def runFrankWolfe(G, nsamples, k, log_file, opt_file, num_fw_iter, p, num_influ_iter, if_herd):
N = nx.number_of_nodes(G)
x = Variable(torch.Tensor([1.0*k/N]*N))
bufsize = 0
f = open(log_file, 'w', bufsize)
influ_obj = Influence(G, p, num_influ_iter)
tic = time.clock()
iter_num = 0
obj = getRelax(G, x, nsamples, influ_obj, if_herd)
toc = time.clock()
influ_val = []
influ_val_best = []
influ_best = -10
print "Iteration: ", iter_num, " obj = ", obj.item(), " time = ", (toc - tic), " Total/New/Cache: ", influ_obj.itr_total , influ_obj.itr_new , influ_obj.itr_cache
f.write(str(toc - tic) + " " + str(obj.item()) + " " + str(influ_obj.itr_total) + '/' + str(influ_obj.itr_new) + '/' + str(influ_obj.itr_cache) + "\n")
for iter_num in np.arange(1, num_fw_iter):
influ_obj.counter_reset()
grad = getGrad(G, x, nsamples, influ_obj, if_herd)
x_star = getCondGrad(grad, k)
step = 2.0/(iter_num + 2)
x = step*x_star + (1 - step)*x
obj = getRelax(G, x, nsamples, influ_obj, if_herd)
toc = time.clock()
print "Iteration: ", iter_num, " obj = ", obj.item(), " time = ", (toc - tic), " Total/New/Cache: ", influ_obj.itr_total , influ_obj.itr_new , influ_obj.itr_cache
f.write(str(toc - tic) + " " + str(obj.item()) + " " + str(influ_obj.itr_total) + '/' + str(influ_obj.itr_new) + '/' + str(influ_obj.itr_cache) + "\n")
if iter_num % 10 == 0:
#Round the current solution and get function values
top_k = Variable(torch.zeros(N)) #conditional grad
sorted_ind = torch.sort(x, descending = True)[1][0:k]
top_k[sorted_ind] = 1
influ = submodObj(G, top_k, p, 100)
influ_val.append(influ)
if influ > influ_best:
influ_best = influ
influ_val_best.append(influ_best)
f.close()
x_opt = x
#Round the optimum solution and get function values
top_k = Variable(torch.zeros(N)) #conditional grad
sorted_ind = torch.sort(x_opt, descending = True)[1][0:k]
top_k[sorted_ind] = 1
gt_val = submodObj(G, top_k, p, 100)
#Save optimum solution and value
f = open(opt_file, 'w')
for i in range(len(influ_val)):
f.write(str(influ_val[i].item()) + ' ' + str(influ_val_best[i].item()) + '\n')
f.write(str(gt_val.item()) + '\n')
for x_t in x_opt:
f.write(str(x_t.item()) + '\n')
f.close()
return x
def fw_reduced_nodes(G, nsamples, k, log_file, opt_file, iterates_file, num_fw_iter, p, num_influ_iter, if_herd, x_good, a):
N = nx.number_of_nodes(G)
D = 200
influ_obj = Influence(G, p, num_influ_iter)
important_nodes = getImportantNodes(G, D)
x = Variable(torch.Tensor([1e-4]*N))
x[important_nodes] = 1.0*k/D
bufsize = 0
f = open(log_file, 'w', bufsize)
f2 = open(iterates_file, 'w', bufsize)
tic = time.clock()
iter_num = 0
obj = getImportanceRelax(G, x_good, x, nsamples, influ_obj, if_herd, a)
toc = time.clock()
print "Iteration: ", iter_num, " obj = ", obj.item(), " time = ", (toc - tic), " Total/New/Cache: ", influ_obj.itr_total , influ_obj.itr_new , influ_obj.itr_cache
f.write(str(toc - tic) + " " + str(obj.item()) + " " + str(influ_obj.itr_total) + '/' + str(influ_obj.itr_new) + '/' + str(influ_obj.itr_cache) + "\n")
for x_t in x:
f2.write(str(x_t.item()) + '\n')
f2.write('\n')
for iter_num in np.arange(1, num_fw_iter):
influ_obj.counter_reset()
grad = getReducedPrunedGrad(G, x_good, x,nsamples, influ_obj, if_herd, a, important_nodes)
x_star = getCondGrad(grad, k)
step = 2.0/(iter_num + 2)
x = step*x_star + (1 - step)*x
obj = getImportanceRelax(G, x_good, x, nsamples, influ_obj, if_herd, a)
toc = time.clock()
print "Iteration: ", iter_num, " obj = ", obj.item(), " time = ", (toc - tic), " Total/New/Cache: ", influ_obj.itr_total , influ_obj.itr_new , influ_obj.itr_cache
f.write(str(toc - tic) + " " + str(obj.item()) + " " + str(influ_obj.itr_total) + '/' + str(influ_obj.itr_new) + '/' + str(influ_obj.itr_cache) + "\n")
for x_t in x:
f2.write(str(x_t.item()) + '\n')
f2.write('\n')
f.close()
f2.close()
x_opt = x
#Round the optimum solution and get function values
top_k = Variable(torch.zeros(N)) #conditional grad
sorted_ind = torch.sort(x_opt, descending = True)[1][0:k]
top_k[sorted_ind] = 1
gt_val = submodObj(G, top_k, p, 100)
#Save optimum solution and value
f = open(opt_file, 'w')
f.write(str(gt_val.item()) + '\n')
for x_t in x_opt:
f.write(str(x_t.item()) + '\n')
f.close()
return x_opt
