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revise settings

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This commit is contained in:
Hong Zhang
2020-11-27 00:19:23 +08:00
parent 293c405593
commit 5796363ecf
5 changed files with 16 additions and 16 deletions
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4
examples/workflow_by_code_gats.py
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@@ -61,9 +61,9 @@ if __name__ == "__main__":
"d_feat": 6, "d_feat": 6,
"hidden_size": 64, "hidden_size": 64,
"num_layers": 2, "num_layers": 2,
"dropout": 0.0, "dropout": 0.7,
"n_epochs": 200, "n_epochs": 200,
"lr": 1e-3, "lr": 1e-4,
"early_stop": 20, "early_stop": 20,
"metric": "loss", "metric": "loss",
"loss": "mse", "loss": "mse",

6
examples/workflow_by_code_hats.py
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@@ -58,11 +58,11 @@ if __name__ == "__main__":
"d_feat": 6, "d_feat": 6,
"hidden_size": 64, "hidden_size": 64,
"num_layers": 2, "num_layers": 2,
"dropout": 0.6, "dropout": 0.7,
"n_epochs": 200, "n_epochs": 200,
"lr": 1e-3, "lr": 1e-4,
"early_stop": 20, "early_stop": 20,
"metric": "IC", "metric": "loss",
"loss": "mse", "loss": "mse",
"base_model": "LSTM", "base_model": "LSTM",
"seed": 0, "seed": 0,

10
qlib/contrib/model/pytorch_gats.py
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@@ -43,13 +43,13 @@ class GAT(Model):
d_feat=6, d_feat=6,
hidden_size=64, hidden_size=64,
num_layers=2, num_layers=2,
dropout=0.0, dropout=0.7,
n_epochs=200, n_epochs=200,
lr=0.001, lr=0.0001,
metric="IC", metric="loss",
early_stop=20, early_stop=20,
loss="mse", loss="mse",
base_model="GRU", base_model="LSTM",
with_pretrain=True, with_pretrain=True,
optimizer="adam", optimizer="adam",
GPU="0", GPU="0",
@@ -174,7 +174,7 @@ class GAT(Model):
def train_epoch(self, x_train, y_train): def train_epoch(self, x_train, y_train):
x_train_values = x_train.values x_train_values = x_train.values
y_train_values = np.squeeze(y_train.values) * 100 y_train_values = np.squeeze(y_train.values)
self.GAT_model.train() self.GAT_model.train()
# organize the train data into daily inter as daily batches # organize the train data into daily inter as daily batches

8
qlib/contrib/model/pytorch_hats.py
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@@ -52,11 +52,11 @@ class HATS(Model):
num_layers=2, num_layers=2,
dropout=0.5, dropout=0.5,
n_epochs=200, n_epochs=200,
lr=0.01, lr=0.0001,
metric="IC", metric="loss",
early_stop=20, early_stop=20,
loss="mse", loss="mse",
base_model="GRU", base_model="LSTM",
with_pretrain=True, with_pretrain=True,
optimizer="adam", optimizer="adam",
GPU="0", GPU="0",
@@ -180,7 +180,7 @@ class HATS(Model):
def train_epoch(self, x_train, y_train): def train_epoch(self, x_train, y_train):
x_train_values = x_train.values x_train_values = x_train.values
y_train_values = np.squeeze(y_train.values) * 100 y_train_values = np.squeeze(y_train.values)
self.HATS_model.train() self.HATS_model.train()

4
qlib/contrib/model/pytorch_lstm.py
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@@ -46,7 +46,7 @@ class LSTM(Model):
dropout=0.0, dropout=0.0,
n_epochs=200, n_epochs=200,
lr=0.001, lr=0.001,
metric="IC", metric="loss",
batch_size=2000, batch_size=2000,
early_stop=20, early_stop=20,
loss="mse", loss="mse",
@@ -154,7 +154,7 @@ class LSTM(Model):
def train_epoch(self, x_train, y_train): def train_epoch(self, x_train, y_train):
x_train_values = x_train.values x_train_values = x_train.values
y_train_values = np.squeeze(y_train.values) * 100 y_train_values = np.squeeze(y_train.values)
self.lstm_model.train() self.lstm_model.train()