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-2 is the default value when no feasible has been found
import matplotlib.pyplot as plt
ai
View sourceimport warnings
import botorch
import gpytorch
import matplotlib.pyplot as plt
import numpy as np
import torch
from tqdm.notebook import tqdm
from bo.objectives import forrester
from bo.train import fit_gp_model
def objective(x):
return forrester(x)
def cost(x):
return -(0.1 * objective(x) + objective(x - 4)) / 3 + x / 3 - 0.5
lb = -5
ub = +5
bounds = torch.tensor([[lb], [ub]], dtype=torch.float)
xs = torch.linspace(lb, ub, 201)
objective_ys = objective(xs)
cost_ys = cost(xs)
plt.plot(xs, objective_ys, label="objective", c="r")
plt.plot(xs, cost_ys, linestyle="--", label="cost", c="k")
plt.legend();
class GPModel(gpytorch.models.ExactGP, botorch.models.gpytorch.GPyTorchModel):
_num_outputs = 1
def __init__(self, train_x, train_y, likelihood):
super().__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.MaternKernel(nu=2.5, ard_num_dims=1)
)
def forward(self, x):
mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
num_queries = 10
num_repeats = 10
strategy = "ei" # "cei" or "ei"
strategy = strategy.upper()
# -2 is the default value when no feasible has been found
default_value = -2
feasible_incumbents = torch.ones((num_repeats, num_queries)) * default_value
for trial in range(num_repeats):
print("trial", trial)
torch.manual_seed(trial)
train_x = bounds[0] + (bounds[1] - bounds[0]) * torch.rand(1, 1)
train_utility = objective(train_x)
train_cost = cost(train_x)
for i in tqdm(range(num_queries)):
feasible_flag = (train_cost <= 0).any()
if feasible_flag:
feasible_incumbents[trial, i] = train_utility[train_cost <= 0].max()
utility_likelihood = gpytorch.likelihoods.GaussianLikelihood()
utility_model = GPModel(train_x, train_utility.squeeze(-1), utility_likelihood)
utility_model.likelihood.noise = 1e-4
fit_gp_model(utility_model, utility_likelihood, train_x, train_utility.squeeze(-1))
cost_likelihood = gpytorch.likelihoods.GaussianLikelihood()
cost_model = GPModel(train_x, train_cost.squeeze(-1), cost_likelihood)
cost_model.likelihood.noise = 1e-4
fit_gp_model(cost_model, cost_likelihood, train_x, train_cost.squeeze(-1))
if feasible_flag:
best_f = train_utility[train_cost <= 0].max()
else:
best_f = torch.tensor(default_value)
if strategy == "EI":
policy = botorch.acquisition.analytic.LogExpectedImprovement(
model=utility_model, best_f=train_utility.max()
)
if strategy == "CEI":
policy = botorch.acquisition.analytic.LogConstrainedExpectedImprovement(
model=botorch.models.model_list_gp_regression.ModelListGP(
utility_model, cost_model
),
best_f=best_f,
objective_index=0,
constraints={1: [None, 0]},
)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
next_x, acq_val = botorch.optim.optimize_acqf(
policy, bounds=bounds, q=1, num_restarts=40, raw_samples=100
)
next_utility = objective(next_x)
next_cost = cost(next_x)
train_x = torch.cat([train_x, next_x])
train_utility = torch.cat([train_utility, next_utility])
train_cost = torch.cat([train_cost, next_cost])
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!mkdir forrester
torch.save(feasible_incumbents, f"./forrester/{strategy}.pth")
mkdir: forrester: File exists
def show_agg_progress(path, name, linestyle="solid"):
def ci(y):
return 2 * y.std(axis=0) / np.sqrt(num_repeats)
incumbents = torch.load(path)
avg_incumbent = incumbents.mean(axis=0)
ci_incumbent = ci(incumbents)
plt.plot(avg_incumbent, linestyle=linestyle, label=name)
plt.fill_between(
np.arange(num_queries),
avg_incumbent + ci_incumbent,
avg_incumbent - ci_incumbent,
alpha=0.1,
)
plt.figure(figsize=(8, 6))
show_agg_progress(
"./forrester/CEI.pth",
"constrained EI",
)
show_agg_progress("./forrester/EI.pth", "EI", linestyle="--")
plt.axhline(
objective_ys[cost_ys <= 0].max(),
c="k",
linestyle=":",
label="feasible best",
)
plt.xlabel("# queries", fontsize=15)
plt.ylabel("accuracy", fontsize=15)
plt.legend(fontsize=15);
ei_incumbents = torch.load("./forrester/EI.pth")
cei_incumbents = torch.load("./forrester/CEI.pth")
torch.set_printoptions(precision=1)
print(ei_incumbents)
tensor([[ 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
[-2.0, 2.7, 2.7, 3.3, 4.1, 4.2, 4.2, 4.2, 4.2, 4.2],
[ 1.8, 1.8, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2],
[ 4.1, 4.1, 4.1, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2],
[ 1.0, 1.6, 4.0, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2],
[-2.0, 1.9, 3.5, 4.1, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2],
[ 1.2, 1.2, 1.9, 1.9, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2],
[ 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
[ 1.6, 1.6, 2.1, 2.1, 2.1, 2.1, 2.2, 2.2, 4.2, 4.2],
[ 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2]])
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