def train(net, lr, trainloader, epoch):
"""
Train SSD
@args
net: (nn.Module) network
lr: (float) learning rate
trainloader: (DataLoader) dataloader
epoch: (int) training epoch
"""
net.train()
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=1e-4)
criterion = MultiBoxLoss(num_classes=config[args.dataset]['num_classes']+1)
progress_bar = ProgressBar(total=len(trainloader))
train_loss = 0
torch.set_printoptions(threshold=10000)
for batch_idx, (images, loc_targets, conf_targets) in enumerate(trainloader):
images = Variable(images.cuda())
loc_targets = Variable(loc_targets.cuda())
conf_targets = Variable(conf_targets.cuda())
optimizer.zero_grad()
loc_preds, conf_preds = net(images)
loc_loss, conf_loss, loss = criterion(loc_preds, loc_targets, conf_preds, conf_targets)
loss.backward()
optimizer.step()
writer.add_scalar('train/loss_loc', loc_loss, batch_idx + epoch * len(trainloader))
writer.add_scalar('train/loss_conf', conf_loss, batch_idx + epoch * len(trainloader))
writer.add_scalar('train/loss_total', loss, batch_idx + epoch * len(trainloader))
train_loss += loss.item()
progress_bar.move(leftmsg="training epoch " + str(epoch), rightmsg="loss: %.6f" % (train_loss/(batch_idx+1)))
def test(net, testloader, epoch):
net.eval()
criterion = MultiBoxLoss(num_classes=config[args.dataset]['num_classes']+1)
progress_bar = ProgressBar(total=len(testloader))
test_loss = 0
for batch_idx, (images, loc_targets, conf_targets) in enumerate(testloader):
images = Variable(images.cuda())
loc_targets = Variable(loc_targets.cuda())
conf_targets = Variable(conf_targets.cuda())
with torch.no_grad():
loc_preds, conf_preds = net(images)
_, _, loss = criterion(loc_preds, loc_targets, conf_preds, conf_targets)
test_loss += loss.item()
progress_bar.move(leftmsg="test epoch " + str(epoch), rightmsg="loss: %.6f" % (test_loss/(batch_idx+1)))
writer.add_scalar('test/loss', loss, epoch)
return test_loss / len(testloader) # average
def backtest(self,
testlist='all',
start='20110101',
end=None,
savepath=PATH + '/data/backtest_records.csv'):
"""
backtest(self, testlist='all', start='20110101', end=None):
回测系统，统计成功率和平均收益率和持有天数
# TODO: 对学习系统还应该有准确率和召回率
Input:
testlist: ('all' or list of code): 要进行回测的股票列表, 'all'为全部股票
start: (string of date): 回测数据的起始时间(含start)
end: (string of date): 回测数据的结束时间(含end)
savepath: (string): 生成的记录报告的保存路径,为None则不保存
Return:
None
"""
if testlist is 'all':
testlist = []
for pool in ['0', '3', '6']:
testlist.extend(get_stock_list(stock_pool=pool))
con0 = get_connection(stock_pool='0')
con3 = get_connection(stock_pool='3')
con6 = get_connection(stock_pool='6')
con = {'0': con0, '3': con3, '6': con6}
records, records_tmp = pd.DataFrame(), pd.DataFrame()
cnt = 0
bar = ProgressBar(total=len(testlist))
for code in testlist:
bar.log(code)
sql = 'select distinct * from ' + code + ' where date>=' + start\
+ ((' and date<='+end) if end is not None else '') + ';'
df = pd.read_sql(sql, con[code[2]])
self.df = _sort(df)
self.df['date'] = self.df['date'].apply(lambda x: Timestamp(x))
self.buy()
self.sell()
if self.record is not None and len(self.record) > 0:
self.record = self.record.apply(
lambda record: self.integrate(record), axis=1)
if self.record is not None and len(self.record) > 0:
records_tmp = self.record.append(records_tmp)
cnt += 1
if cnt >= 100:
records = records_tmp.append(records)
records_tmp = pd.DataFrame()
cnt = 0
bar.move()
records = records_tmp.append(records)
if len(records) > 0:
self.avggainrate = round(records['gainrate'].mean(),
4) - self.gainbias
self.successrate = round(
len(records[records['gainrate'] > self.gainbias]) /
len(records), 4)
self.keepdays = round(records['keepdays'].mean(), 2)
if savepath is not None:
records.to_csv(savepath)
print('records is saved at ' + savepath)
else:
print('No records')
transform = transforms.Compose([
transforms.Resize((300, 300)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
image = transform(img)
image = Variable(image[None, :, :, :].cuda())
timer.tic()
loc, conf = net(image)
batch_time = timer.toc()
boxes, labels, scores = data_encoder.decode(
loc.squeeze(0), F.softmax(conf.squeeze(0), dim=1))
draw = ImageDraw.Draw(img)
for i, box in enumerate(boxes):
if int(labels[i].item()) == 0:
continue
box[::2] *= img.width
box[1::2] *= img.height
draw.rectangle(list(box), outline='red')
draw.text(
(box[0], box[1]),
config[args.dataset]['class_name'][int(labels[i].item())] +
' %.3f' % scores[i].item())
img.save(os.path.join('./results', image_path))
progress_bar.move(leftmsg="test",
rightmsg="fps: %.2f" % (1 / batch_time))
