pref_int_rvea.response = pd.DataFrame(
[response], columns=pref_int_rvea.content["dimensions_data"].columns
)
pref_int_nsga.response = pd.DataFrame(
[response], columns=pref_int_nsga.content["dimensions_data"].columns
)
pref_nimb_rvea.response = pd.DataFrame(
[response],
columns=pref_nimb_rvea.content["dimensions_data"].columns,
)
pref_nimb_nsga.response = pd.DataFrame(
[response],
columns=pref_nimb_nsga.content["dimensions_data"].columns,
)
a_post_rvea.iterate()
a_post_nsga.iterate()
_, pref_int_rvea = int_rvea.iterate(pref_int_rvea)
_, pref_int_nsga = int_nsga.iterate(pref_int_nsga)
_, pref_nimb_rvea = nimb_rvea.iterate(pref_nimb_rvea)
_, pref_nimb_nsga = nimb_nsga.iterate(pref_nimb_nsga)
scalar_rvea = scalar(a_post_rvea.population.objectives, response)
scalar_nsga = scalar(a_post_nsga.population.objectives, response)
scalar_irvea = scalar(int_rvea.population.objectives, response)
scalar_insga = scalar(int_nsga.population.objectives, response)
scalar_nrvea = scalar(nimb_rvea.population.objectives, response)
scalar_nnsga = scalar(nimb_nsga.population.objectives, response)
norm_rvea = np.linalg.norm(a_post_rvea.population.objectives, axis=1)
norm_nsga = np.linalg.norm(a_post_nsga.population.objectives, axis=1)
data = pd.DataFrame(np.hstack((x, y.objectives)),
columns=x_names + y_names)
problem = DataProblem(data=data,
variable_names=x_names,
objective_names=y_names)
problem.train(LipschitzianRegressor)
evolver_L_opt = oRVEA(problem, use_surrogates=True)
while evolver_L_opt.continue_evolution():
evolver_L_opt.iterate()
evolver_L = RVEA(problem, use_surrogates=True)
while evolver_L.continue_evolution():
evolver_L.iterate()
evolver_L_robust = robust_RVEA(problem, use_surrogates=True)
while evolver_L_robust.continue_evolution():
evolver_L_robust.iterate()
problem.train(GaussianProcessRegressor)
evolver_G_opt = oRVEA(problem, use_surrogates=True)
while evolver_G_opt.continue_evolution():
evolver_G_opt.iterate()
evolver_G = RVEA(problem, use_surrogates=True)
while evolver_G.continue_evolution():
evolver_G.iterate()
evolver_G_robust = robust_RVEA(problem, use_surrogates=True)
n_gen_per_iter=gen)
# initial reference point is specified randomly
response = np.random.rand(n_obj)
# run algorithms once with the randomly generated reference point
_, pref_int_rvea = int_rvea.requests()
_, pref_int_nsga = int_nsga.requests()
pref_int_rvea.response = pd.DataFrame(
[response],
columns=pref_int_rvea.content["dimensions_data"].columns)
pref_int_nsga.response = pd.DataFrame(
[response],
columns=pref_int_nsga.content["dimensions_data"].columns)
_, pref_int_rvea = int_rvea.iterate(pref_int_rvea)
_, pref_int_nsga = int_nsga.iterate(pref_int_nsga)
# build initial composite front
cf = generate_composite_front(int_rvea.population.objectives,
int_nsga.population.objectives)
# the following two lines for getting pareto front by using pymoo framework
problemR = get_problem(problem_name.lower(), n_var, n_obj)
ref_dirs = get_reference_directions("das-dennis",
n_obj,
n_partitions=12)
pareto_front = problemR.pareto_front(ref_dirs)
# creates uniformly distributed reference vectors
reference_vectors = ReferenceVectors(lattice_resolution, n_obj)
f3 = _ScalarObjective(name="f3", evaluator=f_3, maximize=[True])
f4 = _ScalarObjective(name="f4", evaluator=f_4)
f5 = _ScalarObjective(name="f5", evaluator=f_5)
varsl = variable_builder(
["x_1", "x_2"],
initial_values=[0.5, 0.5],
lower_bounds=[0.3, 0.3],
upper_bounds=[1.0, 1.0],
)
problem = MOProblem(variables=varsl, objectives=[f1, f2, f3, f4, f5])
evolver = RVEA(problem, interact=True, n_iterations=10, n_gen_per_iter=100)
_, pref = evolver.iterate()
n_iteration = 1
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("10.0.2.15", 5005))
# sock.bind(("127.0.0.1", 5005))
sock.listen(1)
print("Waiting for ComVis to connect...")
connection, client_addr = sock.accept()
print("Connection estabilished!")
while True:
try:
data = connection.recv(2048)
from desdeo_problem.testproblems.TestProblems import test_problem_builder
from desdeo_emo.EAs.RVEA import RVEA
from desdeo_emo.EAs.NSGAIII import NSGAIII
from desdeo_emo.othertools.plotlyanimate import animate_init_, animate_next_
dtlz3 = test_problem_builder("DTLZ3", n_of_variables=12, n_of_objectives=11)
evolver = RVEA(dtlz3, n_iterations=10)
figure = animate_init_(evolver.population.objectives, filename="dtlz3.html")
while evolver.continue_evolution():
evolver.iterate()
figure = animate_next_(
evolver.population.objectives,
figure,
filename="dtlz3.html",
generation=evolver._iteration_counter,
)