def create_result(Xi,
yi,
space=None,
rng=None,
specs=None,
models=None,
model_mu=None,
model_std=None,
gurobi_mipgap=None):
"""
Initialize an `OptimizeResult` object.
Parameters
----------
Xi : list of lists, shape (n_iters, n_features)
Location of the minimum at every iteration.
yi : array-like, shape (n_iters,)
Minimum value obtained at every iteration.
space : Space instance, optional
Search space.
rng : RandomState instance, optional
State of the random state.
specs : dict, optional
Call specifications.
models : list, optional
List of fit surrogate models.
Returns
-------
res : `OptimizeResult`, scipy object
OptimizeResult instance with the required information.
"""
res = OptimizeResult()
yi = np.asarray(yi)
if np.ndim(yi) == 2:
res.log_time = np.ravel(yi[:, 1])
yi = np.ravel(yi[:, 0])
best = np.argmin(yi)
res.x = Xi[best]
res.fun = yi[best]
res.func_vals = yi
res.x_iters = Xi
res.models = models
res.model_mu = model_mu
res.model_std = model_std
res.gurobi_mipgap = gurobi_mipgap
res.space = space
res.random_state = rng
res.specs = specs
return res
