def _optimize(self, fun_negative_acquisition, str_initial_method,
int_samples):
list_next_point = []
if self.str_optimizer_method_bo == 'L-BFGS-B':
list_bounds = self._get_bounds()
arr_initials = self.get_initial(
str_initial_method,
fun_objective=fun_negative_acquisition,
int_samples=int_samples)
for arr_initial in arr_initials:
next_point = minimize(fun_negative_acquisition,
x0=arr_initial,
bounds=list_bounds,
method=self.str_optimizer_method_bo,
options={'disp': False})
next_point_x = next_point.x
list_next_point.append(next_point_x)
if self.debug:
print(
'[DEBUG] _optimize in bo.py: optimized point for acq',
next_point_x)
elif self.str_optimizer_method_bo == 'DIRECT': # pragma: no cover
list_bounds = self._get_bounds()
next_point = directminimize(
fun_negative_acquisition,
bounds=list_bounds,
)
next_point_x = next_point.x
list_next_point.append(next_point_x)
elif self.str_optimizer_method_bo == 'CMA-ES':
list_bounds = self._get_bounds()
list_bounds = np.array(list_bounds)
def fun_wrapper(f):
def g(bx):
return f(bx)[0]
return g
arr_initials = self.get_initial(
str_initial_method,
fun_objective=fun_negative_acquisition,
int_samples=1)
next_point_x = cma.fmin(fun_wrapper(fun_negative_acquisition),
arr_initials[0],
0.5,
options={
'bounds':
[list_bounds[:, 0], list_bounds[:, 1]],
'verbose':
-1
})[0]
list_next_point.append(next_point_x)
next_points = np.array(list_next_point)
next_point = get_best_acquisition(next_points,
fun_negative_acquisition)
return next_point.flatten(), next_points
def _optimize(self, fun_negative_acquisition: constants.TYPING_CALLABLE,
str_sampling_method: str,
num_samples: int) -> constants.TYPING_TUPLE_TWO_ARRAYS:
"""
It optimizes `fun_negative_function` with `self.str_optimizer_method_bo`.
`num_samples` examples are determined by `str_sampling_method`, to
start acquisition function optimization.
:param fun_objective: negative acquisition function.
:type fun_objective: callable
:param str_sampling_method: the name of sampling method.
:type str_sampling_method: str.
:param num_samples: the number of samples.
:type num_samples: int.
:returns: tuple of next point to evaluate and all candidates
determined by acquisition function optimization.
Shape: ((d, ), (`num_samples`, d)).
:rtype: (numpy.ndarray, numpy.ndarray)
"""
list_next_point = []
if self.str_optimizer_method_bo == 'L-BFGS-B':
list_bounds = self._get_bounds()
initials = self.get_samples(str_sampling_method,
fun_objective=fun_negative_acquisition,
num_samples=num_samples)
for arr_initial in initials:
next_point = minimize(fun_negative_acquisition,
x0=arr_initial,
bounds=list_bounds,
method=self.str_optimizer_method_bo,
options={'disp': False})
next_point_x = next_point.x
list_next_point.append(next_point_x)
if self.debug:
self.logger.debug('acquired sample: %s',
utils_logger.get_str_array(next_point_x))
elif self.str_optimizer_method_bo == 'DIRECT': # pragma: no cover
self.logger.debug('num_samples is ignored.')
list_bounds = self._get_bounds()
next_point = directminimize(
fun_negative_acquisition,
bounds=list_bounds,
maxf=88888,
)
next_point_x = next_point.x
list_next_point.append(next_point_x)
elif self.str_optimizer_method_bo == 'CMA-ES':
self.logger.debug('num_samples is ignored.')
list_bounds = self._get_bounds()
list_bounds = np.array(list_bounds)
def fun_wrapper(f):
def g(bx):
return f(bx)[0]
return g
initials = self.get_samples(str_sampling_method,
fun_objective=fun_negative_acquisition,
num_samples=1)
cur_sigma0 = np.mean(list_bounds[:, 1] - list_bounds[:, 0]) / 4.0
next_point_x = cma.fmin(fun_wrapper(fun_negative_acquisition),
initials[0],
cur_sigma0,
options={
'bounds':
[list_bounds[:, 0], list_bounds[:, 1]],
'verbose':
-1,
'maxfevals':
1e5
})[0]
list_next_point.append(next_point_x)
next_points = np.array(list_next_point)
next_point = utils_bo.get_best_acquisition_by_evaluation(
next_points, fun_negative_acquisition)[0]
return next_point, next_points
def _optimize(self, fun_negative_acquisition, str_initial_method,
int_samples):
"""
It optimizes `fun_negative_function` with `self.str_optimizer_method_bo`.
`int_samples` examples are determined by `str_initial_method`, to start acquisition function optimization.
:param fun_objective: negative acquisition function.
:type fun_objective: function
:param str_initial_method: the name of sampling method.
:type str_initial_method: str.
:param int_samples: the number of samples.
:type int_samples: int.
:returns: tuple of next point to evaluate and all candidates determined by acquisition function optimization. Shape: ((d, ), (`int_samples`, d)).
:rtype: (numpy.ndarray, numpy.ndarray)
"""
list_next_point = []
if self.str_optimizer_method_bo == 'L-BFGS-B':
list_bounds = self._get_bounds()
arr_initials = self.get_initial(
str_initial_method,
fun_objective=fun_negative_acquisition,
int_samples=int_samples)
for arr_initial in arr_initials:
next_point = minimize(fun_negative_acquisition,
x0=arr_initial,
bounds=list_bounds,
method=self.str_optimizer_method_bo,
options={'disp': False})
next_point_x = next_point.x
list_next_point.append(next_point_x)
if self.debug:
logger.debug('acquired sample: {}'.format(
utils_logger.get_str_array(next_point_x)))
elif self.str_optimizer_method_bo == 'DIRECT': # pragma: no cover
list_bounds = self._get_bounds()
next_point = directminimize(
fun_negative_acquisition,
bounds=list_bounds,
maxf=88888,
)
next_point_x = next_point.x
list_next_point.append(next_point_x)
elif self.str_optimizer_method_bo == 'CMA-ES':
list_bounds = self._get_bounds()
list_bounds = np.array(list_bounds)
def fun_wrapper(f):
def g(bx):
return f(bx)[0]
return g
arr_initials = self.get_initial(
str_initial_method,
fun_objective=fun_negative_acquisition,
int_samples=1)
cur_sigma0 = np.mean(list_bounds[:, 1] - list_bounds[:, 0]) / 4.0
next_point_x = cma.fmin(fun_wrapper(fun_negative_acquisition),
arr_initials[0],
cur_sigma0,
options={
'bounds':
[list_bounds[:, 0], list_bounds[:, 1]],
'verbose':
-1,
'maxfevals':
1e5
})[0]
list_next_point.append(next_point_x)
next_points = np.array(list_next_point)
next_point = get_best_acquisition(next_points,
fun_negative_acquisition)
return next_point.flatten(), next_points