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Jactus
2020-11-19 16:50:16 +08:00
parent dfc9351096
commit a8ad2120c9
15 changed files with 62 additions and 1394 deletions
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29
qlib/contrib/model/pytorch_gats.py
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@@ -121,7 +121,11 @@ class GAT(Model):
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.GAT_model = GATModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, base_model=self.base_model
d_feat=self.d_feat,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout,
base_model=self.base_model,
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.GAT_model.parameters(), lr=self.lr)
@@ -321,11 +325,10 @@ class GAT(Model):
class GATModel(nn.Module):
def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_model='GRU'):
def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_model="GRU"):
super().__init__()
if base_model == 'GRU':
if base_model == "GRU":
self.rnn = nn.GRU(
input_size=d_feat,
hidden_size=hidden_size,
@@ -333,7 +336,7 @@ class GATModel(nn.Module):
batch_first=True,
dropout=dropout,
)
elif base_model == 'LSTM':
elif base_model == "LSTM":
self.rnn = nn.LSTM(
input_size=d_feat,
hidden_size=hidden_size,
@@ -342,7 +345,7 @@ class GATModel(nn.Module):
dropout=dropout,
)
else:
raise ValueError('unknown base model name `%s`'%base_model)
raise ValueError("unknown base model name `%s`" % base_model)
self.hidden_size = hidden_size
self.bn1 = nn.BatchNorm1d(num_features=hidden_size, track_running_stats=False)
@@ -354,19 +357,19 @@ class GATModel(nn.Module):
self.d_feat = d_feat
def cal_convariance(self, x, y): # the 2nd dimension of x and y are the same
e_x = torch.mean(x, dim = 1).reshape(-1, 1)
e_y = torch.mean(y, dim = 1).reshape(-1, 1)
def cal_convariance(self, x, y): # the 2nd dimension of x and y are the same
e_x = torch.mean(x, dim=1).reshape(-1, 1)
e_y = torch.mean(y, dim=1).reshape(-1, 1)
e_x_e_y = e_x.mm(torch.t(e_y))
x_extend = x.reshape(x.shape[0], 1, x.shape[1]).repeat(1, y.shape[0], 1)
y_extend = y.reshape(1, y.shape[0], y.shape[1]).repeat(x.shape[0], 1, 1)
e_xy = torch.mean(x_extend*y_extend, dim = 2)
e_xy = torch.mean(x_extend * y_extend, dim=2)
return e_xy - e_x_e_y
def forward(self, x):
# x: [N, F*T]
x = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
x = x.permute(0, 2, 1) # [N, T, F]
x = x.reshape(len(x), self.d_feat, -1) # [N, F, T]
x = x.permute(0, 2, 1) # [N, T, F]
out, _ = self.rnn(x)
hidden = out[:, -1, :]
hidden = self.bn1(hidden)
@@ -380,4 +383,4 @@ class GATModel(nn.Module):
output = self.fc(output)
output = self.bn2(output)
output = self.leaky_relu(output)
return self.fc_out(output).squeeze()
return self.fc_out(output).squeeze()