Source code for stk.ea.fitness_normalizers.power

"""
Power
=====

"""

import numpy as np

from .fitness_normalizer import FitnessNormalizer


[docs]class Power(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 :class:`.Power` to raise it to the power of -1 to get your final fitness value .. testcode:: raising-fitness-values-to-a-power 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 :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. If your final fitness value depends on the combination of these properties, you will probably first want to scale them with :class:`.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 .. testcode:: raising-fitness-values-by-a-set-of-powers 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 .. testcode:: selectively-normalizing-fitness-values 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 """
[docs] def __init__( self, power, filter=lambda population, record: True, ): """ Initialize a :class:`.Power` instance. Parameters ---------- power : :class:`float` or \ :class:`tuple` of :class:`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 : :class:`callable`, optional Takes two parameters, first is a :class:`tuple` of :class:`.MoleculeRecord` instances, and the second is a :class:`.MoleculeRecord`. The :class:`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 :meth:`.normalize` is passed as the first argument, while the second argument will be passed every :class:`.MoleculeRecord` in it, one at a time. """ self._power = power self._filter = filter
[docs] def normalize(self, population): for record in population: if self._filter(population, record): yield record.with_fitness_value( fitness_value=np.float_power( record.get_fitness_value(), self._power, ), ) else: yield record