def _test_selection_plotter(selector, population, filename): """ Test :class:`.SelectionPlotter`. Parameters ---------- selector : :class:`.Selector` The selector whose selections will be plotted. population : :class:`tuple` of :class:`.MoleculeRecord` The population from which `selector` selects. filename : :class:`str` The filename for the plots. Returns ------- None : :class:`NoneType` """ stk.SelectionPlotter(filename, selector) tuple(selector.select(population))
progress_fn=apply(pore_diameter), ), stk.ProgressPlotter( filename="max_window_size", property_fn=lambda progress, mol: mol.largest_window, y_label="Maximum Window Size / A", filter=lambda progress, mol: mol.largest_window is not None, progress_fn=apply(largest_window), ), stk.ProgressPlotter( filename="window_std", property_fn=lambda progress, mol: mol.window_std, y_label="Std. Dev. of Window Diameters / A", filter=lambda progress, mol: mol.window_std is not None, progress_fn=apply(window_std), ) ] stk.SelectionPlotter( filename="generational_selection", selector=generation_selector, ) stk.SelectionPlotter( filename="crossover_selection", selector=crossover_selector, ) stk.SelectionPlotter( filename="mutation_selection", selector=mutation_selector, )
) ), stk.ProgressPlotter( filename='window_std', property_fn=lambda progress, mol: mol.window_std, y_label='Std. Dev. of Window Diameters / A', filter=lambda progress, mol: mol.window_std is not None, progress_fn=apply(window_std), ), stk.ProgressPlotter( filename='pore_diameter', property_fn=lambda progress, mol: mol.pore_diameter, y_label='Pore Diameter / A', progress_fn=apply(pore_diameter), ), ] stk.SelectionPlotter( filename='generational_selection', selector=generation_selector, ) stk.SelectionPlotter( filename='crossover_selection', selector=crossover_selector, ) stk.SelectionPlotter( filename='mutation_selection', selector=mutation_selector, )
def main(): parser = argparse.ArgumentParser() parser.add_argument('--mongodb_uri', help='The MongoDB URI for the database to connect to.', default='mongodb://localhost:27017/') args = parser.parse_args() logging.basicConfig(level=logging.INFO) # Use a random seed to get reproducible results. random_seed = 4 generator = np.random.RandomState(random_seed) logger.info('Making building blocks.') # Load the building block databases. fluoros = tuple( get_building_blocks( path=pathlib.Path(__file__).parent / 'fluoros.txt', functional_group_factory=stk.FluoroFactory(), )) bromos = tuple( get_building_blocks( path=pathlib.Path(__file__).parent / 'bromos.txt', functional_group_factory=stk.BromoFactory(), )) initial_population = tuple(get_initial_population(fluoros, bromos)) # Write the initial population. for i, record in enumerate(initial_population): write(record.get_molecule(), f'initial_{i}.mol') client = pymongo.MongoClient(args.mongodb_uri) db = stk.ConstructedMoleculeMongoDb(client) fitness_db = stk.ValueMongoDb(client, 'fitness_values') # Plot selections. generation_selector = stk.Best( num_batches=25, duplicate_molecules=False, ) stk.SelectionPlotter('generation_selection', generation_selector) mutation_selector = stk.Roulette( num_batches=5, random_seed=generator.randint(0, 1000), ) stk.SelectionPlotter('mutation_selection', mutation_selector) crossover_selector = stk.Roulette( num_batches=3, batch_size=2, random_seed=generator.randint(0, 1000), ) stk.SelectionPlotter('crossover_selection', crossover_selector) fitness_calculator = stk.PropertyVector( property_functions=( get_num_rotatable_bonds, get_complexity, get_num_bad_rings, ), input_database=fitness_db, output_database=fitness_db, ) fitness_normalizer = stk.NormalizerSequence( fitness_normalizers=( # Prevent division by 0 error in DivideByMean, by ensuring # a value of each property to be at least 1. stk.Add((1, 1, 1)), stk.DivideByMean(), # Obviously, because all coefficients are equal, the # Multiply normalizer does not need to be here. However, # it's here to show that you can easily change the relative # importance of each component of the fitness value, by # changing the values of the coefficients. stk.Multiply((1, 1, 1)), stk.Sum(), stk.Power(-1), ), ) ea = stk.EvolutionaryAlgorithm( num_processes=1, initial_population=initial_population, fitness_calculator=fitness_calculator, mutator=stk.RandomMutator( mutators=( stk.RandomBuildingBlock( building_blocks=fluoros, is_replaceable=is_fluoro, random_seed=generator.randint(0, 1000), ), stk.SimilarBuildingBlock( building_blocks=fluoros, is_replaceable=is_fluoro, random_seed=generator.randint(0, 1000), ), stk.RandomBuildingBlock( building_blocks=bromos, is_replaceable=is_bromo, random_seed=generator.randint(0, 1000), ), stk.SimilarBuildingBlock( building_blocks=bromos, is_replaceable=is_bromo, random_seed=generator.randint(0, 1000), ), ), random_seed=generator.randint(0, 1000), ), crosser=stk.GeneticRecombination(get_gene=get_functional_group_type, ), generation_selector=generation_selector, mutation_selector=mutation_selector, crossover_selector=crossover_selector, fitness_normalizer=fitness_normalizer, ) logger.info('Starting EA.') generations = [] for generation in ea.get_generations(50): for record in generation.get_molecule_records(): db.put(record.get_molecule()) generations.append(generation) # Write the final population. for i, record in enumerate(generation.get_molecule_records()): write(record.get_molecule(), f'final_{i}.mol') logger.info('Making fitness plot.') # Normalize the fitness values across the entire EA before # plotting the fitness values. generations = tuple( normalize_generations( fitness_calculator=fitness_calculator, fitness_normalizer=fitness_normalizer, generations=generations, )) fitness_progress = stk.ProgressPlotter( generations=generations, get_property=lambda record: record.get_fitness_value(), y_label='Fitness Value', ) fitness_progress.write('fitness_progress.png') fitness_progress.get_plot_data().to_csv('fitness_progress.csv') logger.info('Making rotatable bonds plot.') rotatable_bonds_progress = stk.ProgressPlotter( generations=generations, get_property=lambda record: get_num_rotatable_bonds(record. get_molecule()), y_label='Number of Rotatable Bonds', ) rotatable_bonds_progress.write('rotatable_bonds_progress.png')