specs = {"args": copy.copy(inspect.currentframe().f_locals),
"function": inspect.currentframe().f_code.co_name}
if callback is not None:
if isinstance(callback, Callable):
callback = [callback]
elif not (isinstance(callback, list) and
all([isinstance(c, Callable) for c in callback])):
raise ValueError("callback should be either a callable or "
"a list of callables.")
// Check params
rng = check_random_state(random_state)
space = Space(dimensions)
// Default GP
if base_estimator is None:
base_estimator = GaussianProcessRegressor(
kernel=(ConstantKernel(1.0, (0.01, 1000.0)) *
Matern(length_scale=np.ones(space.transformed_n_dims),
length_scale_bounds=[(0.01, 100)] * space.transformed_n_dims,
nu=2.5)),
normalize_y=True, alpha=alpha, random_state=random_state)
// Initialize with provided points (x0 and y0) and/or random points
if x0 is None:
x0 = []
elif not isinstance(x0[0], list):
x0 = [x0]
if not isinstance(x0, list):
raise ValueError("`x0` should be a list, but got %s" % type(x0))
n_init_func_calls = len(x0) if y0 is None else 0
n_total_init_calls = n_random_starts + n_init_func_calls
if n_total_init_calls <= 0:
// if x0 is not provided and n_random_starts is 0 then
// it will ask for n_random_starts to be > 0.
raise ValueError(
"Expected `n_random_starts` > 0, got %d" % n_random_starts)
if n_calls < n_total_init_calls:
raise ValueError(
"Expected `n_calls` >= %d, got %d" % (n_total_init_calls, n_calls))
func_call_no = 0
if y0 is None and x0:
y0 = []
for i, x in enumerate(x0):
y0.append(verbose_func(
func, x, verbose=verbose, prev_ys=y0, x_info="provided",
func_call_no=func_call_no))
func_call_no += 1
if callback is not None:
curr_res = create_result(x0, y0, space, rng, specs)for c in callback:
c(curr_res)
elif x0:
if isinstance(y0, Iterable):
y0 = list(y0)
elif isinstance(y0, numbers.Number):
