def unpack_point(point, space):
"""Flatten `point` in `space` and convert it to a 1D list.
This function is deprecated and will be removed in v0.2.0. Use
`orion.core.utils.points.flatten_dims` instead.
"""
logger.warning(
"`unpack_point` is deprecated and will be removed in v0.2.0. Use "
"`orion.core.utils.points.regroup_dims` instead.")
return flatten_dims(point, space)
def suggest(self, num=1):
"""Suggest a `num` of new sets of parameters. Randomly draw samples
from the import space and return them.
:param num: how many sets to be suggested.
.. note:: New parameters must be compliant with the problem's domain
`orion.algo.space.Space`.
"""
if num > 1:
raise ValueError("TPE should suggest only one point.")
samples = []
if len(self._trials_info) < self.n_initial_points:
new_point = self.space.sample(1,
seed=tuple(
self.rng.randint(0,
1000000,
size=3)))[0]
samples.append(new_point)
else:
point = []
below_points, above_points = self.split_trials()
below_points = [
flatten_dims(point, self.space) for point in below_points
]
above_points = [
flatten_dims(point, self.space) for point in above_points
]
below_points = list(map(list, zip(*below_points)))
above_points = list(map(list, zip(*above_points)))
idx = 0
for dimension in self.space.values():
shape = dimension.shape
if not shape:
shape = (1, )
if dimension.type == 'real':
points = self._sample_real_dimension(
dimension, shape[0], below_points[idx:idx + shape[0]],
above_points[idx:idx + shape[0]])
elif dimension.type == 'integer' and dimension.prior_name == 'int_uniform':
points = self.sample_one_dimension(
dimension, shape[0], below_points[idx:idx + shape[0]],
above_points[idx:idx + shape[0]],
self._sample_int_point)
elif dimension.type == 'categorical' and dimension.prior_name == 'choices':
points = self.sample_one_dimension(
dimension, shape[0], below_points[idx:idx + shape[0]],
above_points[idx:idx + shape[0]],
self._sample_categorical_point)
elif dimension.type == 'fidelity':
# fidelity dimension
points = dimension.sample(num)
else:
raise NotImplementedError()
if len(points) < shape[0]:
logger.warning(
'TPE failed to sample new point with configuration %s',
self.configuration)
return None
idx += shape[0]
point += points
point = regroup_dims(point, self.space)
samples.append(point)
return samples