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Merge branch 'main' of github.com:microsoft/qlib into fshare

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D-X-Y
2021-03-11 12:54:04 +00:00
parent b99de068f8 e8beaa5257
commit e626264d5a
8 changed files with 41 additions and 13 deletions
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8
qlib/contrib/model/pytorch_alstm_ts.py
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@@ -213,8 +213,12 @@ class ALSTM(Model):
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
train_loader = DataLoader(
dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True
)
valid_loader = DataLoader(
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
)
save_path = get_or_create_path(save_path)

4
qlib/contrib/model/pytorch_gats_ts.py
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@@ -261,8 +261,8 @@ class GATs(Model):
sampler_train = DailyBatchSampler(dl_train)
sampler_valid = DailyBatchSampler(dl_valid)
train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs)
valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs)
train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs, drop_last=True)
valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs, drop_last=True)
save_path = get_or_create_path(save_path)

8
qlib/contrib/model/pytorch_gru_ts.py
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@@ -213,8 +213,12 @@ class GRU(Model):
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
train_loader = DataLoader(
dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True
)
valid_loader = DataLoader(
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
)
save_path = get_or_create_path(save_path)

8
qlib/contrib/model/pytorch_lstm_ts.py
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@@ -209,8 +209,12 @@ class LSTM(Model):
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
train_loader = DataLoader(
dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True
)
valid_loader = DataLoader(
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
)
save_path = get_or_create_path(save_path)

2
qlib/data/dataset/__init__.py
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@@ -413,7 +413,7 @@ class TSDataSampler:
# 1) for better performance, use the last nan line for padding the lost date
# 2) In case of precision problems. We use np.float64. # TODO: I'm not sure if whether np.float64 will result in
# precision problems. It will not cause any problems in my tests at least
indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(np.int)
indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(int)
data = self.data_arr[indices]
if isinstance(idx, mtit):

3
qlib/data/ops.py
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@@ -74,7 +74,6 @@ class NpElemOperator(ElemOperator):
"""
def __init__(self, feature, func):
self.feature = feature
self.func = func
super(NpElemOperator, self).__init__(feature)
@@ -289,8 +288,6 @@ class NpPairOperator(PairOperator):
"""
def __init__(self, feature_left, feature_right, func):
self.feature_left = feature_left
self.feature_right = feature_right
self.func = func
super(NpPairOperator, self).__init__(feature_left, feature_right)

2
qlib/utils/__init__.py
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@@ -64,7 +64,7 @@ def np_ffill(arr: np.array):
arr : np.array
Input numpy 1D array
"""
mask = np.isnan(arr.astype(np.float)) # np.isnan only works on np.float
mask = np.isnan(arr.astype(float)) # np.isnan only works on np.float
# get fill index
idx = np.where(~mask, np.arange(mask.shape[0]), 0)
np.maximum.accumulate(idx, out=idx)