Add

class Add(number, filter=<function Add.<lambda>>)

Bases: FitnessNormalizer

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 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

import stk
import numpy as np

building_block = stk.BuildingBlock(
    smiles='BrCCBr',
    functional_groups=[stk.BromoFactory()],
)

population = (
    stk.MoleculeRecord(
        topology_graph=stk.polymer.Linear(
            building_blocks=(building_block, ),
            repeating_unit='A',
            num_repeating_units=2,
        ),
    ).with_fitness_value(
        fitness_value=(0, 0, 0),
        normalized=False,
    ),
)

normalizer = stk.Add((1, 2, 3))
# Calling normalizer.normalize() will return a new
# population holding the molecule records with normalized
# fitness values.
normalized_population = tuple(normalizer.normalize(
    population=population,
))
normalized_record, = normalized_population
assert np.all(np.equal(
    normalized_record.get_fitness_value(),
    (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

import stk
import numpy as np

building_block = stk.BuildingBlock(
    smiles='BrCCBr',
    functional_groups=[stk.BromoFactory()],
)

population = (
    stk.MoleculeRecord(
        topology_graph=stk.polymer.Linear(
            building_blocks=(building_block, ),
            repeating_unit='A',
            num_repeating_units=2,
        ),
    ).with_fitness_value(
        fitness_value=(0, 0, 0),
        normalized=False,
    ),
    # This will have a fitness value of None.
    stk.MoleculeRecord(
        topology_graph=stk.polymer.Linear(
            building_blocks=(building_block, ),
            repeating_unit='A',
            num_repeating_units=2,
        ),
    ),
)

normalizer = stk.Add(
    number=(1, 2, 3),
    # Only normalize values which are not None.
    filter=lambda population, record:
        record.get_fitness_value() is not None,
)

# Calling normalizer.normalize() will return a new
# population holding the molecule records with normalized
# fitness values.
normalized_population = tuple(normalizer.normalize(
    population=population,
))
normalized_record1, normalized_record2 = normalized_population
assert np.all(np.equal(
    normalized_record1.get_fitness_value(),
    (1, 2, 3),
))
assert normalized_record2.get_fitness_value() is None

Methods

normalize(population)

Normalize the fitness values in population.

__init__(number, filter=<function Add.<lambda>>)

Initialize a Add instance.

Parameters:

• number (float or tuple of 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 (callable, optional) – Takes two parameters, first is a tuple of MoleculeRecord instances, and the second is a MoleculeRecord. The callable 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 population argument of normalize() is passed as the first argument, while the second argument will be passed every MoleculeRecord in it, one at a time.

normalize(population)

Normalize the fitness values in population.

Parameters:

population (tuple of MoleculeRecord) – The molecules which need to have their fitness values normalized.

Yields:

MoleculeRecord – A record with a normalized fitness value.