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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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5
qlib/contrib/data/dataset.py
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@@ -130,7 +130,6 @@ class MTSDatasetH(DatasetH):
input_size=None,
**kwargs,
):
assert num_states == 0 or horizon > 0, "please specify `horizon` to avoid data leakage"
assert memory_mode in ["sample", "daily"], "unsupported memory mode"
assert memory_mode == "sample" or batch_size < 0, "daily memory requires daily sampling (`batch_size < 0`)"
@@ -153,7 +152,6 @@ class MTSDatasetH(DatasetH):
super().__init__(handler, segments, **kwargs)
def setup_data(self, handler_kwargs: dict = None, **kwargs):
super().setup_data(**kwargs)
if handler_kwargs is not None:
@@ -288,7 +286,6 @@ class MTSDatasetH(DatasetH):
daily_count = [] # store number of samples for each day
for j in indices[i : i + batch_size]:
# normal sampling: self.batch_size > 0 => slices is a list => slices_subset is a slice
# daily sampling: self.batch_size < 0 => slices is a nested list => slices_subset is a list
slices_subset = slices[j]
@@ -297,7 +294,6 @@ class MTSDatasetH(DatasetH):
# each slices_subset contains a list of slices for multiple stocks
# NOTE: daily sampling is used in 1) eval mode, 2) train mode with self.batch_size < 0
if self.batch_size < 0:
# store daily index
idx = self._daily_index.index[j] # daily_index.index is the index of the original data
daily_index.append(idx)
@@ -320,7 +316,6 @@ class MTSDatasetH(DatasetH):
slices_subset = [slices_subset]
for slc in slices_subset:
# legacy support for Alpha360 data by `input_size`
if self.input_size:
data.append(self._data[slc.stop - 1].reshape(self.input_size, -1).T)