def island_run(self):
algo = pg.algorithm(pg.pso(gen=self.generation))
pop = pg.population(self.problem, self.pop_size)
island = pg.island(algo=algo, pop=pop, udi=pg.mp_island())
island.evolve()
island.wait()
pop = island.get_population()
return pop.champion_f, pop.champion_x
def test_pygmo_single_generation(two_reservoir_wrapper):
""" Simple pygmo wrapper test. """
wrapper = two_reservoir_wrapper
prob = pg.problem(wrapper)
algo = pg.algorithm(pg.moead(gen=1))
pg.mp_island.init_pool(2)
isl = pg.island(algo=algo, prob=prob, size=50, udi=pg.mp_island())
isl.evolve(1)
def addIsland(arc, algo):
isl = pg.island(
udi=pg.mp_island(),
algo=algo,
prob=prob,
#b=pg.mp_bfe(),
b=pg.bfe(udbfe=multi_bfre2()),
size=16)
arc.push_back(isl)
def get_island(evaluate, params, hooks):
# config
# D = 8 decision space dimension
# N0 = 100 initial population
# Ng = 100 total generation
D, Ng, N0 = itemgetter('D', 'Ng', 'N0')(params)
algo = pg.algorithm(pg.de(gen=Ng))
algo.set_verbosity(int(Ng / 10))
prob = pg.problem(evaluate_wrapper(D, evaluate))
island = pg.island(algo=algo, prob=prob, size=N0, udi=pg.mp_island())
return island
def benchmark_differential_evolution_no_pool(generations):
island = pg_island(
algo=de(gen=generations),
prob=B2_UDP(getLowerBound(),getUpperBound(),'../../../../../sbml/b2.xml'),
size=10,
udi=mp_island(use_pool=False))
N = 50
import arrow
time_start = arrow.utcnow()
print('Differential Evolution (no pool, pop. size {})'.format(island.get_population().get_f().size))
for k in range(N):
island.evolve()
island.wait()
delta_t = arrow.utcnow() - time_start
print('DE {:2}/{}: best fitness {:9.2f}, fevals {}, duration {}'.format(
k,N,float(island.get_population().champion_f[0]),
island.get_population().problem.get_fevals(),
delta_t))
GA_based_optimize(pm,
cm,
prob_var.left_ind,
prob_var.mid_ind,
prob_var.right_ind,
gen=max_gen,
seed=rnd_seed,
log_lvl=log_lvl))
# Generating isl_num initial populations
pop_lst = [pg.population(prob) for _ in range(isl_num)]
[[p.push_back(next(GenerateIndividual)) for p in pop_lst]
for _ in range(pop_size)]
# Generating pm_lst × cm_lst algorithms with different crossover and
# mutation probability
alg_lst = [
pg.algorithm(
GA_based_optimize(p,
c,
prob_var.left_ind,
prob_var.mid_ind,
prob_var.right_ind,
gen=max_gen,
seed=rnd_seed,
log_lvl=log_lvl)) for c in cm_lst for p in pm_lst
]
# Creating islands
islands = [
pg.island(algo=a, pop=p, udi=pg.mp_island())
for a, p in zip(alg_lst, pop_lst)
]
import pygmo as pg
"""
Okay, so the lesson learned here is: not sure :-D
"""
if __name__ == "__main__":
prob = pg.problem(pg.rosenbrock(dim=5))
pool_creator = pg.mp_island()
# pool_creator.resize_pool(1)
pool_creator.init_pool(1)
island = pg.island(udi=pool_creator,
algo=pg.sga(gen=200),
pop=pg.population(prob, 200))
island.evolve()
island.wait()
print("island: ***** \n", island)
def pygmo_main(harmonic=False):
import pygmo as pg
from pywr.optimisation.pygmo import PygmoWrapper
def update_archive(pop, archive=None):
if archive is None:
combined_f = pop.get_f()
else:
combined_f = np.r_[archive, pop.get_f()]
indices = pg.select_best_N_mo(combined_f, 50)
new_archive = combined_f[indices, :]
new_archive = np.unique(new_archive.round(4), axis=0)
return new_archive
wrapper = PygmoWrapper(get_model_data(harmonic=harmonic))
prob = pg.problem(wrapper)
print(prob)
algo = pg.algorithm(pg.moead(gen=1))
# algo = pg.algorithm(pg.nsga2(gen=1))
pg.mp_island.init_pool(2)
isl = pg.island(algo=algo, prob=prob, size=50, udi=pg.mp_island())
ref_point = [216500, 4000]
pop = isl.get_population()
print("Evolving!")
archive = update_archive(pop)
hv = pg.hypervolume(archive)
vol = hv.compute(ref_point)
hvs = [
vol,
]
print(
"Gen: {:03d}, Hypervolume: {:6.4g}, Archive size: {:d}".format(
0, vol, len(archive)
)
)
for gen in range(20):
isl.evolve(1)
isl.wait_check()
pop = isl.get_population()
archive = update_archive(pop, archive)
hv = pg.hypervolume(archive)
vol = hv.compute(ref_point)
print(
"Gen: {:03d}, Hypervolume: {:6.4g}, Archive size: {:d}".format(
gen + 1, vol, len(archive)
)
)
hvs.append(vol)
hvs = pd.Series(hvs)
print("Finished!")
plt.scatter(archive[:, 0], archive[:, 1])
objectives = wrapper.model_objectives
plt.xlabel(objectives[0].name)
plt.ylabel(objectives[1].name)
plt.grid(True)
title = "Harmonic Control Curve" if harmonic else "Monthly Control Curve"
plt.savefig("{} Example ({}).pdf".format("pygmo", title), format="pdf")
fig, ax = plt.subplots()
ax.plot(hvs / 1e6, marker="o")
ax.grid(True)
ax.set_ylabel("Hypervolume")
ax.set_xlabel("Generation")
plt.savefig("{} Example Hypervolume ({}).pdf".format("pygmo", title), format="pdf")
plt.show()
def create_archipelago(
unknowns: list,
optimizers: list,
optimizers_kwargs: list,
pg_problem: pygmo.problem,
rel_pop_size: float,
archipelago_kwargs: dict,
log_each_nth_gen: int,
report_level: int,
) -> PyfoombArchipelago:
"""
Helper method for parallelized estimation using the generalized island model.
Creates the archipelago object for running several rounds of evolutions.
Arguments
---------
unknowns : list
The unknowns, sorted alphabetically and case-insensitive.
optimizers : list
A list of optimizers to be used on individual islands.
optimizers_kwargs : list
A list of corresponding kwargs.
pg_problem : pygmo.problem
An pygmo problem instance.
archipelago_kwargs : dict
Additional kwargs for archipelago creation.
log_each_nth_gen : int
Specifies at which each n-th generation the algorithm stores logs.
report_level : int
Prints information on the archipelago creation for values >= 1.
Returns
-------
archipelago : PyfoombArchipelago
"""
_cpus = joblib.cpu_count()
# There is one optimizer with a set of kwargs
if len(optimizers) == 1 and len(optimizers_kwargs) == 1:
optimizers = optimizers * _cpus
optimizers_kwargs = optimizers_kwargs * _cpus
# Several optimizers with the same kwargs
elif len(optimizers) > 1 and len(optimizers_kwargs) == 1:
optimizers_kwargs = optimizers_kwargs * len(optimizers)
# Several kwargs for the same optimizer
elif len(optimizers) == 1 and len(optimizers_kwargs) > 1:
optimizers = optimizers * len(optimizers_kwargs)
elif len(optimizers) != len(optimizers_kwargs):
raise ValueError(
'Number of optimizers does not match number of corresponding kwarg dicts'
)
# Get the optimizer intances
algos = [
PygmoOptimizers.get_optimizer_algo_instance(
name=_optimizers, kwargs=_optimizers_kwargs) for _optimizers,
_optimizers_kwargs in zip(optimizers, optimizers_kwargs)
]
# Update number of islands
n_islands = len(algos)
if report_level >= 1:
print(
f'Creating archipelago with {n_islands} islands. May take some time...'
)
pop_size = int(numpy.ceil(rel_pop_size * len(unknowns)))
prop_create_args = ((pg_problem, pop_size,
seed * numpy.random.randint(0, 1e4))
for seed, pop_size in enumerate([pop_size] *
n_islands))
try:
parallel_verbose = 0 if report_level == 0 else 1
with joblib.parallel_backend('loky', n_jobs=n_islands):
pops = joblib.Parallel(verbose=parallel_verbose)(map(
joblib.delayed(
ArchipelagoHelpers.parallel_create_population),
prop_create_args))
except Exception as ex:
print(
f'Parallelized archipelago creation failed, falling back to sequential\n{ex}'
)
pops = (
ArchipelagoHelpers.parallel_create_population(prop_create_arg)
for prop_create_arg in prop_create_args)
# Now create the empyty archipelago
if not 't' in archipelago_kwargs.keys():
archipelago_kwargs['t'] = pygmo.fully_connected()
archi = PyfoombArchipelago(**archipelago_kwargs)
archi.set_migrant_handling(pygmo.migrant_handling.preserve)
# Add the populations to the archipelago and wait for its construction
with contextlib.redirect_stdout(io.StringIO()):
for _pop, _algo in zip(pops, algos):
if log_each_nth_gen is not None:
_algo.set_verbosity(int(log_each_nth_gen))
_island = pygmo.island(algo=_algo,
pop=_pop,
udi=pygmo.mp_island())
archi.push_back(_island)
archi.wait_check()
return archi
