def predict(self, x, out, *args, **kwargs):
f = []
g = []
for i in range(0, self.problem.n_obj):
_f = self.model_list["f" + str(i + 1)].predict(x)
f.append(_f)
if self.problem.n_constr > 0:
_g = self.model_list["G" + "_" +
str(self.g_aggregate_func)].predict(x)
else:
_g = np.zeros(x.shape[0])
g.append(_g)
F = np.column_stack(f)
G = np.column_stack(g)
d = prepare_data(f=F,
g=g,
acq_func=['l_' + self.f_aggregate_func],
ref_dirs=self.ref_dirs,
curr_ref_id=self.curr_ref_id)
out["F"] = d['l_' + self.f_aggregate_func]
out["G"] = G
def train(self, x, f, g, *args, **kwargs):
self.select_framework(x, f, g, *args, **kwargs)
for acq in self.best_frameworks_acq_list:
d = prepare_data(x=x, f=f, g=g, acq_func=[acq], ref_dirs=self.ref_dirs,
curr_ref_id=-1)
self.model_list[acq].train(x, d[acq])
def train(self, x, f, g, *args, **kwargs):
for i in range(len(self.ref_dirs)):
d = prepare_data(f=f, g=g, acq_func=[self.f_aggregate_func], ref_dirs=self.ref_dirs,
curr_ref_id=i)
self.model_list["l"+str(i+1)+"_"+str(self.f_aggregate_func)].train(x, d[self.f_aggregate_func])
if self.problem.n_constr > 0:
for i in range(0, self.problem.n_constr):
self.model_list["g" + str(i + 1)].train(x, g[:, i])
def train(self, x, f, g, *args, **kwargs):
string = 'fg_M5_' + str(self.curr_ref_id +
1) + '_' + self.m5_fg_aggregate_func
d = prepare_data(f=f,
g=g,
acq_func=[string],
ref_dirs=self.ref_dirs,
curr_ref_id=self.curr_ref_id)
self.model_list[string].train(x, d[string])
def train(self, x, f, g, *args, **kwargs):
for i in range(len(self.ref_dirs)):
string = "fg_M6_" + str(i + 1) + "_" + str(
self.m6_fg_aggregate_func)
d = prepare_data(f=f,
g=g,
acq_func=[string],
ref_dirs=self.ref_dirs,
curr_ref_id=i)
self.model_list[string].train(x, d[string])
def train(self, x, f, g, *args, **kwargs):
for i in range(0, self.problem.n_obj):
self.model_list["f" + str(i + 1)].train(x, f[:, i])
if self.problem.n_constr > 0:
d = prepare_data(f=f,
g=g,
acq_func=[self.g_aggregate_func],
ref_dirs=self.ref_dirs,
curr_ref_id=self.curr_ref_id)
self.model_list["G" + "_" + str(self.g_aggregate_func)].train(
x, d[self.g_aggregate_func])
def calculate_sep(self, problem, actual_data, prediction_data, n_split):
err = []
for partition in range(n_split):
I_temp = np.arange(0, actual_data['f1'][partition].shape[0])
I = np.asarray(list(product(I_temp, I_temp)))
# find cv for the current partition
if problem.n_constr > 0:
G = []
G_pred = []
for j in range(problem.n_constr):
G.append(actual_data['G_' +
self.g_aggregate_func][partition])
G_pred.append(
prediction_data['G_' +
self.g_aggregate_func][partition])
G = np.column_stack(G)
G_pred = np.column_stack(G_pred)
cv = np.copy(G)
cv[G <= 0] = 0
cv = np.sum(cv, axis=1)
cv_pred = np.copy(G_pred)
cv_pred[G_pred <= 0] = 0
cv_pred = np.sum(cv_pred, axis=1)
else:
cv = np.zeros([I_temp.shape[0], 1])
cv_pred = np.zeros([I_temp.shape[0], 1])
# objective values for the current partition
f = []
f_pred = []
for i in range(problem.n_obj):
f.append(actual_data['f' + str(i + 1)][partition])
f_pred.append(prediction_data['f' + str(i + 1)][partition])
F = np.column_stack(f)
F_pred = np.column_stack(f_pred)
# compute average error over all reference directions
temp_err = 0
count = 0
for j in range(self.ref_dirs.shape[0]):
d = prepare_data(f=F,
acq_func=['l_' + self.f_aggregate_func],
ref_dirs=self.ref_dirs,
curr_ref_id=j)
f = d['l_' + self.f_aggregate_func]
d = prepare_data(f=F_pred,
acq_func=['l_' + self.f_aggregate_func],
ref_dirs=self.ref_dirs,
curr_ref_id=j)
f_pred = d['l_' + self.f_aggregate_func]
for i in range(I.shape[0]):
count = count + 1
d1 = self.constrained_domination(f[I[i, 0]], f[I[i, 1]],
cv[I[i, 0]], cv[I[i, 1]])
d2 = self.constrained_domination(f_pred[I[i, 0]],
f_pred[I[i, 1]],
cv_pred[I[i, 0]],
cv_pred[I[i, 1]])
if d1 != d2:
temp_err = temp_err + 1
temp_err = temp_err / count
err.append(temp_err)
return np.asarray(err)
