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logger & doc
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@@ -200,7 +200,7 @@ class DatasetH(Dataset):
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The data to fetch: DK_*
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Default is DK_I, which indicate fetching data for **inference**.
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kwargs :
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kwargs :
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The parameters that kwargs may contain:
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flt_col : str
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It only exists in TSDatasetH, can be used to add a column of data(True or False) to filter data.
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@@ -250,7 +250,9 @@ class TSDataSampler:
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"""
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def __init__(self, data: pd.DataFrame, start, end, step_len: int, fillna_type: str = "none", dtype=None, flt_data=None):
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def __init__(
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self, data: pd.DataFrame, start, end, step_len: int, fillna_type: str = "none", dtype=None, flt_data=None
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):
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"""
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Build a dataset which looks like torch.data.utils.Dataset.
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@@ -518,17 +520,17 @@ class TSDatasetH(DatasetH):
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"""
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dtype = kwargs.pop("dtype", None)
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start, end = slc.start, slc.stop
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flt_col = kwargs.pop('flt_col', None)
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flt_col = kwargs.pop("flt_col", None)
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# TSDatasetH will retrieve more data for complete
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data = self._prepare_raw_seg(slc, **kwargs)
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flt_kwargs = deepcopy(kwargs)
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if flt_col is not None:
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flt_kwargs['col_set'] = flt_col
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flt_kwargs["col_set"] = flt_col
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flt_data = self._prepare_raw_seg(slc, **flt_kwargs)
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assert len(flt_data.columns) == 1
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else:
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flt_data = None
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tsds = TSDataSampler(data=data, start=start, end=end, step_len=self.step_len, dtype=dtype, flt_data=flt_data)
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return tsds
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return tsds
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