `yh` and `yi`
for pair in safe_mul(istds.unsqueeze(-1), blk_bounds).unbind(-1)
)
blk_corr = Lhh * Lih * istds.prod(-1)
blk_prob = bvn(blk_corr, *blk_lower, *blk_upper)
zh, zi = bvnmom(blk_corr, *blk_lower, *blk_upper, p=blk_prob)
# Replace 1D expectations with 2D ones `L[blk, blk]^{-1} y[..., blk]`
mask = blk_prob > zero
y[..., h] = torch.where(mask, zh, zero)
y[..., i] = torch.where(mask, (std_i * zi - Lih * zh) / Lii, zero)
# Update running approximation to log probability
self.log_prob = self.log_prob + safe_log(blk_prob)
self.step += 1
if should_update_chol:
piv_chol.update_(eps=eps)
# Factor in univariate probability if final term fell outside of a block.
if self.step % 2:
self.log_prob_extra = safe_log(Phi_i)
self.log_prob = self.log_prob + self.log_prob_extra
return self.log_prob
[docs] def select_pivot(self) -> Optional[LongTensor]:
r"""GGE variable prioritization strategy from [Gibson1994monte]_.
Returns the index of the random variable least likely to satisfy its bounds
when conditioning on the previously integrated random variables `X[:t - 1]`
attaining the values of plug-in estimators `y[:t - 1]`. Equivalently,
```
argmin_{i = t, ..., n} P(X[i] \in bounds[i] | X[:t-1] = y[:t -1]),
```
where `t` denotes the current step."""
i = self.piv_chol.step
L = self.piv_chol.tril
bounds = self.bounds
if i:
bounds = bounds[..., i:, :] - L[..., i:, :i] @ self.plug_ins[..., :i, None]
inv_stddev = torch.diagonal(L, dim1=-2, dim2=-1)[..., i:].clip(min=0).rsqrt()
probs_1d = Phi(inv_stddev.unsqueeze(-1) * bounds).diff(dim=-1).squeeze(-1)
return i + torch.argmin(probs_1d, dim=-1)
[docs] def pivot_(self, pivot: LongTensor) -> None:
r"""Swap random variables at `pivot` and `step` positions."""
step = self.step
if self.piv_chol.step == step:
self.piv_chol.pivot_(pivot)
elif self.step > self.piv_chol.step:
raise ValueError
for tnsr in (self.perm, self.bounds):
swap_along_dim_(tnsr, i=self.step, j=pivot, dim=pivot.ndim)
def __getitem__(self, key: Any) -> MVNXPB:
return self.build(
step=self.step,
perm=self.perm[key],
bounds=self.bounds[key],
piv_chol=self.piv_chol[key],
plug_ins=self.plug_ins[key],
log_prob=self.log_prob[key],
log_prob_extra=(
None if self.log_prob_extra is None else self.log_prob_extra[key]
),
)
[docs] def concat(self, other: MVNXPB, dim: int) -> MVNXPB:
if not isinstance(other, MVNXPB):
raise TypeError(
f"Expected `other` to be {type(self)} typed but was {type(other)}."
)
batch_ndim = self.log_prob.ndim
if dim > batch_ndim or dim < -batch_ndim:
raise ValueError(f"`dim={dim}` is not a valid batch dimension.")
state_dict = self.asdict()
for key, _other in other.asdict().items():
_self = state_dict.get(key)
if _self is None and _other is None:
continue
if type(_self) != type(_other):
raise TypeError(
f"Concatenation failed: `self.{key}` has type {type(_self)}, "
f"but `other.{key}` is of type {type(_self)}."
)
if isinstance(_self, PivotedCholesky):
state_dict[key] = _self.concat(_other, dim=dim)
elif isinstance(_self, Tensor):
state_dict[key] = torch.concat((_self, _other), dim=dim)
elif _self != _other:
raise ValueError(
f"Concatenation failed: `self.{key}` does not equal `other.{key}`."
)
return self.build(**state_dict)
[docs] def expand(self, *sizes: int) -> MVNXPB:
state_dict = self.asdict()
state_dict["piv_chol"] = state_dict["piv_chol"].expand(*sizes)
for name, ndim in {
"bounds": 2,
"perm": 1,
"plug_ins": 1,
"log_prob": 0,
"log_prob_extra": 0,
}.items():
src = state_dict[name]
if isinstance(src, Tensor):
state_dict[name] = src.expand(
sizes + src.shape[-ndim:] if ndim else sizes
)
return self.build(**state_dict)
[docs] def augment(
self,
covariance_matrix: Tensor,
bounds: Tensor,
cross_covariance_matrix: Tensor,
disable_pivoting: bool = False,
jitter: Optional[float] = None,
max_tries: Optional[int] = None,
) -> MVNXPB:
r"""Augment an `n`-dimensional MVNXPB instance to include `m` additional random
variables.
"""
n = self.perm.shape[-1]
m = covariance_matrix.shape[-1]
if n != self.piv_chol.step:
raise NotImplementedError(
"Augmentation of incomplete solutions not implemented yet."
)
var = covariance_matrix.diagonal(dim1=-2, dim2=-1).unsqueeze(-1)
std = var.sqrt()
istd = var.rsqrt()
Kmn = istd * cross_covariance_matrix
if self.piv_chol.diag is None:
diag = pad(std.squeeze(-1), (cross_covariance_matrix.shape[-1], 0), value=1)
else:
Kmn = Kmn * (1 / self.piv_chol.diag).unsqueeze(-2)
diag = torch.concat([self.piv_chol.diag, std.squeeze(-1)], -1)
# Augment partial pivoted Cholesky factor
Kmm = istd * covariance_matrix * istd.transpose(-1, -2)
Lnn = self.piv_chol.tril
try:
L = augment_cholesky(Laa=Lnn, Kba=Kmn, Kbb=Kmm, jitter=jitter)
except NotPSDError:
warn("Joint covariance matrix not positive definite, attempting recovery.")
Knn = Lnn @ Lnn.transpose(-1, -2)
Knm = Kmn.transpose(-1, -2)
K = block_matrix_concat(blocks=((Knn, Knm), (Kmn, Kmm)))
L = psd_safe_cholesky(K, jitter=jitter, max_tries=max_tries)
if not disable_pivoting:
Lmm = L[..., n:, n:].clone()
L[..., n:, n:] = (Lmm @ Lmm.transpose(-2, -1)).tril()
_bounds = istd * bounds.clip(*(std * lim for lim in STANDARDIZED_RANGE))
_perm = torch.arange(n, n + m, dtype=self.perm.dtype, device=self.perm.device)
_perm = _perm.expand(covariance_matrix.shape[:-2] + (m,))
piv_chol = PivotedCholesky(
step=n + m if disable_pivoting else n,
tril=L.contiguous(),
perm=torch.cat([self.piv_chol.perm, _perm], dim=-1).contiguous(),
diag=diag,
)
return self.build(
step=self.step,
perm=torch.cat([self.perm, _perm], dim=-1),
bounds=torch.cat([self.bounds, _bounds], dim=-2),
piv_chol=piv_chol,
plug_ins=pad(self.plug_ins, (0, m), value=float("nan")),
log_prob=self.log_prob,
log_prob_extra=self.log_prob_extra,
)
[docs] def detach(self) -> MVNXPB:
state_dict = self.asdict()
for key, obj in state_dict.items():
if isinstance(obj, (PivotedCholesky, Tensor)):
state_dict[key] = obj.detach()
return self.build(**state_dict)
[docs] def clone(self) -> MVNXPB:
state_dict = self.asdict()
for key, obj in state_dict.items():
if isinstance(obj, (PivotedCholesky, Tensor)):
state_dict[key] = obj.clone()
return self.build(**state_dict)
[docs] def asdict(self) -> mvnxpbState:
return mvnxpbState(
step=self.step,
perm=self.perm,
bounds=self.bounds,
piv_chol=self.piv_chol,
plug_ins=self.plug_ins,
log_prob=self.log_prob,
log_prob_extra=self.log_prob_extra,
)
