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Vectorize apply_replacement #207

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Vectorize apply_replacement #207

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4db1868
vectorize colum categ
matheusfacure Aug 10, 2022
836d8ed
fix lint
matheusfacure Aug 10, 2022
8a548c8
fix type issue
matheusfacure Aug 10, 2022
632796e
use map
matheusfacure Aug 10, 2022
e27869b
output to float
matheusfacure Aug 10, 2022
3893f2f
return float
matheusfacure Aug 10, 2022
8c5c9b0
use dict.get and na_action='ignore'
jmoralez Aug 10, 2022
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6 changes: 3 additions & 3 deletions src/fklearn/training/transformation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,9 +354,9 @@ def apply_replacements(df: pd.DataFrame,
Default value to replace when original value is not present in the `vec` dict for the feature

"""
column_categorizer = lambda col: df[col].apply(lambda x: (np.nan
if isinstance(x, float) and np.isnan(x)
else vec[col].get(x, replace_unseen)))
def column_categorizer(col: str) -> pd.Series:
return df[col].map(lambda x: vec[col].get(x, replace_unseen), na_action='ignore')
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is this faster than .apply? I don't know how map works under the hood, but if it implements a for loop in the backend for lambda function, than its just as bad as apply no?

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You're right, I wasn't benchmarking against the original implementation, this takes about the same time (4.5s original, 3.8s this one). Do you have an example where the original is very slow? That may be a better case to benchmark.


categ_columns = {col: column_categorizer(col) for col in columns}
return df.assign(**categ_columns)

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