def test_branin(self):
branin = Branin()
# Check batch computation
n_points = 10
X = np.random.rand(n_points, branin.n_dims)
X[:, 0] = X[:, 0].dot(branin.X_upper[0] - branin.X_lower[0]) + branin.X_lower[0]
X[:, 1] = X[:, 1].dot(branin.X_upper[1] - branin.X_lower[1]) + branin.X_lower[1]
y = branin.evaluate(X)
assert len(y.shape) == 2
assert y.shape[0] == n_points
assert y.shape[1] == 1
# Check single computation
X = np.array([np.random.rand(branin.n_dims)])
X[:, 0] = X[:, 0].dot(branin.X_upper[0] - branin.X_lower[0]) + branin.X_lower[0]
X[:, 1] = X[:, 1].dot(branin.X_upper[1] - branin.X_lower[1]) + branin.X_lower[1]
y = branin.evaluate(X)
assert y.shape[0] == 1
# Check optimas
X = branin.opt
y = branin.evaluate(X)
assert np.all(np.round(y, 6) == np.array([branin.fopt]))
class BraninInBillionDims(REMBO):
def __init__(self):
self.b = Branin()
X_lower = np.concatenate((self.b.X_lower, np.zeros([999998])))
X_upper = np.concatenate((self.b.X_upper, np.ones([999998])))
super(BraninInBillionDims, self).__init__(X_lower, X_upper, d=2)
def objective_function(self, x):
return self.b.objective_function(x[:, :2])
def setUp(self):
self.branin = Branin()
n_points = 5
rng = np.random.RandomState(42)
self.X = init_random_uniform(self.branin.X_lower,
self.branin.X_upper,
n_points,
rng=rng)
self.Y = self.branin.evaluate(self.X)
kernel = GPy.kern.Matern52(input_dim=self.branin.n_dims)
self.model = GPyModel(kernel,
optimize=True,
noise_variance=1e-4,
num_restarts=10)
self.model.train(self.X, self.Y)
self.acquisition_func = EI(self.model,
X_upper=self.branin.X_upper,
X_lower=self.branin.X_lower,
par=0.1)
def setUp(self):
self.branin = Branin()
n_points = 5
rng = np.random.RandomState(42)
self.X = init_random_uniform(self.branin.X_lower,
self.branin.X_upper,
n_points,
rng=rng)
self.Y = self.branin.evaluate(self.X)
kernel = GPy.kern.Matern52(input_dim=self.branin.n_dims)
self.model = GPyModel(kernel, optimize=True,
noise_variance=1e-4,
num_restarts=10)
self.model.train(self.X, self.Y)
self.acquisition_func = EI(self.model,
X_upper=self.branin.X_upper,
X_lower=self.branin.X_lower,
par=0.1)
class TestMaximizers2D(unittest.TestCase):
def setUp(self):
self.branin = Branin()
n_points = 5
rng = np.random.RandomState(42)
self.X = init_random_uniform(self.branin.X_lower,
self.branin.X_upper,
n_points,
rng=rng)
self.Y = self.branin.evaluate(self.X)
kernel = GPy.kern.Matern52(input_dim=self.branin.n_dims)
self.model = GPyModel(kernel, optimize=True,
noise_variance=1e-4,
num_restarts=10)
self.model.train(self.X, self.Y)
self.acquisition_func = EI(self.model,
X_upper=self.branin.X_upper,
X_lower=self.branin.X_lower,
par=0.1)
def test_direct(self):
maximizer = Direct(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_stochastic_local_search(self):
maximizer = StochasticLocalSearch(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_cmaes(self):
maximizer = CMAES(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
Created on Jun 23, 2015
@author: Aaron Klein
'''
from robo.models.random_forest import RandomForest
from robo.acquisition.ei import EI
#from robo.maximizers.direct import Direct
from robo.maximizers.cmaes import CMAES
from robo.task.synthetic_functions.branin import Branin
from robo.solver.bayesian_optimization import BayesianOptimization
from robo.incumbent.posterior_optimization import PosteriorMeanAndStdOptimization
# Specifies the task object that defines the objective functions and
# the bounds of the input space
branin = Branin()
# Instantiate the random forest. Branin does not have any categorical
# values thus we pass a np.zero vector here.
model = RandomForest(branin.types)
# Define the acquisition function
acquisition_func = EI(model,
X_upper=branin.X_upper,
X_lower=branin.X_lower,
par=0.1)
# Strategy of estimating the incumbent
rec = PosteriorMeanAndStdOptimization(model,
branin.X_lower,
branin.X_upper,
ls_sample = np.array([
self.tophat.sample_from_prior(n_samples)[:, 0]
for _ in range(1, (self.n_dims - 1))
]).T
p0[:, 1:(self.n_dims - 1)] = ls_sample
# Noise
p0[:, -1] = self.horseshoe.sample_from_prior(n_samples)[:, 0]
return p0
burnin = 100
chain_length = 200
n_hypers = 20
task = Branin()
cov_amp = 1.0
config_kernel = george.kernels.Matern52Kernel(np.ones([task.n_dims]),
ndim=task.n_dims)
kernel = cov_amp * config_kernel
prior = MyPrior(len(kernel) + 1)
model = GaussianProcessMCMC(kernel,
prior=prior,
burnin=burnin,
chain_length=chain_length,
n_hypers=n_hypers)
class TestMaximizers2D(unittest.TestCase):
def setUp(self):
self.branin = Branin()
n_points = 5
rng = np.random.RandomState(42)
self.X = init_random_uniform(self.branin.X_lower,
self.branin.X_upper,
n_points,
rng=rng)
self.Y = self.branin.evaluate(self.X)
kernel = GPy.kern.Matern52(input_dim=self.branin.n_dims)
self.model = GPyModel(kernel,
optimize=True,
noise_variance=1e-4,
num_restarts=10)
self.model.train(self.X, self.Y)
self.acquisition_func = EI(self.model,
X_upper=self.branin.X_upper,
X_lower=self.branin.X_lower,
par=0.1)
def test_direct(self):
maximizer = Direct(self.acquisition_func, self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_stochastic_local_search(self):
maximizer = StochasticLocalSearch(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_cmaes(self):
maximizer = CMAES(self.acquisition_func, self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def __init__(self):
self.b = Branin()
X_lower = np.concatenate((self.b.X_lower, np.zeros([999998])))
X_upper = np.concatenate((self.b.X_upper, np.ones([999998])))
super(BraninInBillionDims, self).__init__(X_lower, X_upper, d=2)