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🐛 fix bug with bound_method + ✨ new integrations #62
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Original file line number | Diff line number | Diff line change |
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@@ -256,7 +256,7 @@ def calculate( | |
self, | ||
data: Union[pd.Series, pd.DataFrame, List[Union[pd.Series, pd.DataFrame]]], | ||
return_df: Optional[bool] = False, | ||
window_idx: Optional[str] = "end", | ||
window_idx: Optional[str] = "begin", | ||
bound_method: Optional[str] = "inner", | ||
approve_sparsity: Optional[bool] = False, | ||
show_progress: Optional[bool] = False, | ||
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@@ -292,7 +292,7 @@ def calculate( | |
window_idx : str, optional | ||
The window's index position which will be used as index for the | ||
feature_window aggregation. Must be either of: `["begin", "middle", "end"]`. | ||
by default "end". All features in this collection will use the same | ||
by default "begin". All features in this collection will use the same | ||
window_idx. | ||
bound_method: str, optional | ||
The start-end bound methodology which is used to generate the slice ranges | ||
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@@ -367,7 +367,7 @@ def calculate( | |
# determing the bounds of the series dict items and slice on them | ||
start, end = _determine_bounds(bound_method, list(series_dict.values())) | ||
series_dict = { | ||
n: s[s.index.dtype.type(start) : s.index.dtype.type(end)] | ||
n: s.loc[s.index.dtype.type(start) : s.index.dtype.type(end)] # TODO: check memory efficiency of ths | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Check memory efficiency of this There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. |
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for n, s, in series_dict.items() | ||
} | ||
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TODO: maybe state here that the data was already segmented -> so here you can find an example on how to use
tsflex
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Hmm not very proud about how we did it (considering the long table as one large series and having a stride that is equal to your sample size)
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okay, will create an issue or extend an existing one with this topic