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bocs.py
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bocs.py
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# Copyright 2024 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
"""Bayesian Optimization of Combinatorial Structures (BOCS) from https://arxiv.org/abs/1806.08838.
Code is a cleaned-up exact version from /BOCSpy/ in
https://github.com/baptistar/BOCS.
"""
# pylint:disable=invalid-name
import abc
import itertools
from typing import Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cvxpy as cvx
import numpy as np
from vizier import algorithms as vza
from vizier import pyvizier as vz
from vizier._src.algorithms.designers import random
FloatType = Union[float, np.float32, np.float64]
class _BayesianHorseshoeLinearRegression:
"""Computes conditional poster parameter distributions from a sparsity-inducing prior."""
def _fastmvg(self, Phi: np.ndarray, alpha: np.ndarray,
D: np.ndarray) -> np.ndarray:
"""Fast sampler for multivariate Gaussian distributions (large p, p > n) of the form N(mu, S).
We have:
mu = S Phi' y
S = inv(Phi'Phi + inv(D))
Reference: https://arxiv.org/abs/1506.04778
Args:
Phi:
alpha:
D:
Returns:
"""
n, p = Phi.shape
d = np.diag(D)
u = np.random.randn(p) * np.sqrt(d)
delta = np.random.randn(n)
v = np.dot(Phi, u) + delta
mult_vector = np.vectorize(np.multiply)
Dpt = mult_vector(Phi.T, d[:, np.newaxis])
w = np.linalg.solve(np.matmul(Phi, Dpt) + np.eye(n), alpha - v)
return u + np.dot(Dpt, w)
def _fastmvg_rue(self, Phi: np.ndarray, PtP: np.ndarray, alpha: np.ndarray,
D: np.ndarray) -> np.ndarray:
"""Another sampler for multivariate Gaussians (small p) of the form N(mu, S).
We have:
mu = S Phi' y
S = inv(Phi'Phi + inv(D))
Here, PtP = Phi'*Phi (X'X is precomputed).
Reference: https://www.jstor.org/stable/2680602
Args:
Phi:
PtP:
alpha:
D:
Returns:
"""
p = Phi.shape[1]
Dinv = np.diag(1. / np.diag(D))
# Regularize PtP + Dinv matrix for small negative eigenvalues.
try:
L = np.linalg.cholesky(PtP + Dinv)
except np.linalg.LinAlgError:
mat = PtP + Dinv
Smat = (mat + mat.T) / 2.
maxEig_Smat = np.max(np.linalg.eigvals(Smat))
L = np.linalg.cholesky(Smat + maxEig_Smat * 1e-15 * np.eye(Smat.shape[0]))
v = np.linalg.solve(L, np.dot(Phi.T, alpha))
m = np.linalg.solve(L.T, v)
w = np.linalg.solve(L.T, np.random.randn(p))
return m + w
def regress(
self,
X: np.ndarray,
Y: np.ndarray,
nsamples: int = 1000,
burnin: int = 0,
thin: int = 1
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Implementation of the Bayesian horseshoe linear regression hierarchy.
References:
https://arxiv.org/abs/1508.03884
https://www.jstor.org/stable/25734098
(c) Copyright Enes Makalic and Daniel F. Schmidt, 2015
Adapted to python by Ricardo Baptista, 2018
Args:
X: regressor matrix [n x p]
Y: response vector [n x 1]
nsamples: number of samples for the Gibbs sampler (nsamples > 0)
burnin: number of burnin (burnin >= 0)
thin: thinning (thin >= 1)
Returns:
beta: regression parameters [p x nsamples]
b0: regression param. for constant [1 x nsamples]
s2: noise variance sigma^2 [1 x nsamples]
t2: hypervariance tau^2 [1 x nsamples]
l2: hypervariance lambda^2 [p x nsamples]
"""
n, p = X.shape
# Standardize y's
muY = np.mean(Y)
Y = Y - muY
# Return values
beta = np.zeros((p, nsamples))
s2 = np.zeros((1, nsamples))
t2 = np.zeros((1, nsamples))
l2 = np.zeros((p, nsamples))
# Initial values
sigma2 = 1.
lambda2 = np.random.uniform(size=p)
tau2 = 1.
nu = np.ones(p)
xi = 1.
# pre-compute X'*X (used with fastmvg_rue)
XtX = np.matmul(X.T, X)
# Gibbs sampler
k = 0
iter_count = 0
while k < nsamples:
# Sample from the conditional posterior distribution
sigma = np.sqrt(sigma2)
lambda_star = tau2 * np.diag(lambda2)
# Determine best sampler for conditional posterior of beta's
if (p > n) and (p > 200):
b = self._fastmvg(X / sigma, Y / sigma, sigma2 * lambda_star)
else:
b = self._fastmvg_rue(X / sigma, XtX / sigma2, Y / sigma,
sigma2 * lambda_star)
# Sample sigma2
e = Y - np.dot(X, b)
shape = (n + p) / 2.
scale = np.dot(e.T, e) / 2. + np.sum(b**2 / lambda2) / tau2 / 2.
sigma2 = 1. / np.random.gamma(shape, 1. / scale)
# Sample lambda2
scale = 1. / nu + b**2. / 2. / tau2 / sigma2
lambda2 = 1. / np.random.exponential(1. / scale)
# Sample tau2
shape = (p + 1.) / 2.
scale = 1. / xi + np.sum(b**2. / lambda2) / 2. / sigma2
tau2 = 1. / np.random.gamma(shape, 1. / scale)
# Sample nu
scale = 1. + 1. / lambda2
nu = 1. / np.random.exponential(1. / scale)
# Sample xi
scale = 1. + 1. / tau2
xi = 1. / np.random.exponential(1. / scale)
# Store samples
iter_count += 1
if iter_count > burnin:
# thinning
if (iter_count % thin) == 0:
beta[:, k] = b
s2[:, k] = sigma2
t2[:, k] = tau2
l2[:, k] = lambda2
k = k + 1
b0 = muY
return beta, b0, s2, t2, l2
class _GibbsLinearRegressor:
"""Stateful Gibbs sampler."""
def __init__(self, order: int, num_gibbs_retries: int = 10):
self._order = order
self._num_gibbs_retries = num_gibbs_retries
# Below are stateful attributes calulated after `regress()`.
self._alpha: Optional[np.ndarray] = None
self._num_vars: Optional[int] = None
self._X_inf: Optional[np.ndarray] = None
self._y_inf: Optional[np.ndarray] = None
def _preprocess(
self,
X: np.ndarray,
Y: np.ndarray,
inf_threshold: float = 1e6
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Preprocess data to ensure unique points and remove outliers."""
# Limit data to unique points.
unique_X, X_idx = np.unique(X, axis=0, return_index=True)
unique_Y = Y[X_idx]
# separate samples based on Inf output
Y_Infidx = np.where(np.abs(unique_Y) > inf_threshold)[0]
Y_nInfidx = np.setdiff1d(np.arange(len(unique_Y)), Y_Infidx)
X_train = unique_X[Y_nInfidx, :]
Y_train = unique_Y[Y_nInfidx]
# Large-value outliers.
X_inf = X[Y_Infidx, :]
Y_inf = Y[Y_Infidx]
return X_train, Y_train, X_inf, Y_inf
def regress(self, X: np.ndarray, Y: np.ndarray) -> None:
"""Computes and saves alpha (regressor coefficients from the data."""
# Preprocess data for training and store relevant data.
X_train, Y_train, self._X_inf, self._Y_inf = self._preprocess(X, Y)
self._num_vars = X_train.shape[1]
# Create matrix with all covariates based on order.
X_train = self._order_effects(X_train)
(nSamps, nCoeffs) = X_train.shape
# Check if X_train contains columns w/ zeros and find corresponding indices.
check_zero = np.all(X_train == np.zeros((nSamps, 1)), axis=0)
idx_nnzero = np.where(check_zero == False)[0] # pylint:disable=singleton-comparison,g-explicit-bool-comparison
# Remove columns of zeros in X_train.
if np.any(check_zero):
X_train = X_train[:, idx_nnzero]
# Run Gibbs sampler.
bhs = _BayesianHorseshoeLinearRegression()
counter = 0
while counter < self._num_gibbs_retries:
# re-run if there is an error during sampling
counter += 1
try:
alphaGibbs, a0, _, _, _ = bhs.regress(X_train, Y_train)
# run until alpha matrix does not contain any NaNs
if not np.isnan(alphaGibbs).any():
break
except np.linalg.LinAlgError:
logging.error('Numerical error during Gibbs sampling. Trying again.')
continue
if counter >= self._num_gibbs_retries:
raise ValueError('Gibbs sampling failed for %d tries.' %
self._num_gibbs_retries)
# append zeros back - note alpha(1,:) is linear intercept
alpha_pad = np.zeros(nCoeffs)
alpha_pad[idx_nnzero] = alphaGibbs[:, -1]
self._alpha = np.append(a0, alpha_pad)
@property
def alpha(self) -> np.ndarray:
if self._alpha is None:
raise ValueError('You first need to call `regress()` on the data.')
return self._alpha
@property
def num_vars(self) -> int:
if self._num_vars is None:
raise ValueError('You first need to call `regress()` on the data.')
return self._num_vars
def surrogate_model(self, x: np.ndarray) -> FloatType:
"""Surrogate model.
Args:
x: Should only contain one row.
Returns:
Surrogate objective.
"""
# Generate x_all (all basis vectors) based on model order.
x_all = np.append(1, self._order_effects(x))
# check if previous x led to Inf output (if so, barrier=Inf)
barrier = 0.
if self._X_inf.shape[0] != 0 and np.equal(x, self._X_inf).all(axis=1).any():
barrier = np.inf
return np.dot(x_all, self._alpha) + barrier
def _order_effects(self, X: np.ndarray) -> np.ndarray:
"""Function computes data matrix for all coupling."""
# Find number of variables
n_samp, n_vars = X.shape
# Generate matrix to store results
x_allpairs = X
for ord_i in range(2, self._order + 1):
# generate all combinations of indices (without diagonals)
offdProd = np.array(
list(itertools.combinations(np.arange(n_vars), ord_i)))
# generate products of input variables
x_comb = np.zeros((n_samp, offdProd.shape[0], ord_i))
for j in range(ord_i):
x_comb[:, :, j] = X[:, offdProd[:, j]]
x_allpairs = np.append(x_allpairs, np.prod(x_comb, axis=2), axis=1)
return x_allpairs
class AcquisitionOptimizer(abc.ABC):
"""Base class for BOCS acquisition optimizers."""
def __init__(self, lin_reg: _GibbsLinearRegressor, lamda: float = 1e-4):
self._lin_reg = lin_reg
self._num_vars = self._lin_reg.num_vars
self._lamda = lamda
@abc.abstractmethod
def argmin(self) -> np.ndarray:
"""Computes argmin using the regressor."""
pass
class SimulatedAnnealing(AcquisitionOptimizer):
"""Simulated Annealing solver."""
def __init__(self,
lin_reg: _GibbsLinearRegressor,
lamda: float = 1e-4,
num_iters: int = 10,
num_reruns: int = 5,
initial_temp: float = 1.0,
annealing_factor: float = 0.8):
super().__init__(lin_reg=lin_reg, lamda=lamda)
self._num_iters = num_iters
self._num_reruns = num_reruns
self._initial_temp = initial_temp
self._annealing_factor = annealing_factor
def argmin(self) -> np.ndarray:
"""Computes argmin via multiple rounds of Simulated Annealing."""
SA_model = np.zeros((self._num_reruns, self._num_vars))
SA_obj = np.zeros(self._num_reruns)
penalty = lambda x: self._lamda * np.sum(x, axis=1)
acquisition_fn = lambda x: self._lin_reg.surrogate_model(x) + penalty(x)
for j in range(self._num_reruns):
optModel, objVals = self._optimization_loop(acquisition_fn)
SA_model[j, :] = optModel[-1, :]
SA_obj[j] = objVals[-1]
# Find optimal solution
min_idx = np.argmin(SA_obj)
x_new = SA_model[min_idx, :]
return x_new
def _optimization_loop(
self, objective: Callable[[np.ndarray], FloatType]
) -> Tuple[np.ndarray, np.ndarray]:
"""Single optimization round of Simulated Annealing."""
# Declare vectors to save solutions
model_iter = np.zeros((self._num_iters, self._num_vars))
obj_iter = np.zeros(self._num_iters)
# Set initial temperature and cooling schedule
T = self._initial_temp
cool = lambda t: self._annealing_factor * t
# Set initial condition and evaluate objective
old_x = np.zeros((1, self._num_vars))
old_obj = objective(old_x)
# Set best_x and best_obj
best_x = old_x
best_obj = old_obj
# Run simulated annealing
for t in range(self._num_iters):
# Decrease T according to cooling schedule.
T = cool(T)
# Find new sample
flip_bit = np.random.randint(self._num_vars)
new_x = old_x.copy()
new_x[0, flip_bit] = 1. - new_x[0, flip_bit]
# Evaluate objective function.
new_obj = objective(new_x)
# Update current solution iterate.
if (new_obj < old_obj) or (np.random.rand() < np.exp(
(old_obj - new_obj) / T)):
old_x = new_x
old_obj = new_obj
# Update best solution
if new_obj < best_obj:
best_x = new_x
best_obj = new_obj
# Save solution
model_iter[t, :] = best_x
obj_iter[t] = best_obj
return model_iter, obj_iter
class SemiDefiniteProgramming(AcquisitionOptimizer):
"""SDP solver for quadratic acquisition functions."""
def __init__(self,
lin_reg: _GibbsLinearRegressor,
lamda: float = 1e-4,
num_repeats: int = 100):
super().__init__(lin_reg=lin_reg, lamda=lamda)
self._num_repeats = num_repeats
def argmin(self) -> np.ndarray:
"""Perform SDP over the quadratic xt*A*x + bt*x.
(A,b) is recovered from alpha.
Returns:
Argmin of the SDP problem.
"""
alpha = self._lin_reg.alpha
# Extract vector of coefficients
b = alpha[1:self._num_vars + 1] + self._lamda
a = alpha[self._num_vars + 1:]
# Get indices for quadratic terms.
idx_prod = np.array(
list(itertools.combinations(np.arange(self._num_vars), 2)))
n_idx = idx_prod.shape[0]
# Check number of coefficients
if a.size != n_idx:
raise ValueError('Number of Coefficients does not match indices!')
# Convert a to matrix form
A = np.zeros((self._num_vars, self._num_vars))
for i in range(n_idx):
A[idx_prod[i, 0], idx_prod[i, 1]] = a[i] / 2.
A[idx_prod[i, 1], idx_prod[i, 0]] = a[i] / 2.
# Convert to standard form.
bt = b / 2. + np.dot(A, np.ones(self._num_vars)) / 2.
bt = bt.reshape((self._num_vars, 1))
At = np.vstack((np.append(A / 4., bt / 2., axis=1), np.append(bt.T, 2.)))
# Run SDP relaxation.
X = cvx.Variable((self._num_vars + 1, self._num_vars + 1), PSD=True)
obj = cvx.Minimize(cvx.trace(cvx.matmul(At, X)))
constraints = [cvx.diag(X) == np.ones(self._num_vars + 1)]
prob = cvx.Problem(obj, constraints)
prob.solve(solver=cvx.CVXOPT)
# Extract vectors and compute Cholesky.
try:
L = np.linalg.cholesky(X.value)
except np.linalg.LinAlgError:
XpI = X.value + 1e-15 * np.eye(self._num_vars + 1)
L = np.linalg.cholesky(XpI)
suggest_vect = np.zeros((self._num_vars, self._num_repeats))
obj_vect = np.zeros(self._num_repeats)
for kk in range(self._num_repeats):
# Generate a random cutting plane vector (uniformly distributed on the
# unit sphere - normalized vector)
r = np.random.randn(self._num_vars + 1)
r = r / np.linalg.norm(r)
y_soln = np.sign(np.dot(L.T, r))
# Convert solution to original domain and assign to output vector.
suggest_vect[:, kk] = (y_soln[:self._num_vars] + 1.) / 2.
obj_vect[kk] = np.dot(
np.dot(suggest_vect[:, kk].T, A), suggest_vect[:, kk]) + np.dot(
b, suggest_vect[:, kk])
# Find optimal rounded solution.
opt_idx = np.argmin(obj_vect)
return suggest_vect[:, opt_idx]
AcqusitionOptimizerFactory = Callable[[_GibbsLinearRegressor, float],
AcquisitionOptimizer]
class BOCSDesigner(vza.Designer):
"""BOCS Designer."""
def __init__(self,
problem_statement: vz.ProblemStatement,
order: int = 2,
acquisition_optimizer_factory:
AcqusitionOptimizerFactory = SemiDefiniteProgramming,
lamda: float = 1e-4,
num_initial_randoms: int = 10):
"""Init.
Args:
problem_statement: Must use a boolean search space.
order: Statistical model order.
acquisition_optimizer_factory: Which acquisition optimizer to use.
lamda: Sparsity regularization coefficient.
num_initial_randoms: Number of initial random suggestions for seeding the
model.
"""
if problem_statement.search_space.is_conditional:
raise ValueError(
f'This designer {self} does not support conditional search.')
for p_config in problem_statement.search_space.parameters:
if p_config.external_type != vz.ExternalType.BOOLEAN:
raise ValueError('Only boolean search spaces are supported.')
self._problem_statement = problem_statement
self._metric_name = self._problem_statement.metric_information.item().name
self._search_space = problem_statement.search_space
self._current_index = 0
self._order = order
self._acquisition_optimizer_factory = acquisition_optimizer_factory
self._lamda = lamda
self._num_initial_randoms = num_initial_randoms
self._trials = []
def update(
self, completed: vza.CompletedTrials, all_active: vza.ActiveTrials
) -> None:
self._trials += tuple(completed.trials)
def suggest(self,
count: Optional[int] = None) -> Sequence[vz.TrialSuggestion]:
"""Core BOCS method.
Initially will use random search for the first `num_initial_randoms`
suggestions, and then will use SDP or Simulated Annealing for acqusition
minimization.
Args:
count: Makes best effort to generate this many suggestions. If None,
suggests as many as the algorithm wants.
Returns:
New suggestions.
"""
count = count or 1
if count > 1:
raise ValueError('This designer does not support batched suggestions.')
if len(self._trials) < self._num_initial_randoms:
random_designer = random.RandomDesigner(self._search_space)
return random_designer.suggest(count)
X = []
Y = []
for t in self._trials:
single_x = [
float(t.parameters.get_value(p.name) == 'True')
for p in self._search_space.parameters
]
single_y = t.final_measurement.metrics[self._metric_name].value
X.append(single_x)
Y.append(single_y)
X = np.array(X)
Y = np.array(Y)
if self._problem_statement.metric_information.item(
).goal == vz.ObjectiveMetricGoal.MAXIMIZE:
Y = -Y
# Train initial statistical model
lin_reg = _GibbsLinearRegressor(self._order)
lin_reg.regress(X, Y)
# Run acquisition optimization.
optimizer = self._acquisition_optimizer_factory(lin_reg, self._lamda)
x_new = optimizer.argmin()
parameters = vz.ParameterDict()
for i, p in enumerate(self._search_space.parameters):
parameters[p.name] = 'True' if x_new[i] == 1.0 else 'False'
return [vz.TrialSuggestion(parameters=parameters)]