import typing
from collections.abc import Callable, Iterable
from typing import Any
import numpy as np
from .fitness_normalizer import FitnessNormalizer
T = typing.TypeVar("T")
[docs]
class Add(FitnessNormalizer[T]):
"""
Adds a number to the fitness values.
Examples:
*Incrementing Fitness Values by a Set of Values*
In this example, assume that each fitness value consists of a
:class:`tuple` of numbers, each representing a different property
of the molecule, and each contributing to the final fitness value.
The properties can be anything, such as energy, number of atoms
or diameter.
Often, if these properties indicate a low fitness value, you
will take their inverse. However, if these properties can have
a value of 0, and you try to take their inverse you can end up
dividing by 0, which is bad. To avoid this, you can add a number,
like 1, to the fitness values before taking their inverse. This
normalizer allows you to do this.
Giving a concrete example
.. testcode:: incrementing-fitness-values-by-a-set-of-values
import stk
import numpy as np
building_block = stk.BuildingBlock('BrCCBr', stk.BromoFactory())
record = stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[building_block],
repeating_unit='A',
num_repeating_units=2,
),
)
fitness_values = {
record: (0, 0, 0),
}
normalizer = stk.Add((1, 2, 3))
normalized_fitness_values = normalizer.normalize(fitness_values)
assert np.all(np.equal(
normalized_fitness_values[record],
(1, 2, 3),
))
*Selectively Normalizing Fitness Values*
Sometimes, you only want to normalize some members of a population,
for example if some do not have an assigned fitness value,
because the fitness calculation failed for whatever reason.
You can use the `filter` parameter to exclude records from the
normalization
.. testcode:: incrementing-fitness-values-by-a-set-of-values
import stk
import numpy as np
building_block = stk.BuildingBlock('BrCCBr', stk.BromoFactory())
record1 = stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[building_block],
repeating_unit='A',
num_repeating_units=2,
),
)
record2 = stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=[building_block],
repeating_unit='A',
num_repeating_units=2,
),
)
fitness_values = {
record1: (0, 0, 0),
record2: None,
}
normalizer = stk.Add(
number=(1, 2, 3),
# Only normalize values which are not None.
filter=lambda fitness_values, record:
fitness_values[record] is not None,
)
normalized_fitness_values = normalizer.normalize(
fitness_values=fitness_values,
)
assert np.all(np.equal(
normalized_fitness_values[record1],
(1, 2, 3),
))
assert normalized_fitness_values[record2] is None
"""
def __init__(
self,
number: float | Iterable[float],
filter: Callable[[dict[T, Any], T], bool] = lambda fitness_values,
record: True,
) -> None:
"""
Parameters:
number (float | list[float]):
The number each fitness value is increased by. Can
be a single number or multiple numbers, depending on the
form of the fitness value.
filter:
A function which returns ``True`` or ``False``. Only
molecules which return ``True`` will have fitness values
normalized. By default, all molecules will have fitness
values normalized.
The instance passed to the `fitness_values` argument of
:meth:`.normalize` is passed as the first argument, while
the second argument will be passed every
:class:`.MoleculeRecord` in it, one at a time.
"""
if not isinstance(number, int | float):
number = tuple(number)
self._number = number
self._filter = filter
[docs]
def normalize(self, fitness_values: dict[T, Any]) -> dict[T, Any]:
return {
record: (
np.add(self._number, fitness_value)
if self._filter(fitness_values, record)
else fitness_value
)
for record, fitness_value in fitness_values.items()
}