fitness_calculator = stk.PropertyVector(
pore_diameter,
window_std,
sa_score,
)
def valid_fitness(population, mol):
return None not in population.get_fitness_values()[mol]
fitness_normalizer = stk.Sequence(
stk.Power([1, -1, 1], filter=valid_fitness),
stk.DivideByMean(filter=valid_fitness),
stk.Multiply([1.0, 1.0, 10.0], filter=valid_fitness),
stk.Sum(filter=valid_fitness),
stk.ReplaceFitness(
replacement_fn=lambda population: min(f for _, f in population.
get_fitness_values().items()
if not isinstance(f, list)) / 2,
filter=lambda p, m: isinstance(p.get_fitness_values()[m], list),
))
# #####################################################################
# Exit condition.
# #####################################################################
terminator = stk.NumGenerations(60)
# #####################################################################
elif isinstance(f, list):
return None not in population.get_fitness_values()[mol]
else:
return False
# Minimize synthetic accessibility and asymmetry.
# Maximise pore volume and window size.
fitness_normalizer = stk.TryCatch(
stk.Sequence(
save_fitness,
stk.Power([1, 1, -1, -1], filter=valid_fitness),
stk.DivideByMean(filter=valid_fitness),
stk.Multiply([0, 0, 0, 0], filter=valid_fitness),
stk.Sum(filter=valid_fitness),
# Replace all fitness values that are lists or None with
# a small value.
stk.ReplaceFitness(replacement_fn=lambda population: 1e-8,
filter=lambda p, m: isinstance(
p.get_fitness_values()[m],
(list, type(None)),
)),
),
stk.ReplaceFitness(replacement_fn=lambda population: 1e-8, ))
# #####################################################################
# Exit condition.
# #####################################################################
elif isinstance(f, list):
return None not in population.get_fitness_values()[mol]
else:
return False
# Minimize synthetic accessibility and asymmetry.
# Maximise pore volume and window size.
fitness_normalizer = stk.TryCatch(
stk.Sequence(
save_fitness,
stk.Power([1, 1, -1, -1], filter=valid_fitness),
stk.DivideByMean(filter=valid_fitness),
stk.Multiply([10, 0, 5, 5], filter=valid_fitness),
stk.Sum(filter=valid_fitness),
# Replace all fitness values that are lists or None with
# a small value.
stk.ReplaceFitness(
replacement_fn=lambda population: 1e-8,
filter=lambda p, m:
isinstance(
p.get_fitness_values()[m],
(list, type(None)),
)
),
),
stk.ReplaceFitness(replacement_fn=lambda population: 1e-8,)
)
elif isinstance(f, list):
return None not in population.get_fitness_values()[mol]
else:
return False
# Minimize synthetic accessibility and asymmetry.
# Maximise pore volume and window size.
fitness_normalizer = stk.TryCatch(
stk.Sequence(
save_fitness,
stk.Power([1, 1, -1, -1], filter=valid_fitness),
stk.DivideByMean(filter=valid_fitness),
stk.Multiply([5, 1, 10, 5], filter=valid_fitness),
stk.Sum(filter=valid_fitness),
# Replace all fitness values that are lists or None with
# a small value.
stk.ReplaceFitness(replacement_fn=lambda population: 1e-8,
filter=lambda p, m: isinstance(
p.get_fitness_values()[m],
(list, type(None)),
)),
),
stk.ReplaceFitness(replacement_fn=lambda population: 1e-8, ))
# #####################################################################
# Exit condition.
# #####################################################################