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bxdd
2020-11-27 13:09:40 +08:00
parent 8b275a6006
commit ae757a4b51
4 changed files with 8 additions and 5 deletions
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2
qlib/contrib/data/handler.py
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@@ -226,7 +226,7 @@ class Alpha158(DataHandlerLP):
data_loader=data_loader, data_loader=data_loader,
infer_processors=infer_processors, infer_processors=infer_processors,
learn_processors=learn_processors, learn_processors=learn_processors,
process_type=process_type process_type=process_type,
) )
def get_feature_config(self): def get_feature_config(self):

1
qlib/contrib/model/pytorch_alstm.py
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@@ -146,7 +146,6 @@ class ALSTM(Model):
raise ValueError("unknown metric `%s`" % self.metric) raise ValueError("unknown metric `%s`" % self.metric)
def train_epoch(self, x_train, y_train): def train_epoch(self, x_train, y_train):
x_train_values = x_train.values x_train_values = x_train.values

3
qlib/contrib/model/pytorch_nn.py
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@@ -22,6 +22,7 @@ from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, creat
from ...log import get_module_logger, TimeInspector from ...log import get_module_logger, TimeInspector
from ...workflow import R from ...workflow import R
class DNNModelPytorch(Model): class DNNModelPytorch(Model):
"""DNN Model """DNN Model
@@ -349,7 +350,7 @@ class Net(nn.Module):
def _weight_init(self): def _weight_init(self):
for m in self.modules(): for m in self.modules():
if isinstance(m, nn.Linear): if isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, a=0.1, mode='fan_in', nonlinearity='leaky_relu') nn.init.kaiming_normal_(m.weight, a=0.1, mode="fan_in", nonlinearity="leaky_relu")
def forward(self, x): def forward(self, x):
cur_output = x cur_output = x

7
qlib/data/dataset/processor.py
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@@ -100,7 +100,8 @@ class DropCol(Processor):
else: else:
mask = df.columns.isin(self.col_list) mask = df.columns.isin(self.col_list)
return df.loc[:, ~mask] return df.loc[:, ~mask]
class TanhProcess(Processor): class TanhProcess(Processor):
""" Use tanh to process noise data""" """ Use tanh to process noise data"""
@@ -133,6 +134,7 @@ class ProcessInf(Processor):
return replace_inf(df) return replace_inf(df)
class Fillna(Processor): class Fillna(Processor):
"""Process NaN""" """Process NaN"""
@@ -270,6 +272,7 @@ class CSRankNorm(Processor):
df[cols] = t df[cols] = t
return df return df
class CSZFillna(Processor): class CSZFillna(Processor):
"""Cross Sectional Fill Nan""" """Cross Sectional Fill Nan"""
@@ -279,4 +282,4 @@ class CSZFillna(Processor):
def __call__(self, df): def __call__(self, df):
cols = get_group_columns(df, self.fields_group) cols = get_group_columns(df, self.fields_group)
df[cols] = df[cols].groupby("datetime").apply(lambda x: x.fillna(x.mean())) df[cols] = df[cols].groupby("datetime").apply(lambda x: x.fillna(x.mean()))
return df return df