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Update TCTS. (#643)

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* Update TCTS.

* Update TCTS README.

* Update TCTS README.

* Update TCTS.

Co-authored-by: lewwang <lwwang@microsoft.com>
This commit is contained in:
Lewen Wang
2021-10-12 10:08:48 +08:00
committed by GitHub
parent c0ce712be9
commit 17ea44e0cf
5 changed files with 63 additions and 63 deletions
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44
qlib/contrib/model/pytorch_tcts.py
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@@ -61,8 +61,9 @@ class TCTS(Model):
weight_lr=5e-7,
steps=3,
GPU=0,
seed=None,
target_label=0,
mode="soft",
seed=None,
lowest_valid_performance=0.993,
**kwargs
):
@@ -87,6 +88,7 @@ class TCTS(Model):
self.weight_lr = weight_lr
self.steps = steps
self.target_label = target_label
self.mode = mode
self.lowest_valid_performance = lowest_valid_performance
self._fore_optimizer = fore_optimizer
self._weight_optimizer = weight_optimizer
@@ -100,6 +102,8 @@ class TCTS(Model):
"\nn_epochs : {}"
"\nbatch_size : {}"
"\nearly_stop : {}"
"\ntarget_label : {}"
"\nmode : {}"
"\nloss_type : {}"
"\nvisible_GPU : {}"
"\nuse_GPU : {}"
@@ -111,6 +115,8 @@ class TCTS(Model):
n_epochs,
batch_size,
early_stop,
target_label,
mode,
loss,
GPU,
self.use_gpu,
@@ -120,9 +126,17 @@ class TCTS(Model):
def loss_fn(self, pred, label, weight):
loc = torch.argmax(weight, 1)
loss = (pred - label[np.arange(weight.shape[0]), loc]) ** 2
return torch.mean(loss)
if self.mode == "hard":
loc = torch.argmax(weight, 1)
loss = (pred - label[np.arange(weight.shape[0]), loc]) ** 2
return torch.mean(loss)
elif self.mode == "soft":
loss = (pred - label.transpose(0, 1)) ** 2
return torch.mean(loss * weight.transpose(0, 1))
else:
raise NotImplementedError("mode {} is not supported!".format(self.mode))
def train_epoch(self, x_train, y_train, x_valid, y_valid):
@@ -132,6 +146,10 @@ class TCTS(Model):
indices = np.arange(len(x_train_values))
np.random.shuffle(indices)
task_embedding = torch.zeros([self.batch_size, self.output_dim])
task_embedding[:, self.target_label] = 1
task_embedding = task_embedding.to(self.device)
init_fore_model = copy.deepcopy(self.fore_model)
for p in init_fore_model.parameters():
p.init_fore_model = False
@@ -155,12 +173,13 @@ class TCTS(Model):
init_pred = init_fore_model(feature)
pred = self.fore_model(feature)
dis = init_pred - label.transpose(0, 1)
weight_feature = torch.cat((feature, dis.transpose(0, 1), label, init_pred.view(-1, 1)), 1)
weight_feature = torch.cat(
(feature, dis.transpose(0, 1), label, init_pred.view(-1, 1), task_embedding), 1
)
weight = self.weight_model(weight_feature)
loss = self.loss_fn(pred, label, weight) # hard
loss = self.loss_fn(pred, label, weight)
self.fore_optimizer.zero_grad()
loss.backward()
@@ -188,11 +207,11 @@ class TCTS(Model):
pred = self.fore_model(feature)
dis = pred - label.transpose(0, 1)
weight_feature = torch.cat((feature, dis.transpose(0, 1), label, pred.view(-1, 1)), 1)
weight_feature = torch.cat((feature, dis.transpose(0, 1), label, pred.view(-1, 1), task_embedding), 1)
weight = self.weight_model(weight_feature)
loc = torch.argmax(weight, 1)
valid_loss = torch.mean((pred - label[:, 0]) ** 2)
loss = torch.mean(-valid_loss * torch.log(weight[np.arange(weight.shape[0]), loc]))
valid_loss = torch.mean((pred - label[:, abs(self.target_label)]) ** 2)
loss = torch.mean(valid_loss * torch.log(weight[np.arange(weight.shape[0]), loc]))
self.weight_optimizer.zero_grad()
loss.backward()
@@ -207,7 +226,6 @@ class TCTS(Model):
self.fore_model.eval()
scores = []
losses = []
indices = np.arange(len(x_values))
@@ -277,7 +295,7 @@ class TCTS(Model):
dropout=self.dropout,
)
self.weight_model = MLPModel(
d_feat=360 + 2 * self.output_dim + 1,
d_feat=360 + 3 * self.output_dim + 1,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
@@ -303,8 +321,6 @@ class TCTS(Model):
best_loss = np.inf
best_epoch = 0
stop_round = 0
fore_best_param = copy.deepcopy(self.fore_optimizer.state_dict())
weight_best_param = copy.deepcopy(self.weight_optimizer.state_dict())
for epoch in range(self.n_epochs):
print("Epoch:", epoch)