import typing
from collections.abc import Callable, Iterator, Sequence
import numpy as np
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 Roulette(Selector[T]):
"""
Uses roulette selection to select batches of molecules.
In roulette selection the probability a batch is selected
is given by its fitness. If the total fitness is the sum of all
fitness values, the chance a batch is selected is given
by::
p = batch fitness / total fitness,
where ``p`` is the probability of selection and the batch
fitness is the sum of all fitness values of molecules in the
batch [#]_.
References:
.. [#] http://tinyurl.com/csc3djm
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.
roulette = stk.Roulette(num_batches=5)
population = {
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[
stk.BuildingBlock('BrCCBr', stk.BromoFactory()),
],
repeating_unit='A',
num_repeating_units=2,
),
): i
for i in range(100)
}
# Select the molecules.
for selected, in roulette.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.
roulette = stk.Roulette(num_batches=5, 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,
),
): i
for i in range(100)
}
# Select the molecules.
for selected1, selected2 in roulette.select(population):
# Do stuff to the 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,
random_seed: int | np.random.Generator | None = None,
) -> 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 molecules 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.
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.
random_seed:
The random seed to use.
"""
super().__init__(key_maker, fitness_modifier, batch_size)
if random_seed is None or isinstance(random_seed, int):
random_seed = np.random.default_rng(random_seed)
self._generator = random_seed
self._duplicate_molecules = duplicate_molecules
self._duplicate_batches = duplicate_batches
self._num_batches = (
float("inf") if num_batches is None else num_batches
)
def _select_from_batches(
self,
batches: Sequence[Batch[T]],
yielded_batches: YieldedBatches[T],
) -> Iterator[Batch[T]]:
while batches and yielded_batches.get_num() < self._num_batches:
total = sum(batch.get_fitness_value() for batch in batches)
weights = [batch.get_fitness_value() / total for batch in batches]
yield self._generator.choice(
batches, # type: ignore
p=weights,
)
if not self._duplicate_molecules:
batches_ = filter(
yielded_batches.has_no_yielded_molecules,
batches,
)
if not self._duplicate_batches:
batches_ = filter(
yielded_batches.is_unyielded_batch,
batches,
)
if not self._duplicate_molecules or not self._duplicate_batches:
batches = tuple(batches_)