liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-11 06:46:56 +08:00
Code Activity

format

Browse Source
This commit is contained in:
Yuchen Fang
2021-01-28 00:41:02 +08:00
parent 98086e4fdc
commit a03b08bb4c
21 changed files with 154 additions and 563 deletions
Download Patch File Download Diff File Expand all files Collapse all files

27
examples/trade/util.py
View File

@@ -16,9 +16,7 @@ def nan_weighted_avg(vals, weights, axis=None):
:param axis: On which axis to calculate the weighted avrage. (Default value = None)
"""
assert vals.shape == weights.shape, AssertionError(
f"{vals.shape} & {weights.shape}"
)
assert vals.shape == weights.shape, AssertionError(f"{vals.shape} & {weights.shape}")
vals = vals.copy()
weights = weights.copy()
res = (vals * weights).sum(axis=axis) / weights.sum(axis=axis)
@@ -53,11 +51,7 @@ def merge_dicts(d1, d2):
def deep_update(
original,
new_dict,
new_keys_allowed=False,
whitelist=None,
override_all_if_type_changes=None,
original, new_dict, new_keys_allowed=False, whitelist=None, override_all_if_type_changes=None,
):
"""Updates original dict with values from new_dict recursively.
If new key is introduced in new_dict, then if new_keys_allowed is not
@@ -140,18 +134,9 @@ def generate_seq(seqlen, list):
maxlen = np.max(seqlen)
for i in seqlen:
if isinstance(list, torch.Tensor):
res.append(
torch.cat(
(list[index : index + i], torch.zeros_like(list[: maxlen - i])),
dim=0,
)
)
res.append(torch.cat((list[index : index + i], torch.zeros_like(list[: maxlen - i])), dim=0,))
else:
res.append(
np.concatenate(
(list[index : index + i], np.zeros_like(list[: maxlen - i])), axis=0
)
)
res.append(np.concatenate((list[index : index + i], np.zeros_like(list[: maxlen - i])), axis=0))
index += i
if isinstance(list, torch.Tensor):
res = torch.stack(res, dim=0)
@@ -298,9 +283,7 @@ def to_torch(
return x
def to_torch_as(
x: Union[torch.Tensor, dict, Batch, np.ndarray], y: torch.Tensor
) -> Union[dict, Batch, torch.Tensor]:
def to_torch_as(x: Union[torch.Tensor, dict, Batch, np.ndarray], y: torch.Tensor) -> Union[dict, Batch, torch.Tensor]:
"""
:param x: Union[torch.Tensor: