def setUp(self):
self.task = SinFunction()
kernel = george.kernels.Matern52Kernel(np.ones([self.task.n_dims]) *
0.01,
ndim=self.task.n_dims)
noise_kernel = george.kernels.WhiteKernel(1e-9, ndim=self.task.n_dims)
kernel = 3000 * (kernel + noise_kernel)
prior = default_priors.TophatPrior(-2, 2)
model = GaussianProcess(kernel, prior=prior)
X = init_random_uniform(self.task.X_lower, self.task.X_upper, 3)
Y = self.task.evaluate(X)
model.train(X, Y, do_optimize=False)
self.acquisition_func = InformationGain(model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
self.acquisition_func.update(model)
def __init__(self,
objective_func,
X_lower,
X_upper,
maximizer="direct",
acquisition="LogEI",
par=None,
n_func_evals=4000,
n_iters=500):
self.objective_func = objective_func
self.X_lower = X_lower
self.X_upper = X_upper
assert self.X_upper.shape[0] == self.X_lower.shape[0]
self.task = Task(self.X_lower, self.X_upper, self.objective_func)
cov_amp = 2
initial_ls = np.ones([self.task.n_dims])
exp_kernel = george.kernels.Matern32Kernel(initial_ls,
ndim=self.task.n_dims)
kernel = cov_amp * exp_kernel
#kernel = GPy.kern.Matern52(input_dim=task.n_dims)
prior = DefaultPrior(len(kernel) + 1)
n_hypers = 3 * len(kernel)
if n_hypers % 2 == 1:
n_hypers += 1
#self.model = GaussianProcessMCMC(kernel, prior=prior, n_hypers=n_hypers, chain_length=500, burnin_steps=100)
self.model = GaussianProcess(kernel,
prior=prior,
dim=self.X_lower.shape[0],
noise=1e-3)
#self.model = GPyModel(kernel)
#MAP ESTMIATE
if acquisition == "EI":
if par is not None:
self.a = EI(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower,
par=par)
else:
self.a = EI(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
elif acquisition == "LogEI":
if par is not None:
self.a = LogEI(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower,
par=par)
else:
self.a = LogEI(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
elif acquisition == "PI":
self.a = PI(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
elif acquisition == "UCB":
if par is not None:
self.a = LCB(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower,
par=par)
else:
self.a = LCB(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
elif acquisition == "UCB_GP":
if par is not None:
self.a = LCB_GP(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower,
par=par)
else:
self.a = LCB_GP(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
elif acquisition == "InformationGain":
self.a = InformationGain(self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
elif acquisition == "InformationGainMC":
self.a = InformationGainMC(
self.model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower,
)
else:
logger.error("ERROR: %s is not a"
"valid acquisition function!" % (acquisition))
return None
#self.acquisition_func = IntegratedAcquisition(self.model, self.a, self.task.X_lower, self.task.X_upper)
self.acquisition_func = self.a
if maximizer == "cmaes":
self.max_fkt = cmaes.CMAES(self.acquisition_func,
self.task.X_lower, self.task.X_upper)
elif maximizer == "direct":
self.max_fkt = direct.Direct(
self.acquisition_func,
self.task.X_lower,
self.task.X_upper,
n_func_evals=n_func_evals,
n_iters=n_iters) #default is n_func_evals=400, n_iters=200
elif maximizer == "stochastic_local_search":
self.max_fkt = stochastic_local_search.StochasticLocalSearch(
self.acquisition_func, self.task.X_lower, self.task.X_upper)
elif maximizer == "grid_search":
self.max_fkt = grid_search.GridSearch(self.acquisition_func,
self.task.X_lower,
self.task.X_upper)
else:
logger.error("ERROR: %s is not a valid function"
"to maximize the acquisition function!" %
(acquisition))
return None
self.bo = BayesianOptimization(acquisition_func=self.acquisition_func,
model=self.model,
maximize_func=self.max_fkt,
task=self.task)
def fmin(objective_func,
X_lower,
X_upper,
num_iterations=30,
maximizer="direct",
acquisition="LogEI",
initX=None,
initY=None):
assert X_upper.shape[0] == X_lower.shape[0]
class Task(BaseTask):
def __init__(self, X_lower, X_upper, objective_fkt):
super(Task, self).__init__(X_lower, X_upper)
self.objective_function = objective_fkt
task = Task(X_lower, X_upper, objective_func)
cov_amp = 2
initial_ls = np.ones([task.n_dims])
exp_kernel = george.kernels.Matern52Kernel(initial_ls, ndim=task.n_dims)
kernel = cov_amp * exp_kernel
prior = DefaultPrior(len(kernel) + 1)
n_hypers = 3 * len(kernel)
if n_hypers % 2 == 1:
n_hypers += 1
model = GaussianProcessMCMC(kernel,
prior=prior,
n_hypers=n_hypers,
chain_length=200,
burnin_steps=100)
if acquisition == "EI":
a = EI(model, X_upper=task.X_upper, X_lower=task.X_lower)
elif acquisition == "LogEI":
a = LogEI(model, X_upper=task.X_upper, X_lower=task.X_lower)
elif acquisition == "PI":
a = PI(model, X_upper=task.X_upper, X_lower=task.X_lower)
elif acquisition == "UCB":
a = LCB(model, X_upper=task.X_upper, X_lower=task.X_lower)
elif acquisition == "InformationGain":
a = InformationGain(model, X_upper=task.X_upper, X_lower=task.X_lower)
elif acquisition == "InformationGainMC":
a = InformationGainMC(
model,
X_upper=task.X_upper,
X_lower=task.X_lower,
)
else:
logger.error("ERROR: %s is not a"
"valid acquisition function!" % (acquisition))
return None
acquisition_func = IntegratedAcquisition(model, a, task.X_lower,
task.X_upper)
if maximizer == "cmaes":
max_fkt = cmaes.CMAES(acquisition_func, task.X_lower, task.X_upper)
elif maximizer == "direct":
max_fkt = direct.Direct(acquisition_func, task.X_lower, task.X_upper)
elif maximizer == "stochastic_local_search":
max_fkt = stochastic_local_search.StochasticLocalSearch(
acquisition_func, task.X_lower, task.X_upper)
elif maximizer == "grid_search":
max_fkt = grid_search.GridSearch(acquisition_func, task.X_lower,
task.X_upper)
else:
logger.error("ERROR: %s is not a valid function"
"to maximize the acquisition function!" % (acquisition))
return None
bo = BayesianOptimization(acquisition_func=acquisition_func,
model=model,
maximize_func=max_fkt,
task=task)
best_x, f_min = bo.run(num_iterations, X=initX, Y=initY)
return task.retransform(best_x), f_min, model, acquisition_func, max_fkt
def test(self):
X_lower = np.array([0])
X_upper = np.array([1])
X = init_random_uniform(X_lower, X_upper, 10)
Y = np.sin(X)
kernel = george.kernels.Matern52Kernel(np.ones([1]), ndim=1)
prior = TophatPrior(-2, 2)
model = GaussianProcess(kernel, prior=prior)
model.train(X, Y)
x_test = init_random_uniform(X_lower, X_upper, 3)
# Shape matching predict
m, v = model.predict(x_test)
assert len(m.shape) == 2
assert m.shape[0] == x_test.shape[0]
assert m.shape[1] == 1
assert len(v.shape) == 2
assert v.shape[0] == x_test.shape[0]
assert v.shape[1] == x_test.shape[0]
#TODO: check gradients
# Shape matching function sampling
x_ = np.linspace(X_lower, X_upper, 10)
x_ = x_[:, np.newaxis]
funcs = model.sample_functions(x_, n_funcs=2)
assert len(funcs.shape) == 2
assert funcs.shape[0] == 2
assert funcs.shape[1] == x_.shape[0]
# Shape matching predict variance
x_test1 = np.array([np.random.rand(1)])
x_test2 = np.random.rand(10)[:, np.newaxis]
var = model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test2.shape[0]
assert var.shape[1] == 1
# Check compatibility with all acquisition functions
acq_func = EI(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = PI(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = LCB(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = InformationGain(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
# Check compatibility with all incumbent estimation methods
rec = BestObservation(model, X_lower, X_upper)
inc, inc_val = rec.estimate_incumbent(None)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanAndStdOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
def test(self):
X_lower = np.array([0])
X_upper = np.array([1])
X = init_random_uniform(X_lower, X_upper, 10)
Y = np.sin(X)
kernel = GPy.kern.Matern52(input_dim=1)
model = GPyModel(kernel)
model.train(X, Y)
x_test = init_random_uniform(X_lower, X_upper, 3)
# Shape matching predict
m, v = model.predict(x_test, full_cov=True)
assert len(m.shape) == 2
assert m.shape[0] == x_test.shape[0]
assert m.shape[1] == 1
assert len(v.shape) == 2
assert v.shape[0] == x_test.shape[0]
assert v.shape[1] == x_test.shape[0]
# Check gradients
dm, dv = model.predictive_gradients(x_test)
assert len(dm.shape) == 2
assert dm.shape[0] == x_test.shape[0]
assert dm.shape[1] == x_test.shape[1]
assert len(dv.shape) == 2
assert dv.shape[0] == x_test.shape[0]
assert dv.shape[1] == 1
# Shape matching function sampling
x_ = np.linspace(X_lower, X_upper, 10)
x_ = x_[:, np.newaxis]
funcs = model.sample_functions(x_, n_funcs=2)
assert len(funcs.shape) == 2
assert funcs.shape[0] == 2
assert funcs.shape[1] == x_.shape[0]
# Shape matching predict variance
x_test2 = np.array([np.random.rand(1)])
x_test1 = np.random.rand(10)[:, np.newaxis]
var = model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test1.shape[0]
assert var.shape[1] == 1
# Check compatibility with all acquisition functions
acq_func = EI(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = PI(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = LCB(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = InformationGain(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
# Check compatibility with all incumbent estimation methods
rec = BestObservation(model, X_lower, X_upper)
inc, inc_val = rec.estimate_incumbent(None)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanAndStdOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1