generation_selector = stk.StochasticUniversalSampling(
num_batches=population_size,
random_seed=random_seed,
duplicate_batches=False,
duplicate_mols=False,
)
# #####################################################################
# Selector for selecting parents.
# #####################################################################
# Settings for tournament sampling for crossover.
crossover_selector = stk.Tournament(
num_batches=10,
batch_size=2,
duplicate_batches=False,
duplicate_mols=False,
random_seed=random_seed,
)
# #####################################################################
# Selector for selecting molecules for mutation.
# #####################################################################
mutation_selector = stk.Roulette(
num_batches=5,
duplicate_mols=False,
batch_size=1,
random_seed=random_seed,
)
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(0),
)
@pytest.fixture(
scope='session',
params=(
lambda population: CaseData(
selector=stk.Tournament(duplicate_molecules=False, ),
population=population,
selected=(
stk.Batch(
records=(population[0], ),
fitness_values={population[0]: 10},
key_maker=stk.Inchi(),
),
stk.Batch(
records=(population[1], ),
fitness_values={population[1]: 9},
key_maker=stk.Inchi(),
),
stk.Batch(
records=(population[2], ),
fitness_values={population[2]: 2},