Source code for stk.ea.fitness_normalizers.divide_by_mean

"""
Divide By Mean
==============

"""

import logging
from functools import partial

import numpy as np

from .fitness_normalizer import FitnessNormalizer

logger = logging.getLogger(__name__)


[docs]class DivideByMean(FitnessNormalizer): """ Divides fitness values by the population mean. While this function can be used if the fitness value of each :class:`.Molecule` in the population is a single number, it is most useful when the fitness value is a :class:`tuple` of numbers. In this case, it is necessary to somehow combine the numbers so that a single fitness value is produced. For example, take a fitness value which is the vector holding the properties ``[energy, diameter, num_atoms]``. For a given molecule these numbers may be something like ``[200,000, 12, 140]``. If we were to sum these numbers, the energy term would dominate the final fitness value. In order to combine these numbers we can divide them by the population averages. For example, if the average energy of molecules in the population is ``300,000`` the average diameter is ``10`` and the average number of atoms is ``70`` then the fitness vector would be scaled to ``[0.5, 1.2, 2]``. These numbers are now of a similar magnitude and can be summed to give a reasonable value. After division , each value represents how much better than the population average each property value is. In essence we have removed the units from each parameter. Examples -------- *Selectively Normalizing Fitness Values* Sometimes you do not want to normalize all the values in a population together. For example, if a failed fitness value calculation resulted in some records having a fitness value of ``None``, you would want to ignore these 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=(1., 2., 3.), 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, ), ), ) mean_scaler = stk.DivideByMean( # Only normalize values which are not None. filter=lambda population, record: record.get_fitness_value() is not None ) # Calling mean_scaler.normalize() will return a new # population holding the molecule records with normalized # fitness values. normalized_population = tuple(mean_scaler.normalize( population=population, )) normalized_record1, normalized_record2 = normalized_population assert np.all(np.equal( normalized_record1.get_fitness_value(), (1, 1, 1), )) """
[docs] def __init__(self, filter=lambda population, record: True): """ Initialize a :class:`.DivideByMean` instance. Parameters ---------- 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._filter = filter
[docs] def normalize(self, population): filtered = filter( partial(self._filter, population), population, ) mean = np.mean( a=[record.get_fitness_value() for record in filtered], axis=0, ) logger.debug(f'Means used: {mean}') for record in population: if self._filter(population, record): yield record.with_fitness_value( fitness_value=np.divide( record.get_fitness_value(), mean, ) ) else: yield record