def trainab(model_name, dataset_name, iter_num):
# init T,R,SP
T = getSGGRes(model_name, dataset_name, "train", tasktype)
R = getGT(model_name, dataset_name, "train", tasktype)
SP, N = getPfromR(R, dataset_name)
print("T info: ", len(T), len(T[0]), T[0][0])
print("R info: ", len(R), len(R[0]), R[0][0])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dd = 1e23
# start iteration
for i in range(iter_num):
ga = GA(func=fitness, n_dim=2, size_pop=10, max_iter=50, \
lb=[0.0, 0.0], ub=[dd, dd], precision=1e-7)
#ga = GA(func=fitness, n_dim=2, size_pop=10, max_iter=4, \
# lb=[0.0, 0.0], ub=[dd, dd], precision=1e-7)
ga.to(device=device) #GPU
best_x, best_y = ga.run() # best_x={alpha,beta},best_y=Q-R
alpha, beta = best_x
print('iteration ', i, ': best alpha and beta: ', alpha, beta)
print('best_y:', best_y)
if best_y == 0:
print('best {alpha,beta}:', best_x, 'best_y', best_y)
end_cnt = fitness(best_x)
print("detect if zeros: ", end_cnt)
if end_cnt == 0:
break
else:
R = ReSet(alpha, beta)
SP, _ = getPfromR(R, dataset_name)
return best_x
def trainabGA(model_name,dataset_name,iter_num):
# init T,R,SP
T = getSGGRes(model_name,dataset_name,"train",tasktype)
R = getGT(model_name,dataset_name,"train",tasktype)
SP,N = getPfromR(R,dataset_name)
print("T info: ",len(T),len(T[0]),T[0][0])
print("R info: ",len(R),len(R[0]),R[0][0])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# start iteration
low = [0.0,0.0]
high = [1e6,1e6]
for i in range(iter_num):
#ga = GA(func=fitness, n_dim=2, size_pop=4, max_iter=10, \
# lb=[0.0, 0.0], ub=[1.0, 1.0], precision=1e-7)
#ga = GA(func=fitness, n_dim=2, size_pop=6, max_iter=50, \
# lb=low, ub=high, precision=1e-7)
#ga = GA(func=fitness, n_dim=2, size_pop=50, max_iter=50, \
# lb=low, ub=high, precision=1e-7)
ga = GA(func=fitnessNDCG, n_dim=2, size_pop=100, max_iter=100, \
lb=low, ub=high, precision=1e-7)
ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3).\
register(operator_name='ranking', operator=ranking.ranking). \
register(operator_name='crossover', operator=crossover.crossover_2point). \
register(operator_name='mutation', operator=mutation.mutation)
ga.to(device=device) #GPU
start_time = time.time()
best_x, best_y = ga.run()# best_x={alpha,beta},best_y=Q-R
print("run time: ",time.time() - start_time)
alpha, beta = best_x
print('iteration ',i,': best alpha and beta: ', alpha,beta)
print('best_y:', best_y)
print("sum_ndcg: ",-best_y-0.5*(alpha**2+beta**2))
if best_y==0:
print('best {alpha,beta}:',best_x,'best_y',best_y)
end_cnt = fitnessNDCG(best_x)
print("detect if zeros: ",end_cnt)
if end_cnt==0:
break
else:
#R = ReSet(alpha,beta)
R = addsmallNDCG(alpha,beta,R)
SP,_ = getPfromR(R,dataset_name)
cnt_now=fitness(best_x)
print(i," iter :", cnt_now)
return best_x
return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)
import torch
from sko.GA import GA
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
ga = GA(func=schaffer,
n_dim=2,
size_pop=50,
max_iter=800,
lb=[-1, -1],
ub=[1, 1],
precision=1e-7)
ga.to(device=device)
start_time = time.time()
best_x, best_y = ga.run()
print(time.time() - start_time)
print('best_x:', best_x, '\n', 'best_y:', best_y)
ga = GA(func=schaffer,
n_dim=2,
size_pop=50,
max_iter=800,
lb=[-1, -1],
ub=[1, 1],
precision=1e-7)
start_time = time.time()
best_x, best_y = ga.run()
print(time.time() - start_time)
