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Update GRU and LSTM model.

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lwwang1995
2020-11-26 13:35:07 +08:00
parent 45192413f9
commit 5102566aad
2 changed files with 9 additions and 26 deletions
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17
qlib/contrib/model/pytorch_gru.py
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@@ -28,14 +28,10 @@ class GRU(Model):
Parameters Parameters
---------- ----------
input_dim : int d_feat : int
input dimension input dimension for each time step
output_dim : int metric: str
output dimension the evaluate metric used in early stop
layers : tuple
layer sizes
lr : float
learning rate
optimizer : str optimizer : str
optimizer name optimizer name
GPU : str GPU : str
@@ -112,10 +108,6 @@ class GRU(Model):
) )
) )
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.gru_model = GRUModel( self.gru_model = GRUModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
) )
@@ -251,7 +243,6 @@ class GRU(Model):
# train # train
self.logger.info("training...") self.logger.info("training...")
self._fitted = True self._fitted = True
# return
for step in range(self.n_epochs): for step in range(self.n_epochs):
self.logger.info("Epoch%d:", step) self.logger.info("Epoch%d:", step)

18
qlib/contrib/model/pytorch_lstm.py
View File

@@ -28,20 +28,17 @@ class LSTM(Model):
Parameters Parameters
---------- ----------
input_dim : int d_feat : int
input dimension input dimension for each time step
output_dim : int metric: str
output dimension the evaluate metric used in early stop
layers : tuple
layer sizes
lr : float
learning rate
optimizer : str optimizer : str
optimizer name optimizer name
GPU : str GPU : str
the GPU ID(s) used for training the GPU ID(s) used for training
""" """
def __init__( def __init__(
self, self,
d_feat=6, d_feat=6,
@@ -112,10 +109,6 @@ class LSTM(Model):
) )
) )
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.lstm_model = LSTMModel( self.lstm_model = LSTMModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
) )
@@ -251,7 +244,6 @@ class LSTM(Model):
# train # train
self.logger.info("training...") self.logger.info("training...")
self._fitted = True self._fitted = True
# return
for step in range(self.n_epochs): for step in range(self.n_epochs):
self.logger.info("Epoch%d:", step) self.logger.info("Epoch%d:", step)