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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:
model:
class: HATS
module_path: qlib.contrib.model.pytorch_gats
module_path: qlib.contrib.model.pytorch_hats
kwargs:
d_feat: 6
hidden_size: 64

10
examples/workflow_by_code_sfm.py
View File

@@ -72,8 +72,8 @@ if __name__ == "__main__":
"kwargs": {
"d_feat": 6,
"hidden_size": 32,
"output_dim" : 16,
"freq_dim" : 25,
"output_dim": 16,
"freq_dim": 25,
"dropout_W": 0.5,
"dropout_U": 0.5,
"n_epochs": 200,
@@ -82,9 +82,9 @@ if __name__ == "__main__":
"early_stop": 20,
"eval_steps": 5,
"loss": "mse",
"lr_decay" : 0.96,
"lr_decay_steps" : 100,
"optimizer" : "adam",
"lr_decay": 0.96,
"lr_decay_steps": 100,
"optimizer": "adam",
"GPU": 1,
"seed": 710,
},

64
qlib/contrib/model/pytorch_sfm.py
View File

@@ -21,11 +21,12 @@ from ...model.base import Model
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
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__()
self.input_dim = d_feat
self.input_dim = d_feat
self.output_dim = output_dim
self.freq_dim = freq_dim
self.hidden_dim = hidden_size
@@ -65,13 +66,13 @@ class SFM_Model(nn.Module):
self.states = []
def forward(self, input):
input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
input = input.permute(0, 2, 1) # [N, T, F]
input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
input = input.permute(0, 2, 1) # [N, T, F]
time_step = input.shape[1]
for ts in range(time_step):
x = input[:, ts,:]
if(len(self.states)==0): #hasn't initialized yet
x = input[:, ts, :]
if len(self.states) == 0: # hasn't initialized yet
self.init_states(x)
self.get_constants(x)
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_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))
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):
constants = []
constants.append([torch.tensor(1.).to(self.device) for _ in range(6)])
constants.append([torch.tensor(1.).to(self.device) for _ in range(7)])
array = np.array([float(ii)/self.freq_dim for ii in range(self.freq_dim)])
constants.append([torch.tensor(1.0).to(self.device) for _ in range(6)])
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)])
constants.append(torch.tensor(array).to(self.device))
self.states[5:] = constants
class SFM(Model):
"""SFM Model
@@ -185,7 +188,7 @@ class SFM(Model):
d_feat=6,
hidden_size=64,
output_dim=1,
freq_dim = 10,
freq_dim=10,
dropout_W=0.0,
dropout_U=0.0,
n_epochs=200,
@@ -221,7 +224,7 @@ class SFM(Model):
self.lr_decay_steps = lr_decay_steps
self.optimizer = optimizer.lower()
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.seed = seed
@@ -272,13 +275,13 @@ class SFM(Model):
self.sfm_model = SFM_Model(
d_feat=self.d_feat,
output_dim = self.output_dim,
hidden_size = self.hidden_size,
freq_dim = self.freq_dim,
output_dim=self.output_dim,
hidden_size=self.hidden_size,
freq_dim=self.freq_dim,
dropout_W=self.dropout_W,
dropout_U = self.dropout_U,
device = self.device
)
dropout_U=self.dropout_U,
device=self.device,
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
elif optimizer.lower() == "gd":
@@ -303,14 +306,7 @@ class SFM(Model):
self._fitted = False
self.sfm_model.to(self.device)
def fit(
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
**kwargs
):
def fit(self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, **kwargs):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
@@ -399,12 +395,12 @@ class SFM(Model):
# update learning rate
self.scheduler.step(cur_loss_val)
if self.device != 'cpu':
if self.device != "cpu":
torch.cuda.empty_cache()
def get_loss(self, pred, target, loss_type):
if loss_type == "mse":
sqr_loss = (pred - target)**2
sqr_loss = (pred - target) ** 2
loss = sqr_loss.mean()
return loss
elif loss_type == "binary":
@@ -424,19 +420,19 @@ class SFM(Model):
sample_num = x_values.shape[0]
preds = []
for begin in range(sample_num)[::self.batch_size]:
if sample_num-begin<self.batch_size:
for begin in range(sample_num)[:: self.batch_size]:
if sample_num - begin < self.batch_size:
end = sample_num
else:
end = begin + self.batch_size
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)
with torch.no_grad():
if self.device != 'cpu':
if self.device != "cpu":
pred = self.sfm_model(x_batch).detach().cpu().numpy()
else:
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._fitted = True
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()