#!/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.
from __future__ import annotations
from typing import Callable, List, Optional, Tuple
import botorch.models.model as model
import torch
from botorch.logging import _get_logger
from botorch.utils.sampling import manual_seed
from torch import Tensor
logger = _get_logger(name="Feasibility")
[docs]
def get_feasible_samples(
samples: Tensor,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
) -> Tuple[Tensor, float]:
r"""
Checks which of the samples satisfy all of the inequality constraints.
Args:
samples: A `sample size x d` size tensor of feature samples,
where d is a feature dimension.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) >= rhs`.
Returns:
2-element tuple containing
- Samples satisfying the linear constraints.
- Estimated proportion of samples satisfying the linear constraints.
"""
if inequality_constraints is None:
return samples, 1.0
nsamples = samples.size(0)
feasible = torch.ones(nsamples, device=samples.device, dtype=torch.bool)
for (indices, coefficients, rhs) in inequality_constraints:
lhs = samples.index_select(1, indices) @ coefficients.to(dtype=samples.dtype)
feasible &= lhs >= rhs
feasible_samples = samples[feasible]
p_linear = feasible_samples.size(0) / nsamples
return feasible_samples, p_linear
[docs]
def get_outcome_feasibility_probability(
model: model.Model,
X: Tensor,
outcome_constraints: List[Callable[[Tensor], Tensor]],
threshold: float = 0.1,
nsample_outcome: int = 1000,
seed: Optional[int] = None,
) -> float:
r"""
Monte Carlo estimate of the feasible volume with respect to the outcome constraints.
Args:
model: The model used for sampling the posterior.
X: A tensor of dimension `batch-shape x 1 x d`, where d is feature dimension.
outcome_constraints: A list of callables, each mapping a Tensor of dimension
`sample_shape x batch-shape x q x m` to a Tensor of dimension
`sample_shape x batch-shape x q`, where negative values imply feasibility.
threshold: A lower limit for the probability of posterior samples feasibility.
nsample_outcome: The number of samples from the model posterior.
seed: The seed for the posterior sampler. If omitted, use a random seed.
Returns:
Estimated proportion of features for which posterior samples satisfy
given outcome constraints with probability above or equal to
the given threshold.
"""
if outcome_constraints is None:
return 1.0
from botorch.sampling.get_sampler import get_sampler
seed = seed if seed is not None else torch.randint(0, 1000000, (1,)).item()
posterior = model.posterior(X) # posterior consists of batch_shape marginals
sampler = get_sampler(
posterior=posterior, sample_shape=torch.Size([nsample_outcome]), seed=seed
)
# size of samples: (num outcome samples, batch_shape, 1, outcome dim)
samples = sampler(posterior)
feasible = torch.ones(samples.shape[:-1], dtype=torch.bool, device=samples.device)
# a sample passes if each constraint applied to the sample
# produces a non-negative tensor
for oc in outcome_constraints:
# broadcasted evaluation of the outcome constraints
feasible &= oc(samples) <= 0
# proportion of feasibile samples for each of the elements of X
# summation is done across feasible outcome samples
p_feas = feasible.sum(0).float() / feasible.size(0)
# proportion of features leading to the posterior outcome
# satisfying the given outcome constraints
# with at probability above a given threshold
p_outcome = (p_feas >= threshold).sum().item() / X.size(0)
return p_outcome
[docs]
def estimate_feasible_volume(
bounds: Tensor,
model: model.Model,
outcome_constraints: List[Callable[[Tensor], Tensor]],
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
nsample_feature: int = 1000,
nsample_outcome: int = 1000,
threshold: float = 0.1,
verbose: bool = False,
seed: Optional[int] = None,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
) -> Tuple[float, float]:
r"""
Monte Carlo estimate of the feasible volume with respect
to feature constraints and outcome constraints.
Args:
bounds: A `2 x d` tensor of lower and upper bounds
for each column of `X`.
model: The model used for sampling the outcomes.
outcome_constraints: A list of callables, each mapping a Tensor of dimension
`sample_shape x batch-shape x q x m` to a Tensor of dimension
`sample_shape x batch-shape x q`, where negative values imply
feasibility.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) >= rhs`.
nsample_feature: The number of feature samples satisfying the bounds.
nsample_outcome: The number of outcome samples from the model posterior.
threshold: A lower limit for the probability of outcome feasibility
seed: The seed for both feature and outcome samplers. If omitted,
use a random seed.
verbose: An indicator for whether to log the results.
Returns:
2-element tuple containing:
- Estimated proportion of volume in feature space that is
feasible wrt the bounds and the inequality constraints (linear).
- Estimated proportion of feasible features for which
posterior samples (outcome) satisfies the outcome constraints
with probability above the given threshold.
"""
seed = seed if seed is not None else torch.randint(0, 1000000, (1,)).item()
with manual_seed(seed=seed):
box_samples = bounds[0] + (bounds[1] - bounds[0]) * torch.rand(
(nsample_feature, bounds.size(1)), dtype=dtype, device=device
)
features, p_feature = get_feasible_samples(
samples=box_samples, inequality_constraints=inequality_constraints
) # each new feature sample is a row
p_outcome = get_outcome_feasibility_probability(
model=model,
X=features.unsqueeze(-2),
outcome_constraints=outcome_constraints,
threshold=threshold,
nsample_outcome=nsample_outcome,
seed=seed,
)
if verbose: # pragma: no cover
logger.info(
"Proportion of volume that satisfies linear constraints: "
+ f"{p_feature:.4e}"
)
if p_feature <= 0.01:
logger.warning(
"The proportion of satisfying volume is very low and may lead to "
+ "very long run times. Consider making your constraints less "
+ "restrictive."
)
logger.info(
"Proportion of linear-feasible volume that also satisfies each "
+ f"outcome constraint with probability > 0.1: {p_outcome:.4e}"
)
if p_outcome <= 0.001:
logger.warning(
"The proportion of volume that also satisfies the outcome constraint "
+ "is very low. Consider making your parameter and outcome constraints "
+ "less restrictive."
)
return p_feature, p_outcome
