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Merge branch 'main' into dnn_drop

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This commit is contained in:
bxdd
2020-11-26 23:04:34 -06:00
committed by GitHub
parent a144a9c3c6 c8355f9f18
commit c31bf61278
105 changed files with 6034 additions and 2725 deletions
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90
qlib/contrib/data/handler.py
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@@ -10,6 +10,28 @@ from inspect import getfullargspec
import copy
def check_transform_proc(proc_l, fit_start_time, fit_end_time):
new_l = []
for p in proc_l:
if not isinstance(p, Processor):
klass, pkwargs = get_cls_kwargs(p, processor_module)
args = getfullargspec(klass).args
if "fit_start_time" in args and "fit_end_time" in args:
assert (
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
new_l.append({"class": klass.__name__, "kwargs": pkwargs})
else:
new_l.append(p)
return new_l
class ALPHA360_Denoise(DataHandlerLP):
def __init__(self, instruments="csi500", start_time=None, end_time=None, fit_start_time=None, fit_end_time=None):
data_loader = {
@@ -83,28 +105,42 @@ class ALPHA360_Denoise(DataHandlerLP):
return fields, names
_DEFAULT_LEARN_PROCESSORS = [
{"class": "DropnaLabel"},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
]
_DEFAULT_INFER_PROCESSORS = [
{"class": "ProcessInf", "kwargs": {}},
{"class": "ZScoreNorm", "kwargs": {}},
{"class": "Fillna", "kwargs": {}},
]
class ALPHA360(DataHandlerLP):
def __init__(self, instruments="csi500", start_time=None, end_time=None, fit_start_time=None, fit_end_time=None):
def __init__(
self,
instruments="csi500",
start_time=None,
end_time=None,
infer_processors=_DEFAULT_INFER_PROCESSORS,
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
**kwargs,
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
data_loader = {
"class": "QlibDataLoader",
"kwargs": {
"config": {
"feature": self.get_feature_config(),
"label": self.get_label_config(),
"label": kwargs.get("label", self.get_label_config()),
},
},
}
learn_processors = [
{"class": "DropnaLabel", "kwargs": {"fields_group": "label"}},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
]
infer_processors = [
{"class": "ProcessInf", "kwargs": {}},
{"class": "ZscoreNorm", "kwargs": {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time}},
{"class": "Fillna", "kwargs": {}},
]
super().__init__(
instruments,
start_time,
@@ -168,39 +204,19 @@ class Alpha158(DataHandlerLP):
start_time=None,
end_time=None,
infer_processors=[],
learn_processors=["DropnaLabel", {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}}],
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
process_type=DataHandlerLP.PTYPE_A
**kwargs,
):
def check_transform_proc(proc_l):
new_l = []
for p in proc_l:
if not isinstance(p, Processor):
klass, pkwargs = get_cls_kwargs(p, processor_module)
args = getfullargspec(klass).args
if "fit_start_time" in args and "fit_end_time" in args:
assert (
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
new_l.append({"class": klass.__name__, "kwargs": pkwargs})
else:
new_l.append(p)
return new_l
infer_processors = check_transform_proc(infer_processors)
learn_processors = check_transform_proc(learn_processors)
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
data_loader = {
"class": "QlibDataLoader",
"kwargs": {
"config": {"feature": self.get_feature_config(), "label": self.get_label_config()},
"config": {"feature": self.get_feature_config(), "label": kwargs.get("label", self.get_label_config())},
},
}
super().__init__(