"""Provides the GeneticAlgorithmEmitter."""
from __future__ import annotations
from collections.abc import Collection
import numpy as np
from numpy.typing import ArrayLike
from ribs._utils import check_batch_shape, check_shape
from ribs.archives import ArchiveBase
from ribs.emitters._emitter_base import EmitterBase
from ribs.emitters.operators import _get_op
from ribs.typing import Float, Int
[docs]
class GeneticAlgorithmEmitter(EmitterBase):
"""Creates solutions with a genetic algorithm.
If the archive is empty and ``initial_solutions`` is set, a call to :meth:`ask` will
return ``initial_solutions``. If ``initial_solutions`` is not set, we pass ``x0``
through the operator.
Args:
archive: Archive of solutions, e.g., :class:`ribs.archives.GridArchive`.
operator: Internal operator for mutating solutions. See
:mod:`ribs.emitters.operators` for the list of allowed names.
operator_kwargs: Additional arguments to pass to the operator. See
:mod:`ribs.emitters.operators` for the arguments allowed by each operator.
x0: Initial solution.
initial_solutions: An (n, solution_dim) array of solutions to be used when the
archive is empty, in lieu of ``x0``.
bounds: Bounds of the solution space. Pass None to indicate there are no bounds.
Alternatively, pass an array-like to specify the bounds for each dim. Each
element in this array-like can be None to indicate no bound, or a tuple of
``(lower_bound, upper_bound)``, where ``lower_bound`` or ``upper_bound`` may
be None to indicate no bound. Unbounded upper bounds are set to +inf, and
unbounded lower bounds are set to -inf.
lower_bounds: Instead of specifying ``bounds``, ``lower_bounds`` and
``upper_bounds`` may be specified. This is useful if, for instance,
solutions are multi-dimensional. Here, pass None to indicate there are no
bounds (i.e., bounds are set to -inf), or pass an array specifying the lower
bounds of the solution space.
upper_bounds: Upper bounds of the solution space; see ``lower_bounds`` above.
Pass None to indicate there are no bounds (i.e., bounds are set to inf).
batch_size: Number of solutions to return in :meth:`ask`.
seed: Value to seed the random number generator. Set to None to avoid a fixed
seed.
Raises:
ValueError: There is an error in x0 or initial_solutions.
ValueError: There is an error in the bounds configuration.
"""
def __init__(
self,
archive: ArchiveBase,
*,
operator: str,
operator_kwargs: dict | None = None,
x0: ArrayLike | None = None,
initial_solutions: ArrayLike | None = None,
bounds: Collection[tuple[None | Float, None | Float]] | None = None,
lower_bounds: ArrayLike | None = None,
upper_bounds: ArrayLike | None = None,
batch_size: Int = 64,
seed: Int | None = None,
) -> None:
EmitterBase.__init__(
self,
archive,
solution_dim=archive.solution_dim,
bounds=bounds,
lower_bounds=lower_bounds,
upper_bounds=upper_bounds,
)
self._batch_size = batch_size
self._x0 = x0
self._initial_solutions = None
if x0 is None and initial_solutions is None:
raise ValueError("Either x0 or initial_solutions must be provided.")
if x0 is not None and initial_solutions is not None:
raise ValueError("x0 and initial_solutions cannot both be provided.")
if x0 is not None:
self._x0 = np.array(x0, dtype=archive.dtypes["solution"])
check_shape(self._x0, "x0", archive.solution_dim, "archive.solution_dim")
elif initial_solutions is not None:
self._initial_solutions = np.asarray(
initial_solutions, dtype=archive.dtypes["solution"]
)
check_batch_shape(
self._initial_solutions,
"initial_solutions",
archive.solution_dim,
"archive.solution_dim",
)
operator_class = _get_op(operator)
self._operator = operator_class(
**(operator_kwargs if operator_kwargs is not None else {}),
# We assume the class takes in a seed, but this is technically not part of
# the API.
seed=seed,
)
@property
def x0(self) -> np.ndarray | None:
"""Initial solution (if ``initial_solutions`` is not set)."""
return self._x0
@property
def initial_solutions(self) -> np.ndarray | None:
"""Returned when the archive is empty (if :attr:`x0` is not set)."""
return self._initial_solutions
@property
def batch_size(self) -> Int:
"""Number of solutions to return in :meth:`ask`."""
return self._batch_size
def _clip(self, solutions: np.ndarray) -> np.ndarray:
"""Clips solutions to the bounds of the solution space."""
return np.clip(solutions, self.lower_bounds, self.upper_bounds)
[docs]
def ask(self) -> np.ndarray:
"""Creates solutions with the provided operator.
If the archive is empty and ``initial_solutions`` is set, a call to :meth:`ask`
will return ``initial_solutions``. If ``initial_solutions`` is not set, we pass
``x0`` through the operator. Otherwise, we sample parents from the archive to be
passed to the operator.
Returns:
If the archive is not empty, ``(batch_size, solution_dim)``
array -- contains ``batch_size`` new solutions to evaluate. If the archive
is empty, we return ``initial_solutions``, which might not have
``batch_size`` solutions.
Raises:
ValueError: The ``parent_type`` of the operator is unknown.
"""
if self.archive.empty and self.initial_solutions is not None:
return self._clip(self.initial_solutions)
if self._operator.parent_type == 1:
if self.archive.empty:
parents = np.repeat(self.x0[None], repeats=self.batch_size, axis=0)
else:
parents = self.archive.sample_elites(self.batch_size)["solution"]
return self._clip(self._operator.ask(parents))
elif self._operator.parent_type == 2:
if self.archive.empty:
parents = np.repeat(self.x0[None], repeats=2 * self.batch_size, axis=0)
else:
parents = self.archive.sample_elites(2 * self.batch_size)["solution"]
return self._clip(
self._operator.ask(parents.reshape(2, self.batch_size, -1))
)
else:
raise ValueError(
f"Unknown operator `parent_type` {self._operator.parent_type}"
)