Closed solegalli closed 1 year ago
Merging #676 (a9cefc1) into main (e73772d) will increase coverage by
0.07%
. The diff coverage is99.28%
.
@@ Coverage Diff @@
## main #676 +/- ##
==========================================
+ Coverage 97.91% 97.99% +0.07%
==========================================
Files 100 100
Lines 3748 3841 +93
Branches 726 754 +28
==========================================
+ Hits 3670 3764 +94
+ Misses 29 28 -1
Partials 49 49
Impacted Files | Coverage Δ | |
---|---|---|
feature_engine/datetime/datetime_subtraction.py | 94.66% <ø> (ø) |
|
feature_engine/tags.py | 100.00% <ø> (ø) |
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feature_engine/selection/drop_psi_features.py | 99.40% <98.55%> (+1.08%) |
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feature_engine/creation/relative_features.py | 100.00% <100.00%> (ø) |
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feature_engine/encoding/rare_label.py | 100.00% <100.00%> (ø) |
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feature_engine/encoding/woe.py | 100.00% <100.00%> (ø) |
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feature_engine/imputation/drop_missing_data.py | 100.00% <100.00%> (ø) |
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feature_engine/selection/shuffle_features.py | 100.00% <100.00%> (ø) |
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feature_engine/transformation/reciprocal.py | 100.00% <100.00%> (ø) |
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..._engine/variable_handling/_variable_type_checks.py | 92.30% <100.00%> (+1.00%) |
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Hey @glevv
I am expanding the functionality of the WoE encoder to do 2 things:
Regarding 1: the error was there already. The improvement consists in letting users know which variables are the problematic
Regarding 2: I think this defeats the point of the calculation, but I guess we leave this to the user. The implementation is different from category encoders because 1) category encoders adds the regularization to all categories, regardless of whether they have zero or not. Here we modify only those with 0. And 2) we replace the 0 with the fill value. Category encoders adds the regularization to both denominator and numerator)
I would appreciate if you had 2 minutes to let me know what you think about the implementation, and if you can go over the code, even better.
Thanks a lot!
closes #672 closes #430 closes #56
and also closes #673