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Fill NaN of Empatica's SD second order feature (must be tested).

notes
Primoz 2022-09-19 07:34:02 +00:00
parent 52e11cdcab
commit a96ea508c6
1 changed files with 1 additions and 1 deletions
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2
src/features/cr_features_helper_methods.py
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@ -21,7 +21,7 @@ def extract_second_order_features(intraday_features, so_features_names, prefix="
so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).median().add_suffix("_SO_median")], axis=1) so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).median().add_suffix("_SO_median")], axis=1)
if "sd" in so_features_names: if "sd" in so_features_names:
so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().add_suffix("_SO_sd")], axis=1) so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().fillna(0).add_suffix("_SO_sd")], axis=1)
if "nlargest" in so_features_names: # largest 5 -- maybe there is a faster groupby solution? if "nlargest" in so_features_names: # largest 5 -- maybe there is a faster groupby solution?
for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]: for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]: