def naive_non_dominated_sort(F, **kwargs):
M = Dominator.calc_domination_matrix(F)
fronts = []
remaining = set(range(M.shape[0]))
while len(remaining) > 0:
front = []
for i in remaining:
is_dominated = False
dominating = set()
for j in front:
rel = M[i, j]
if rel == 1:
dominating.add(j)
elif rel == -1:
is_dominated = True
break
if is_dominated:
continue
else:
front = [x for x in front if x not in dominating]
front.append(i)
[remaining.remove(e) for e in front]
fronts.append(front)
return fronts
def find_non_dominated(F, _F=None):
M = Dominator.calc_domination_matrix(F, _F)
I = np.where(np.all(M >= 0, axis=1))[0]
return I
def fast_non_dominated_sort(F, **kwargs):
M = Dominator.calc_domination_matrix(F)
# calculate the dominance matrix
n = M.shape[0]
fronts = []
if n == 0:
return fronts
# final rank that will be returned
n_ranked = 0
ranked = np.zeros(n, dtype=int)
# for each individual a list of all individuals that are dominated by this one
is_dominating = [[] for _ in range(n)]
# storage for the number of solutions dominated this one
n_dominated = np.zeros(n)
current_front = []
for i in range(n):
for j in range(i + 1, n):
rel = M[i, j]
if rel == 1:
is_dominating[i].append(j)
n_dominated[j] += 1
elif rel == -1:
is_dominating[j].append(i)
n_dominated[i] += 1
if n_dominated[i] == 0:
current_front.append(i)
ranked[i] = 1.0
n_ranked += 1
# append the first front to the current front
fronts.append(current_front)
# while not all solutions are assigned to a pareto front
while n_ranked < n:
next_front = []
# for each individual in the current front
for i in current_front:
# all solutions that are dominated by this individuals
for j in is_dominating[i]:
n_dominated[j] -= 1
if n_dominated[j] == 0:
next_front.append(j)
ranked[j] = 1.0
n_ranked += 1
fronts.append(next_front)
current_front = next_front
return fronts
def train(self, x, f, cross_val=False, *args, **kwargs):
# f = (f - np.min(f))/(np.max(f)-np.min(f))
f_normalized = self.normalize.normalize(f, self.dataset_func)
kf = KFold(n_splits=self.n_splits)
best_acc = 0
best_loss = np.inf
if self.dataset_func is False:
cv = np.copy(f_normalized)
index = np.any(f_normalized > 0, axis=1)
cv[f_normalized <= 0] = 0
cv = np.sum(cv, axis=1)
acv = np.sum(f_normalized, axis=1)
acv[index] = np.copy(cv[index])
g_label = cv > 0
g_label[cv <= 0] = -1
# g_label = g > 0
# g_label[g <= 0] = -1
g_label = g_label.astype(int)
g_label = np.vstack(g_label)
# cross-validation
for train_index, test_index in kf.split(x):
train_data_x, test_data_x, train_data_f, test_data_f \
= x[train_index], x[test_index], f_normalized[train_index], f_normalized[test_index]
if self.dataset_func:
self.train_dominance_matrix = Dominator.calc_domination_matrix(
f_normalized[train_index])
self.test_dominance_matrix = Dominator.calc_domination_matrix(
f_normalized[test_index])
else:
train_data_cv, test_data_cv, train_g_label, test_g_label, \
= torch.from_numpy(cv[train_index]), torch.from_numpy(cv[test_index]), \
torch.from_numpy(g_label[train_index]), torch.from_numpy(g_label[test_index])
train_data_x, test_data_x, train_data_f, test_data_f \
= torch.from_numpy(train_data_x), torch.from_numpy(test_data_x), torch.from_numpy(train_data_f), \
torch.from_numpy(test_data_f)
train_indices = torch.from_numpy(
np.asarray(range(0, train_data_x.shape[0])))
test_indices = torch.from_numpy(
np.asarray(range(0, test_data_x.shape[0])))
if self.dataset_func:
self.trainset = DatasetFunction(train_indices, train_data_x,
train_data_f)
self.testset = DatasetFunction(test_indices, test_data_x,
test_data_f)
else:
self.trainset = DatasetConstraint(train_indices, train_data_x,
train_data_f, train_g_label)
self.testset = DatasetConstraint(test_indices, test_data_x,
test_data_f, test_g_label)
self.trainloader = torch.utils.data.DataLoader(
self.trainset, batch_size=self.batch_size, shuffle=True)
self.testloader = torch.utils.data.DataLoader(
self.testset, batch_size=self.batch_size, shuffle=True)
self.net = copy.deepcopy(self.neuralnet)
self.optimizer = optim.Adam(self.net.parameters(),
lr=0.01,
weight_decay=5e-1,
betas=(0.9, 0.999))
self.scheduler = CosineAnnealingLR(self.optimizer,
T_max=self.total_epoch,
eta_min=1e-7)
model, acc, loss = self.train_partition()
if self.best_accuracy_model:
if acc > best_acc:
self.model = model
else:
if loss < best_loss:
self.model = model
if not self.cross_val:
break
return self.model
def calc_as_fronts(F, G=None, only_pareto_front=False):
"""
try:
import pygmo as pg
ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(F)
except ImportError:
pass
"""
# calculate the dominance matrix
n = F.shape[0]
M = Dominator.calc_domination_matrix(F, G)
fronts = []
# final rank that will be returned
n_ranked = 0
ranked = np.zeros(n, dtype=np.int)
# for each individual a list of all individuals that are dominated by this one
is_dominating = [[] for _ in range(n)]
# storage for the number of solutions dominated this one
n_dominated = np.zeros(n)
current_front = []
for i in range(n):
for j in range(i + 1, n):
rel = M[i, j]
if rel == 1:
is_dominating[i].append(j)
n_dominated[j] += 1
elif rel == -1:
is_dominating[j].append(i)
n_dominated[i] += 1
if n_dominated[i] == 0:
current_front.append(i)
ranked[i] = 1.0
n_ranked += 1
if only_pareto_front:
return current_front
# append the first front to the current front
fronts.append(current_front)
# while not all solutions are assigned to a pareto front
while n_ranked < n:
next_front = []
# for each individual in the current front
for i in current_front:
# all solutions that are dominated by this individuals
for j in is_dominating[i]:
n_dominated[j] -= 1
if n_dominated[j] == 0:
next_front.append(j)
ranked[j] = 1.0
n_ranked += 1
fronts.append(next_front)
current_front = next_front
return fronts