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Code Activity

Adjust rolling api (#1594)

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* Intermediate version

* Fix yaml template & Successfully run rolling

* Be compatible with benchmark

* Get same results with previous linear model

* Black formatting

* Update black

* Update the placeholder mechanism

* Update CI

* Update CI

* Upgrade Black

* Fix CI and simplify code

* Fix CI

* Move the data processing caching mechanism into utils.

* Adjusting DDG-DA

* Organize import
This commit is contained in:
you-n-g
2023-07-14 12:16:12 +08:00
committed by GitHub
parent 8d3adf34ac
commit be4646b4b7
148 changed files with 1035 additions and 1028 deletions
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7
examples/benchmarks/TRA/src/model.py
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@@ -45,7 +45,6 @@ class TRAModel(Model):
avg_params=True,
**kwargs,
):
np.random.seed(seed)
torch.manual_seed(seed)
@@ -93,7 +92,6 @@ class TRAModel(Model):
self.global_step = -1
def train_epoch(self, data_set):
self.model.train()
self.tra.train()
@@ -146,7 +144,6 @@ class TRAModel(Model):
return total_loss
def test_epoch(self, data_set, return_pred=False):
self.model.eval()
self.tra.eval()
data_set.eval()
@@ -204,7 +201,6 @@ class TRAModel(Model):
return metrics, preds
def fit(self, dataset, evals_result=dict()):
train_set, valid_set, test_set = dataset.prepare(["train", "valid", "test"])
best_score = -1
@@ -380,7 +376,6 @@ class LSTM(nn.Module):
self.output_size = hidden_size
def forward(self, x):
x = self.input_drop(x)
if self.training and self.noise_level > 0:
@@ -464,7 +459,6 @@ class Transformer(nn.Module):
self.output_size = hidden_size
def forward(self, x):
x = self.input_drop(x)
if self.training and self.noise_level > 0:
@@ -514,7 +508,6 @@ class TRA(nn.Module):
self.predictors = nn.Linear(input_size, num_states)
def forward(self, hidden, hist_loss):
preds = self.predictors(hidden)
if self.num_states == 1: