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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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8
qlib/contrib/model/pytorch_localformer.py
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@@ -46,7 +46,6 @@ class LocalformerModel(Model):
seed=None,
**kwargs
):
# set hyper-parameters.
self.d_model = d_model
self.dropout = dropout
@@ -96,7 +95,6 @@ class LocalformerModel(Model):
raise ValueError("unknown loss `%s`" % self.loss)
def metric_fn(self, pred, label):
mask = torch.isfinite(label)
if self.metric in ("", "loss"):
@@ -105,7 +103,6 @@ class LocalformerModel(Model):
raise ValueError("unknown metric `%s`" % self.metric)
def train_epoch(self, x_train, y_train):
x_train_values = x_train.values
y_train_values = np.squeeze(y_train.values)
@@ -115,7 +112,6 @@ class LocalformerModel(Model):
np.random.shuffle(indices)
for i in range(len(indices))[:: self.batch_size]:
if len(indices) - i < self.batch_size:
break
@@ -131,7 +127,6 @@ class LocalformerModel(Model):
self.train_optimizer.step()
def test_epoch(self, data_x, data_y):
# prepare training data
x_values = data_x.values
y_values = np.squeeze(data_y.values)
@@ -144,7 +139,6 @@ class LocalformerModel(Model):
indices = np.arange(len(x_values))
for i in range(len(indices))[:: self.batch_size]:
if len(indices) - i < self.batch_size:
break
@@ -167,7 +161,6 @@ class LocalformerModel(Model):
evals_result=dict(),
save_path=None,
):
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"],
col_set=["feature", "label"],
@@ -232,7 +225,6 @@ class LocalformerModel(Model):
preds = []
for begin in range(sample_num)[:: self.batch_size]:
if sample_num - begin < self.batch_size:
end = sample_num
else: