def filter_fn(mol): return not hasattr(mol, fn.__name__) def inner(progress): all(True for _ in map(fn, filter(filter_fn, progress))) return inner plotters = [ stk.ProgressPlotter( filename="fitness_plot", property_fn=lambda progress, mol: progress.get_fitness_values()[mol], y_label="Fitness", filter=lambda progress, mol: progress.get_fitness_values()[mol], progress_fn=lambda progress: progress. set_fitness_values_from_calculators( fitness_calculator=fitness_calculator, fitness_normalizer=fitness_normalizer, num_processes=num_processes, ), ), stk.ProgressPlotter( filename="synthetic_accesibility_scores", property_fn=lambda progress, mol: mol.sa_score, y_label="Synthetic Accessibility / unitless", filter=lambda progress, mol: mol.sa_score is not None, progress_fn=apply(synthetic_accesibility_func), ), stk.ProgressPlotter( filename="volume_plot", property_fn=lambda progress, mol: mol.pore_diameter,
return not hasattr(mol, fn.__name__) def inner(progress): all(True for _ in map(fn, filter(filter_fn, progress))) return inner plotters = [ stk.ProgressPlotter( filename='fitness_plot', property_fn=lambda progress, mol: progress.get_fitness_values()[mol], y_label='Fitness', filter=lambda progress, mol: progress.get_fitness_values()[mol], progress_fn=lambda progress: progress.set_fitness_values_from_calculators( fitness_calculator=fitness_calculator, fitness_normalizer=fitness_normalizer, num_processes=num_processes, ) ), 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(
plotter=stk.ProgressPlotter( generations=( stk.Generation( molecule_records=get_generation(0, 1, 2), mutation_records=(), crossover_records=(), ), stk.Generation( molecule_records=get_generation(10, 20, 30), mutation_records=(), crossover_records=(), ), stk.Generation( molecule_records=get_generation(40, 50, 60), mutation_records=(), crossover_records=(), ), stk.Generation( molecule_records=get_generation(40, 50, 60), mutation_records=(), crossover_records=(), ), stk.Generation( molecule_records=get_generation(70, 80, 90), mutation_records=(), crossover_records=(), ), ), get_property=lambda record: record.get_fitness_value(), y_label='Fitness Value', filter=lambda record: record.get_fitness_value() is not None, ),
fitness_normalizer = stk.NormalizerSequence(stk.Power(0.5), stk.Sum()) # ##################################################################### # Exit condition. # ##################################################################### terminator = stk.NumGenerations(25) # ##################################################################### # Make plotters. # ##################################################################### plotters = [ stk.ProgressPlotter( filename='fitness_plot', property_fn=lambda mol: mol.fitness, y_label='Fitness', ), stk.ProgressPlotter( filename='atom_number_plot', property_fn=lambda mol: len(mol.atoms), y_label='Number of Atoms', ) ] 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')
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) ea = stk.EvolutionaryAlgorithm( initial_population=initial_population, fitness_calculator=stk.FitnessFunction(get_fitness_value), 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=stk.Best( num_batches=25, duplicate_molecules=False, ), mutation_selector=stk.Roulette( num_batches=5, random_seed=generator.randint(0, 1000), ), crossover_selector=stk.Roulette( num_batches=3, batch_size=2, random_seed=generator.randint(0, 1000), ), ) 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.') fitness_progress = stk.ProgressPlotter( generations=generations, get_property=lambda record: record.get_fitness_value(), y_label='Fitness Value', ) fitness_progress.write('fitness_progress.png') logger.info('Making rotatable bonds plot.') rotatable_bonds_progress = stk.ProgressPlotter( generations=generations, get_property=get_num_rotatable_bonds, y_label='Number of Rotatable Bonds', ) rotatable_bonds_progress.write('rotatable_bonds_progress.png')