Source code for stk.ea.fitness_normalizers.add

"""
Add
===

"""

import numpy as np

from .fitness_normalizer import FitnessNormalizer


[docs]class Add(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 :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( 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 .. testcode:: incrementing-fitness-values-by-a-set-of-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=(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 """
[docs] def __init__( self, number, filter=lambda population, record: True, ): """ Initialize a :class:`.Add` instance. Parameters ---------- number : :class:`float` or \ :class:`tuple` of :class:`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 : :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._number = number self._filter = filter
[docs] def normalize(self, population): for record in population: if self._filter(population, record): # Use np.add here so that both tuples and arrays # work properly. yield record.with_fitness_value( fitness_value=np.add( self._number, record.get_fitness_value(), ), ) else: yield record