def predict_with_acq(self, x):
from entmoot.utils import is_2Dlistlike
if is_2Dlistlike(x):
next_x = np.asarray(self.space.transform(x))
else:
next_x = np.asarray(self.space.transform([x])[0]).reshape(1, -1)
if self.models:
temp_val = _gaussian_acquisition(
X=next_x,
model=self.models[-1],
y_opt=np.min(self.yi),
acq_func=self.acq_func,
acq_func_kwargs=self.acq_func_kwargs)
else:
est = self.base_estimator_
est.fit(self.space.transform(self.Xi), self.yi)
temp_val = _gaussian_acquisition(
X=next_x,
model=est,
y_opt=np.min(self.yi),
acq_func=self.acq_func,
acq_func_kwargs=self.acq_func_kwargs)
if is_2Dlistlike(x):
return temp_val
else:
return temp_val[0]
def predict_with_est(self, x, return_std=True):
from entmoot.utils import is_2Dlistlike
if is_2Dlistlike(x):
next_x = np.asarray(self.space.transform(x))
else:
next_x = np.asarray(self.space.transform([x])[0]).reshape(1, -1)
est = self.base_estimator_
est.fit(self.space.transform(self.Xi), self.yi)
temp_mu, temp_std = \
est.predict(
X=next_x,
return_std=True)
if is_2Dlistlike(x):
if return_std:
return temp_mu, temp_std
else:
return temp_mu
else:
if return_std:
return temp_mu[0], temp_std[0]
else:
return temp_mu[0]
def _tell(self, x, y):
"""
Adds the new data points to the data set.
:param x: list, locations of new data points
:param y: list, target value of new data points
"""
from entmoot.utils import is_listlike
from entmoot.utils import is_2Dlistlike
# if y isn't a scalar it means we have been handed a batch of points
if is_listlike(y) and is_2Dlistlike(x):
self.Xi.extend(x)
self.yi.extend(y)
self._n_initial_points -= len(y)
elif is_listlike(x):
self.Xi.append(x)
self.yi.append(y)
self._n_initial_points -= 1
else:
raise ValueError("Type of arguments `x` (%s) and `y` (%s) "
"not compatible." % (type(x), type(y)))
# optimizer learned something new - discard cache
self.cache_ = {}
# set self._next_x to None to indicate that the solver has learned something new
self._next_x = None
def _check_y_is_valid(self, x, y):
"""check if the shape and types of x and y are consistent."""
from entmoot.utils import is_listlike
from entmoot.utils import is_2Dlistlike
# single objective checks for scalar values
if self.num_obj == 1:
# if y isn't a scalar it means we have been handed a batch of points
if is_listlike(y) and is_2Dlistlike(x):
for y_value in y:
if not isinstance(y_value, Number):
raise ValueError("expected y to be a list of scalars")
elif is_listlike(x):
if not isinstance(y, Number):
raise ValueError("`func` should return a scalar")
else:
raise ValueError("Type of arguments `x` (%s) and `y` (%s) "
"not compatible." % (type(x), type(y)))
else:
# if y isn't a scalar it means we have been handed a batch of points
if is_listlike(y[0]) and is_2Dlistlike(x):
for y_value in y:
if len(y_value) != self.num_obj:
raise ValueError(
f"expected y to be of size {self.num_obj}")
for yi in y_value:
if not isinstance(yi, Number):
raise ValueError(
f"expected y to be a list of list-like items of length {self.num_obj}"
)
elif is_listlike(x):
if len(y) != self.num_obj:
raise ValueError(
f"`func` should return a list-like item of length {self.num_obj}"
)
for yi in y:
if not isinstance(yi, Number):
raise ValueError(
f"`func` should return a list-like item of length {self.num_obj}"
)
else:
raise ValueError("Type of arguments `x` (%s) and `y` (%s) "
"not compatible." % (type(x), type(y)))
def __call__(self, X):
# check if multiple points are given
if not is_2Dlistlike(X):
X = [X]
res = []
for x in X:
res.append(self._eval_point(x))
if len(res) == 1:
res = res[0]
return res