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synced 2026-06-06 05:51:17 +08:00
fix ubg
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@@ -71,7 +71,7 @@ class HighFreqNorm(Processor):
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).sort_index()
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return df_new_features
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def config(fit_start_time=None, fit_end_time=None, **kwargs):
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def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
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if fit_start_time:
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self.fit_start_time = fit_start_time
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if fit_end_time:
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@@ -31,7 +31,7 @@ class HighfreqWorkflow(object):
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SPEC_CONF = {"custom_ops": [DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut], "expression_cache": None}
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MARKET = "csi300"
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MARKET = "all"
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start_time = "2020-09-15 00:00:00"
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end_time = "2021-01-18 16:00:00"
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@@ -101,15 +101,16 @@ class RollingDataWorkflow(object):
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print(f"===========rolling{rolling_offset} start===========")
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if rolling_offset:
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dataset.init(
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dataset.config(
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handler_kwargs={
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"init_type": DataHandlerLP.IT_FIT_SEQ,
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"start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
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"end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
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"fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
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"fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
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"processor_kwargs":{
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"fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
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"fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
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},
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},
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segment_kwargs={
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segments={
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"train": (
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datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
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datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
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@@ -124,6 +125,9 @@ class RollingDataWorkflow(object):
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),
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},
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)
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dataset.setup_data(
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handler_kwargs={"init_type": DataHandlerLP.IT_FIT_SEQ,}
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)
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dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"])
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print(dtrain, dvalid, dtest)
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@@ -407,7 +407,7 @@ class DataHandlerLP(DataHandler):
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if self.drop_raw:
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del self._data
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def config(self, processors_kwargs:dict = None, **kwargs):
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def config(self, processor_kwargs:dict = None, **kwargs):
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"""
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configuration of data.
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# what data to be loaded from data source
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@@ -417,7 +417,7 @@ class DataHandlerLP(DataHandler):
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"""
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super().config(**kwargs)
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if processors_kwargs is not None:
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if processor_kwargs is not None:
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for processor in self.get_all_processors():
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processor.config(**processor_kwargs)
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@@ -72,7 +72,7 @@ class Processor(Serializable):
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"""
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return True
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def config(**kwargs):
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def config(self, **kwargs):
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super().config(kwargs.get("dump_all", None), kwargs.get("exclude", None))
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@@ -195,7 +195,7 @@ class MinMaxNorm(Processor):
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df.loc(axis=1)[self.cols] = normalize(df[self.cols].values)
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return df
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def config(fit_start_time=None, fit_end_time=None, **kwargs):
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def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
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if fit_start_time:
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self.fit_start_time = fit_start_time
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if fit_end_time:
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@@ -230,7 +230,7 @@ class ZScoreNorm(Processor):
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df.loc(axis=1)[self.cols] = normalize(df[self.cols].values)
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return df
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def config(fit_start_time=None, fit_end_time=None, **kwargs):
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def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
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if fit_start_time:
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self.fit_start_time = fit_start_time
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if fit_end_time:
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@@ -273,7 +273,7 @@ class RobustZScoreNorm(Processor):
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df.clip(-3, 3, inplace=True)
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return df
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def config(fit_start_time=None, fit_end_time=None, **kwargs):
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def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
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if fit_start_time:
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self.fit_start_time = fit_start_time
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if fit_end_time:
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