Power

class Power(power, filter=<function Power.<lambda>>)

Bases: stk.ea.fitness_normalizers.fitness_normalizer.FitnessNormalizer

Raises fitness values to some power.

Examples

Raising Fitness Values to a Power

Sometimes you might calculate a property for a molecule, where that property indicates a low fitness value. You can use Power to raise it to the power of -1 to get your final fitness value

import stk

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=1,
        normalized=False,
    ),
    stk.MoleculeRecord(
        topology_graph=stk.polymer.Linear(
            building_blocks=(building_block, ),
            repeating_unit='A',
            num_repeating_units=2,
        ),
    ).with_fitness_value(
        fitness_value=2,
        normalized=False,
    ),
)

normalizer = stk.Power(-1)
normalized_population = tuple(normalizer.normalize(population))
normalized_record1, normalized_record2 = normalized_population
assert normalized_record1.get_fitness_value() == 1
assert normalized_record2.get_fitness_value() == 0.5

Raising Fitness Values by a Set of Powers

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.

If your final fitness value depends on the combination of these properties, you will probably first want to scale them with DivideByMean. Once this is done, you may want to raise each property by some power. For example if you raise the value of one property by 1 and another by -1, it means that when the value of property 1 is big, the fitness value should also be big, but if the value of property 2 is big, the fitness value should be small.

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=(2, 2, 2),
        normalized=False,
    ),
)

normalizer = stk.Power((1, -1, 2))
normalized_population = tuple(normalizer.normalize(population))
normalized_record, = normalized_population
assert np.all(np.equal(
    normalized_record.get_fitness_value(),
    (2, 0.5, 4),
))

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=(2, 2, 2),
        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.Power(
    power=(1, -1, 2),
    # 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(),
    (2, 0.5, 4),
))
assert normalized_record2.get_fitness_value() is None

Methods

normalize(population)

Normalize the fitness values in population.

__init__(power, filter=<function Power.<lambda>>)

Initialize a Power instance.

Parameters

• power (float or tuple of float) – The power each fitness value is raised to. 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.