import itertools
import typing
from collections.abc import Callable, Iterable, Iterator, Sequence
from stk._internal.ea.molecule_record import MoleculeRecord
from stk._internal.ea.selection.batch import Batch
from stk._internal.ea.selection.selectors.yielded_batches import YieldedBatches
from stk._internal.key_makers.inchi import Inchi
from stk._internal.key_makers.molecule import MoleculeKeyMaker
from .selector import Selector
T = typing.TypeVar("T", bound=MoleculeRecord)
[docs]
class Best(Selector[T]):
"""
Selects batches of molecules, highest fitness value first.
Examples:
*Yielding Single Molecule Batches*
Yielding molecules one at a time. For example, if molecules need
to be selected for mutation or the next generation.
.. testcode:: yielding-single-molecule-batches
import stk
# Make the selector.
best = stk.Best()
population = {
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[
stk.BuildingBlock('BrCCBr', stk.BromoFactory()),
],
repeating_unit='A',
num_repeating_units=2,
),
): 1,
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[
stk.BuildingBlock('BrCCBr', stk.BromoFactory()),
],
repeating_unit='A',
num_repeating_units=2,
),
): 2
}
# Select the molecules.
for selected, in best.select(population):
# Do stuff with each selected molecule.
pass
*Yielding Batches Holding Multiple Molecules*
Yielding multiple molecules at once. For example, if molecules need
to be selected for crossover.
.. testcode:: yielding-batches-holding-multiple-molecules
import stk
# Make the selector.
best = stk.Best(batch_size=2)
population = {
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[
stk.BuildingBlock('BrCCBr', stk.BromoFactory()),
],
repeating_unit='A',
num_repeating_units=2,
),
): 1,
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[
stk.BuildingBlock('BrCCBr', stk.BromoFactory()),
],
repeating_unit='A',
num_repeating_units=2,
),
): 2
}
# Select the molecules.
for selected1, selected2 in best.select(population):
# Do stuff with the selected molecules.
pass
"""
def __init__(
self,
num_batches: int | None = None,
batch_size: int = 1,
duplicate_molecules: bool = True,
duplicate_batches: bool = True,
key_maker: MoleculeKeyMaker = Inchi(),
fitness_modifier: Callable[
[dict[T, float]], dict[T, float]
] = lambda x: x,
) -> None:
"""
Parameters:
num_batches:
The number of batches to yield. If ``None`` then yielding
will continue forever or until the generator is exhausted,
whichever comes first.
batch_size:
The number of molecule records yielded at once.
duplicate_molecules:
If ``True`` the same molecule can be yielded in more than
one batch.
duplicate_batches:
If ``True`` the same batch can be yielded more than once.
Duplicate batches can occur if the same molecule is found
multiple times in a population.
key_maker:
Used to get the keys of molecules. If two molecules have
the same key, they are considered duplicates.
fitness_modifier:
Takes the `population` on which :meth:`~.Selector.select`
is called and returns a :class:`dict`, which maps records
in the `population` to the fitness values the
:class:`.Selector` should use.
"""
super().__init__(key_maker, fitness_modifier, batch_size)
self._duplicate_molecules = duplicate_molecules
self._duplicate_batches = duplicate_batches
self._num_batches = num_batches
self._batch_size = batch_size
def _select_from_batches(
self,
batches: Sequence[Batch[T]],
yielded_batches: YieldedBatches[T],
) -> Iterator[Batch[T]]:
selected_batches: Iterable[Batch[T]] = sorted(batches, reverse=True)
if not self._duplicate_molecules:
selected_batches = filter(
yielded_batches.has_no_yielded_molecules,
selected_batches,
)
if not self._duplicate_batches:
selected_batches = filter(
yielded_batches.is_unyielded_batch,
selected_batches,
)
yield from itertools.islice(selected_batches, self._num_batches)