def validate(val_loader, model, criterion):
losses = AverageMeter()
score_micro = np.zeros(3)
test_p1, test_p3, test_p5 = 0, 0, 0
test_ndcg1, test_ndcg3, test_ndcg5 = 0, 0, 0
model.eval()
with torch.no_grad():
for batch_idx, (input, target) in enumerate(val_loader):
input = input.cuda()
target = target.cuda()
output = model(input)
loss = criterion(output, target.float())
target = target.data.cpu().float()
output = output.data.cpu()
_p1, _p3, _p5 = precision_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_p1 += _p1
test_p3 += _p3
test_p5 += _p5
_ndcg1, _ndcg3, _ndcg5 = ndcg_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_ndcg1 += _ndcg1
test_ndcg3 += _ndcg3
test_ndcg5 += _ndcg5
output[output > 0.5] = 1
output[output <= 0.5] = 0
score_micro += [
precision_score(target, output, average='micro'),
recall_score(target, output, average='micro'),
f1_score(target, output, average='micro')
]
losses.update(loss.item(), input.size(0))
# plot progress
np.set_printoptions(formatter={'float': '{: 0.4}'.format})
print('the result of micro: \n', score_micro / len(val_loader))
test_p1 /= len(val_loader)
test_p3 /= len(val_loader)
test_p5 /= len(val_loader)
test_ndcg1 /= len(val_loader)
test_ndcg3 /= len(val_loader)
test_ndcg5 /= len(val_loader)
print("precision@1 : %.4f , precision@3 : %.4f , precision@5 : %.4f " %
(test_p1, test_p3, test_p5))
print("ndcg@1 : %.4f , ndcg@3 : %.4f , ndcg@5 : %.4f " %
(test_ndcg1, test_ndcg3, test_ndcg5))
return score_micro / len(val_loader)
def validate(val_loader, model):
data_time = AverageMeter()
microF1 = AverageMeter()
test_p1, test_p3, test_p5 = 0, 0, 0
test_ndcg1, test_ndcg3, test_ndcg5 = 0, 0, 0
model.eval()
with torch.no_grad():
end = time.time()
for batch_idx, (input, target) in enumerate(val_loader):
data_time.update(time.time() - end)
input = input.cuda()
target = target.cuda()
output = model(input)
target = target.data.cpu().float()
output = output.data.cpu()
_p1, _p3, _p5 = precision_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_p1 += _p1
test_p3 += _p3
test_p5 += _p5
_ndcg1, _ndcg3, _ndcg5 = ndcg_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_ndcg1 += _ndcg1
test_ndcg3 += _ndcg3
test_ndcg5 += _ndcg5
output[output > 0.5] = 1
output[output <= 0.5] = 0
micro, macro = calc_f1(target, output)
microF1.update(micro.item(), input.size(0))
np.set_printoptions(formatter={'float': '{: 0.4}'.format})
print('the result of micro: \n', microF1.avg)
test_p1 /= len(val_loader)
test_p3 /= len(val_loader)
test_p5 /= len(val_loader)
test_ndcg1 /= len(val_loader)
test_ndcg3 /= len(val_loader)
test_ndcg5 /= len(val_loader)
print("precision@1 : %.4f , precision@3 : %.4f , precision@5 : %.4f " %
(test_p1, test_p3, test_p5))
print("ndcg@1 : %.4f , ndcg@3 : %.4f , ndcg@5 : %.4f " %
(test_ndcg1, test_ndcg3, test_ndcg5))
return (microF1.avg)
def get_full_sort_score(self, epoch, answers, pred_list):
recall, ndcg = [], []
for k in [5, 10, 15, 20]:
recall.append(recall_at_k(answers, pred_list, k))
ndcg.append(ndcg_k(answers, pred_list, k))
post_fix = {
"Epoch": epoch,
"HIT@5": '{:.4f}'.format(recall[0]),
"NDCG@5": '{:.4f}'.format(ndcg[0]),
"HIT@10": '{:.4f}'.format(recall[1]),
"NDCG@10": '{:.4f}'.format(ndcg[1]),
"HIT@20": '{:.4f}'.format(recall[3]),
"NDCG@20": '{:.4f}'.format(ndcg[3])
}
print(post_fix)
with open(self.args.log_file, 'a') as f:
f.write(str(post_fix) + '\n')
return [recall[0], ndcg[0], recall[1], ndcg[1], recall[3],
ndcg[3]], str(post_fix)
def fine_tuning(train_loader, model, criterion, optimizer):
F1 = np.zeros(54)
score_micro = np.zeros(3)
score_macro = np.zeros(3)
data_time = AverageMeter()
losses = AverageMeter()
microF1 = AverageMeter()
macroF1 = AverageMeter()
model.train()
test_p1, test_p3, test_p5 = 0, 0, 0
test_ndcg1, test_ndcg3, test_ndcg5 = 0, 0, 0
end = time.time()
# bar = Bar('Training', max=len(train_loader))
for batch_idx, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
input = input.cuda()
target = target.cuda()
output = model(input)
loss = criterion(output, target.float())
target = target.data.cpu().float()
output = output.data.cpu()
micro, macro = calc_f1(target, output)
losses.update(loss.item(), input.size(0))
microF1.update(micro.item(), input.size(0))
macroF1.update(macro.item(), input.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
_p1, _p3, _p5 = precision_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_p1 += _p1
test_p3 += _p3
test_p5 += _p5
_ndcg1, _ndcg3, _ndcg5 = ndcg_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_ndcg1 += _ndcg1
test_ndcg3 += _ndcg3
test_ndcg5 += _ndcg5
output[output > 0.5] = 1
output[output <= 0.5] = 0
for l in range(54):
F1[l] += f1_score(target[:, l], output[:, l], average='binary')
# precision[l] += precision_score(target[:, l], output[:, l], average='binary')
# recall[l] += recall_score(target[:, l], output[:, l], average='binary')
# micro, macro = calc_f1(target, output)
# acc += accuracy_score(target, output)
# print("acc",acc)
score_micro += [
precision_score(target, output, average='micro'),
recall_score(target, output, average='micro'),
f1_score(target, output, average='micro')
]
score_macro += [
precision_score(target, output, average='macro'),
recall_score(target, output, average='macro'),
f1_score(target, output, average='macro')
]
# acc = calc_acc(target, output)
np.set_printoptions(formatter={'float': '{: 0.4}'.format})
print('the result of F1: \n', F1 / len(train_loader))
print('the result of micro: \n', score_micro / len(train_loader))
print('the result of macro: \n', score_macro / len(train_loader))
test_p1 /= len(train_loader)
test_p3 /= len(train_loader)
test_p5 /= len(train_loader)
test_ndcg1 /= len(train_loader)
test_ndcg3 /= len(train_loader)
test_ndcg5 /= len(train_loader)
print("precision@1 : %.4f , precision@3 : %.4f , precision@5 : %.4f " %
(test_p1, test_p3, test_p5))
print("ndcg@1 : %.4f , ndcg@3 : %.4f , ndcg@5 : %.4f " %
(test_ndcg1, test_ndcg3, test_ndcg5))
def validate(val_loader, model):
F1 = np.zeros(54)
score_micro = np.zeros(3)
score_macro = np.zeros(3)
test_p1, test_p3, test_p5 = 0, 0, 0
test_ndcg1, test_ndcg3, test_ndcg5 = 0, 0, 0
model.eval()
with torch.no_grad():
for batch_idx, (input, target) in enumerate(val_loader):
# measure data loading time
input = input.cuda()
target = target.cuda()
output = model(input)
target = target.data.cpu().float()
output = output.data.cpu()
_p1, _p3, _p5 = precision_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_p1 += _p1
test_p3 += _p3
test_p5 += _p5
_ndcg1, _ndcg3, _ndcg5 = ndcg_k(output.topk(k=5)[1].numpy(),
target.numpy(),
k=[1, 3, 5])
test_ndcg1 += _ndcg1
test_ndcg3 += _ndcg3
test_ndcg5 += _ndcg5
output[output > 0.5] = 1
output[output <= 0.5] = 0
for l in range(54):
F1[l] += f1_score(target[:, l], output[:, l], average='binary')
# precision[l] += precision_score(target[:, l], output[:, l], average='binary')
# recall[l] += recall_score(target[:, l], output[:, l], average='binary')
# micro, macro = calc_f1(target, output)
# acc += accuracy_score(target, output)
# print("acc",acc)
score_micro += [
precision_score(target, output, average='micro'),
recall_score(target, output, average='micro'),
f1_score(target, output, average='micro')
]
score_macro += [
precision_score(target, output, average='macro'),
recall_score(target, output, average='macro'),
f1_score(target, output, average='macro')
]
# acc = calc_acc(target, output)
np.set_printoptions(formatter={'float': '{: 0.4}'.format})
print('the result of F1: \n', F1 / len(val_loader))
print('the result of micro: \n', score_micro / len(val_loader))
print('the result of macro: \n', score_macro / len(val_loader))
test_p1 /= len(val_loader)
test_p3 /= len(val_loader)
test_p5 /= len(val_loader)
test_ndcg1 /= len(val_loader)
test_ndcg3 /= len(val_loader)
test_ndcg5 /= len(val_loader)
print("precision@1 : %.4f , precision@3 : %.4f , precision@5 : %.4f " %
(test_p1, test_p3, test_p5))
print("ndcg@1 : %.4f , ndcg@3 : %.4f , ndcg@5 : %.4f " %
(test_ndcg1, test_ndcg3, test_ndcg5))