Source code for stk.ea.selection.selectors.roulette

"""
Roulette
========

"""

import numpy as np

from stk.molecular import Inchi

from .selector import Selector


[docs]class Roulette(Selector): """ Uses roulette selection to select batches of molecules. In roulette selection the probability a batch is selected is given by its fitness. If the total fitness is the sum of all fitness values, the chance a batch is selected is given by:: p = batch fitness / total fitness, where ``p`` is the probability of selection and the batch fitness is the sum of all fitness values of molecules in the batch [#]_. References ---------- .. [#] http://tinyurl.com/csc3djm Examples -------- *Yielding Single Molecule Batches* Yielding molecules one at a time. For example, if molecules need to be selected for mutation or the next generation .. testcode:: yielding-single-molecule-batches import stk # Make the selector. roulette = stk.Roulette(num_batches=5) population = tuple( stk.MoleculeRecord( topology_graph=stk.polymer.Linear( building_blocks=( stk.BuildingBlock( smiles='BrCCBr', functional_groups=[stk.BromoFactory()], ), ), repeating_unit='A', num_repeating_units=2, ), ).with_fitness_value(i) for i in range(100) ) # Select the molecules. for selected, in roulette.select(population): # Do stuff with each selected molecule. pass *Yielding Batches Holding Multiple Molecules* Yielding multiple molecules at once. For example, if molecules need to be selected for crossover .. testcode:: yielding-batches-holding-multiple-molecules import stk # Make the selector. roulette = stk.Roulette(num_batches=5, batch_size=2) population = tuple( stk.MoleculeRecord( topology_graph=stk.polymer.Linear( building_blocks=( stk.BuildingBlock( smiles='BrCCBr', functional_groups=[stk.BromoFactory()], ), ), repeating_unit='A', num_repeating_units=2, ), ).with_fitness_value(i) for i in range(100) ) # Select the molecules. for selected1, selected2 in roulette.select(population): # Do stuff to the molecules. pass """
[docs] def __init__( self, num_batches=None, batch_size=1, duplicate_molecules=True, duplicate_batches=True, key_maker=Inchi(), fitness_modifier=None, random_seed=None ): """ Initialize a :class:`Roulette` instance. Parameters ---------- num_batches : :class:`int`, optional The number of batches to yield. If ``None`` then yielding will continue forever or until the generator is exhausted, whichever comes first. batch_size : :class:`int`, optional The number of molecules yielded at once. duplicate_molecules : :class:`bool`, optional If ``True`` the same molecule can be yielded in more than one batch. duplicate_batches : :class:`bool`, optional If ``True`` the same batch can be yielded more than once. key_maker : :class:`.MoleculeKeyMaker`, optional Used to get the keys of molecules. If two molecules have the same key, they are considered duplicates. fitness_modifier : :class:`callable`, optional Takes the `population` on which :meth:`.select` is called and returns a :class:`dict`, which maps records in the `population` to the fitness values the :class:`.Selector` should use. If ``None``, the regular fitness values of the records are used. random_seed : :class:`int`, optional The random seed to use. """ if num_batches is None: num_batches = float('inf') if fitness_modifier is None: fitness_modifier = self._get_fitness_values self._generator = np.random.RandomState(random_seed) self._duplicate_molecules = duplicate_molecules self._duplicate_batches = duplicate_batches self._num_batches = num_batches self._batch_size = batch_size super().__init__( key_maker=key_maker, fitness_modifier=fitness_modifier, )
def _select_from_batches(self, batches, yielded_batches): while ( batches and yielded_batches.get_num() < self._num_batches ): total = sum(batch.get_fitness_value() for batch in batches) weights = [ batch.get_fitness_value() / total for batch in batches ] yield self._generator.choice(batches, p=weights) if not self._duplicate_molecules: batches = filter( yielded_batches.has_no_yielded_molecules, batches, ) if not self._duplicate_batches: batches = filter( yielded_batches.is_unyielded_batch, batches, ) if ( not self._duplicate_molecules or not self._duplicate_batches ): batches = tuple(batches)