stk.MoleculeRecord(
topology_graph=get_topology_graph(7),
).with_fitness_value(100),
stk.MoleculeRecord(
topology_graph=get_topology_graph(7),
).with_fitness_value(100),
stk.MoleculeRecord(
topology_graph=get_topology_graph(9),
).with_fitness_value(1),
)
@pytest.fixture(
params=(
CaseData(
selector=stk.Best(),
population=population1,
selected=(
stk.Batch(
records=(population1[0], ),
fitness_values={population1[0]: 10},
key_maker=stk.Inchi(),
),
stk.Batch(
records=(population1[1], ),
fitness_values={population1[1]: 9},
key_maker=stk.Inchi(),
),
stk.Batch(
records=(population1[2], ),
fitness_values={population1[2]: 2},
stk.MoleculeRecord(
topology_graph=get_topology_graph(3), ).with_fitness_value(9),
stk.MoleculeRecord(
topology_graph=get_topology_graph(4), ).with_fitness_value(2),
stk.MoleculeRecord(
topology_graph=get_topology_graph(5), ).with_fitness_value(1),
stk.MoleculeRecord(
topology_graph=get_topology_graph(6), ).with_fitness_value(1),
)
@pytest.fixture(
scope='session',
params=(lambda population: CaseData(
selector=stk.RemoveMolecules(
remover=stk.Best(2),
selector=stk.Best(),
),
population=population,
selected=(
stk.Batch(
records=(population[2], ),
fitness_values={population[2]: 2},
key_maker=stk.Inchi(),
),
stk.Batch(
records=(population[3], ),
fitness_values={population[3]: 1},
key_maker=stk.Inchi(),
),
stk.Batch(
topology_graph=get_topology_graph(2), ).with_fitness_value(10),
stk.MoleculeRecord(
topology_graph=get_topology_graph(3), ).with_fitness_value(9),
stk.MoleculeRecord(
topology_graph=get_topology_graph(4), ).with_fitness_value(2),
stk.MoleculeRecord(
topology_graph=get_topology_graph(5), ).with_fitness_value(1),
stk.MoleculeRecord(
topology_graph=get_topology_graph(6), ).with_fitness_value(1),
)
@pytest.fixture(
params=(CaseData(
selector=stk.FilterBatches(
filter=stk.Best(4),
selector=stk.Best(),
),
population=population1,
selected=(
stk.Batch(
records=(population1[0], ),
fitness_values={population1[0]: 10},
key_maker=stk.Inchi(),
),
stk.Batch(
records=(population1[1], ),
fitness_values={population1[1]: 9},
key_maker=stk.Inchi(),
),
stk.Batch(
).with_fitness_value(2),
stk.MoleculeRecord(
topology_graph=get_topology_graph(5),
).with_fitness_value(1),
stk.MoleculeRecord(
topology_graph=get_topology_graph(6),
).with_fitness_value(1),
)
@pytest.fixture(
params=(
CaseData(
selector=stk.RemoveBatches(
remover=stk.Worst(4),
selector=stk.Best(),
),
population=population1,
selected=(
stk.Batch(
records=(population1[0], ),
fitness_values={population1[0]: 10},
key_maker=stk.Inchi(),
),
),
),
),
)
def remove_batches(request):
return request.param
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')
