def _watch_time_cost(res):
start_time = time.time()
T = 20
for one_tuple in res[:T]:
r, M = _preprocess(one_tuple)
vx, max_res = kMIQP(r, M, lamb=1.0, k=10)
print(vx, max_res)
cost_time = time.time() - start_time
print('User Per Second: %.4fs' % (cost_time / T))
def reduce_by_kMIQP(res, source_file, save_path=None):
outputs = []
k = 10
pd = []
div = []
xa = []
for li in np.arange(0, 10, 0.5):
lamb = li
xa.append(lamb)
for one_tuple in tqdm(res, ncols=77):
r, M = _preprocess(one_tuple)
vx, max_res = kMIQP(r, M, lamb, k=k)
groundtruth, preds, scores = one_tuple
preds = [preds[x] for x in vx]
outputs.append((groundtruth, preds, max_res))
if save_path is None:
prec, jacc = 0.0, 0.0
for groundtruth, preds, scores in outputs:
jacc += jaccard(preds)
prec += precision(groundtruth, preds)
pd.append(prec * 100 / len(outputs))
div.append(jacc / len(outputs))
else:
with open(save_path, 'wb') as f:
pickle.dump(outputs, f, pickle.HIGHEST_PROTOCOL)
fig = plt.figure()
ax1 = fig.add_subplot(111)
freqpd = [0.2599] * 20
freqdiv = [0.0444] * 20
l1, = ax1.plot(xa,
freqpd,
label='freq-p@' + str(k),
color='darkviolet',
marker='o')
l2, = ax1.plot(xa, pd, label='gurobi-p@' + str(k), color='r', marker='o')
#ax1.legend(loc=1)
ax1.set_ylabel('p@' + str(k))
ax2 = ax1.twinx()
l3, = ax2.plot(xa, div, label="gurobi-diversity", color='g', marker='*')
l4, = ax2.plot(xa, freqdiv, label="freq-diversity", color='y', marker='*')
#ax2.legend(loc=2)
ax2.set_ylabel('diversity')
ax1.set_xlabel('lamb(ele)')
print(xa)
print(pd)
print(div)
my_xticks = np.arange(0, 101, 10)
plt.xticks(my_xticks)
plt.legend(handles=[
l1,
l2,
l3,
l4,
], loc='best')
plt.show()
def reduce_by_kMIQP(res, source_file, save_path=None):
outputs = []
k = 5
for one_tuple in tqdm(res, ncols=77):
r, M = _preprocess(one_tuple)
vx, max_res = kMIQP(r, M, lamb=1.0, k=k)
groundtruth, preds, scores = one_tuple
preds = [preds[x] for x in vx]
outputs.append((groundtruth, preds, max_res))
if save_path is None:
print_all_metrics(source_file, outputs, k)
else:
with open(save_path, 'wb') as f:
pickle.dump(outputs, f, pickle.HIGHEST_PROTOCOL)
def _watch_converge(res):
random.shuffle(res)
tqdmInput = tqdm(res, ncols=77, leave=True)
prec, jacc = 0.0, 0.0
for iter, one_tuple in enumerate(tqdmInput):
r, M = _preprocess(one_tuple)
vx, max_res = kMIQP(r, M, lamb=1.0, k=10)
groundtruth, preds, scores = one_tuple
preds = [preds[x] for x in vx]
prec += precision(groundtruth, preds)
jacc += jaccard(preds)
tqdmInput.set_description('Prec@10: %.3f%% Div: %.3f' %
(prec * 100 / (iter + 1), jacc / (iter + 1)))
def _watch_log(res):
for one_tuple in res:
r, M = _preprocess(one_tuple)
vx, max_res = kMIQP(r, M, lamb=1.0, k=10, outputFlag=True)
print(vx, max_res)
break