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
def create_result(Xi, yi, space=None, rng=None, specs=None, models=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.space = space
res.random_state = rng
res.specs = specs
return res
def create_result(xi, yi, space=None, rs=None, specs=None, models=None):
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.space = space
res.random_state = rs
res.specs = specs
return res
def create_result(Xi, yi, space=None, rng=None, specs=None, models=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.space = space
res.random_state = rng
res.specs = specs
return res
def create_result(Xi,
yi,
n_evaluations=None,
space=None,
rng=None,
specs=None,
models=None,
maximize=False):
"""
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()
try:
# Hyperband returns evaluations as lists of lists.
# We want to store the results as a single array.
yi = list(itertools.chain.from_iterable(yi))
Xi = list(itertools.chain.from_iterable(Xi))
except TypeError:
# All algorithms other than Hyperband already return a single list.
pass
yi = np.asarray(yi)
if np.ndim(yi) == 2:
res.log_time = np.ravel(yi[:, 1])
yi = np.ravel(yi[:, 0])
if maximize:
best = np.argmax(yi)
else:
best = np.argmin(yi)
res.x = Xi[best]
res.fun = yi[best]
if n_evaluations:
unique, sort_indices = np.unique(yi, return_index=True)
if len(unique) < n_evaluations:
func_sort_idx = np.argsort(yi)
func_vals = sorted(yi)
res.func_vals = np.asarray(func_vals[:n_evaluations])
x_iter_sort = []
for idx in func_sort_idx:
x_iter_sort.append(Xi[idx])
res.x_iters = np.asarray(x_iter_sort[:n_evaluations])
res.all_func_vals = np.asarray(yi)
res.all_x_iters = np.asarray(Xi)
else:
func_vals = sorted(unique)
res.func_vals = np.asarray(func_vals[:n_evaluations])
x_iter_sort = []
for idx in sort_indices:
x_iter_sort.append(Xi[idx])
res.x_iters = np.asarray(x_iter_sort[:n_evaluations])
res.all_func_vals = np.asarray(yi)
res.all_x_iters = np.asarray(Xi)
else:
res.func_vals = np.asarray(yi)
res.x_iters = np.asarray(Xi)
res.models = models
res.space = space
res.random_state = rng
res.specs = specs
return res
