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Fix config and format

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This commit is contained in:
Jactus
2020-11-25 19:53:22 +08:00
parent 64b7748033
commit 7f1f86488c
3 changed files with 57 additions and 59 deletions
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2
examples/benchmarks/HATS/worflow_config_hats.yaml
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@@ -27,7 +27,7 @@ port_analysis_config: &port_analysis_config
task: task:
model: model:
class: HATS class: HATS
module_path: qlib.contrib.model.pytorch_gats module_path: qlib.contrib.model.pytorch_hats
kwargs: kwargs:
d_feat: 6 d_feat: 6
hidden_size: 64 hidden_size: 64

34
qlib/contrib/model/pytorch_sfm.py
View File

@@ -21,6 +21,7 @@ from ...model.base import Model
from ...data.dataset import DatasetH from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP from ...data.dataset.handler import DataHandlerLP
class SFM_Model(nn.Module): class SFM_Model(nn.Module):
def __init__(self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu"): def __init__(self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu"):
super().__init__() super().__init__()
@@ -71,7 +72,7 @@ class SFM_Model(nn.Module):
for ts in range(time_step): for ts in range(time_step):
x = input[:, ts, :] x = input[:, ts, :]
if(len(self.states)==0): #hasn't initialized yet if len(self.states) == 0: # hasn't initialized yet
self.init_states(x) self.init_states(x)
self.get_constants(x) self.get_constants(x)
p_tm1 = self.states[0] p_tm1 = self.states[0]
@@ -89,8 +90,9 @@ class SFM_Model(nn.Module):
x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)) # not sure whether I am doing in the right unsquuze i = self.inner_activation(
x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)
) # not sure whether I am doing in the right unsquuze
ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste)) ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre)) fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
@@ -152,13 +154,14 @@ class SFM_Model(nn.Module):
def get_constants(self, x): def get_constants(self, x):
constants = [] constants = []
constants.append([torch.tensor(1.).to(self.device) for _ in range(6)]) constants.append([torch.tensor(1.0).to(self.device) for _ in range(6)])
constants.append([torch.tensor(1.).to(self.device) for _ in range(7)]) constants.append([torch.tensor(1.0).to(self.device) for _ in range(7)])
array = np.array([float(ii) / self.freq_dim for ii in range(self.freq_dim)]) array = np.array([float(ii) / self.freq_dim for ii in range(self.freq_dim)])
constants.append(torch.tensor(array).to(self.device)) constants.append(torch.tensor(array).to(self.device))
self.states[5:] = constants self.states[5:] = constants
class SFM(Model): class SFM(Model):
"""SFM Model """SFM Model
@@ -221,7 +224,7 @@ class SFM(Model):
self.lr_decay_steps = lr_decay_steps self.lr_decay_steps = lr_decay_steps
self.optimizer = optimizer.lower() self.optimizer = optimizer.lower()
self.loss_type = loss self.loss_type = loss
self.device = 'cuda:%d'%(GPU) if torch.cuda.is_available() else 'cpu' self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.use_gpu = torch.cuda.is_available() self.use_gpu = torch.cuda.is_available()
self.seed = seed self.seed = seed
@@ -277,7 +280,7 @@ class SFM(Model):
freq_dim=self.freq_dim, freq_dim=self.freq_dim,
dropout_W=self.dropout_W, dropout_W=self.dropout_W,
dropout_U=self.dropout_U, dropout_U=self.dropout_U,
device = self.device device=self.device,
) )
if optimizer.lower() == "adam": if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr) self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
@@ -303,14 +306,7 @@ class SFM(Model):
self._fitted = False self._fitted = False
self.sfm_model.to(self.device) self.sfm_model.to(self.device)
def fit( def fit(self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, **kwargs):
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
**kwargs
):
df_train, df_valid = dataset.prepare( df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
@@ -399,7 +395,7 @@ class SFM(Model):
# update learning rate # update learning rate
self.scheduler.step(cur_loss_val) self.scheduler.step(cur_loss_val)
if self.device != 'cpu': if self.device != "cpu":
torch.cuda.empty_cache() torch.cuda.empty_cache()
def get_loss(self, pred, target, loss_type): def get_loss(self, pred, target, loss_type):
@@ -432,11 +428,11 @@ class SFM(Model):
x_batch = torch.from_numpy(x_values[begin:end]).float() x_batch = torch.from_numpy(x_values[begin:end]).float()
if self.device != 'cpu': if self.device != "cpu":
x_batch = x_batch.to(self.device) x_batch = x_batch.to(self.device)
with torch.no_grad(): with torch.no_grad():
if self.device != 'cpu': if self.device != "cpu":
pred = self.sfm_model(x_batch).detach().cpu().numpy() pred = self.sfm_model(x_batch).detach().cpu().numpy()
else: else:
pred = self.sfm_model(x_batch).detach().cpu().numpy() pred = self.sfm_model(x_batch).detach().cpu().numpy()
@@ -461,8 +457,10 @@ class SFM(Model):
self.sfm_model.load_state_dict(torch.load(_model_path)) self.sfm_model.load_state_dict(torch.load(_model_path))
self._fitted = True self._fitted = True
class AverageMeter(object): class AverageMeter(object):
"""Computes and stores the average and current value""" """Computes and stores the average and current value"""
def __init__(self): def __init__(self):
self.reset() self.reset()