def calc_scales(self):
"""
calculate scales for normalized simplex
:return: np.array() - device_delta_limits * norm_coef
"""
self.norm_scales = normscales.normscales(
self.target.mi, self.devices)[0] # 0th element is length scales
for idx, dev in enumerate(self.devices):
if self.norm_scales[idx] is not None:
continue
delta = dev.get_delta()
self.norm_scales[idx] = delta * self.norm_coef
self.norm_scales = np.array(self.norm_scales)
# Randomize the initial steps of simplex - Talk to Joe if it fails
if isinstance(self.minimizer, Simplex):
self.norm_scales *= np.sign(np.random.randn(self.norm_scales.size))
return self.norm_scales
def preprocess(self):
self.target.mi.target = self.target
# assemble hyper parameters
self.length_scales, self.amp_variance, self.single_noise_variance, self.mean_noise_variance, self.precision_matrix = normscales.normscales(
self.target.mi, self.devices, correlationsQ=self.correlationsQ)
# build precision_matrix if not returned
print('Precision before', self.precision_matrix)
if self.precision_matrix is None:
self.covarmat = np.diag(self.length_scales)**2
print('Covariance', self.covarmat)
self.precision_matrix = np.linalg.inv(self.covarmat)
print('Precision', self.precision_matrix)
print('Length Scales', self.length_scales)
# create OnlineGP model
dim = len(self.devices)
hyperparams = (self.precision_matrix, np.log(self.amp_variance),
np.log(self.mean_noise_variance))
# self.model = OGP(dim, hyperparams, maxBV=self.numBV, weighted=False)
self.model = OGP(dim,
hyperparams,
maxBV=self.numBV,
covar=['RBF_ARD', 'MATERN32_ARD', 'MATERN52_ARD'][0],
weighted=False)
# initialize model on prior data if available
if (self.prior_data is not None):
p_X = self.prior_data.iloc[:, :-1]
p_Y = self.prior_data.iloc[:, -1]
num = p_X.shape[0]
self.model.fit(p_X, p_Y, min(self.m, num))
# create Bayesian optimizer
dev_ids = [dev.eid for dev in self.devices]
dev_vals = [dev.get_value() for dev in self.devices]
self.scanner = BayesOpt(
model=self.model,
target_func=self.target,
acq_func=self.acq_func,
xi=self.xi,
alt_param=self.alt_param,
m=self.m,
bounds=self.bounds,
iter_bound=self.iter_bound,
prior_data=self.prior_data,
start_dev_vals=dev_vals,
dev_ids=dev_ids,
searchBoundScaleFactor=self.searchBoundScaleFactor)
self.scanner.max_iter = self.max_iter
self.scanner.opt_ctrl = self.opt_ctrl