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FIX divide by sqrt(2) the median entry in SMOTENC #1014

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Jul 10, 2023
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FIX divide by sqrt(2) the median entry in SMOTENC #1014

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FIX divide by sqrt(2) the median entry in SMOTENC
glemaitre Jul 10, 2023
b1294e0
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glemaitre Jul 10, 2023
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10 changes: 5 additions & 5 deletions doc/over_sampling.rst
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Expand Up @@ -203,11 +203,11 @@ or relying on `dtype` inference if the columns are using the
>>> print(sorted(Counter(y_resampled).items()))
[(0, 30), (1, 30)]
>>> print(X_resampled[-5:])
[['A' 0.5246469549655818 2]
['B' -0.3657680728116921 2]
['B' 0.9344237230779993 2]
['B' 0.3710891618824609 2]
['B' 0.3327240726719727 2]]
[['A' 0.52... 2]
['B' -0.36... 2]
['B' 0.93... 2]
['B' 0.37... 2]
['B' 0.33... 2]]

Therefore, it can be seen that the samples generated in the first and last
columns are belonging to the same categories originally presented without any
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8 changes: 8 additions & 0 deletions doc/whats_new/v0.11.rst
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Expand Up @@ -6,6 +6,14 @@ Version 0.11.1
Changelog
---------

Bug fixes
.........

- Fix a bug in :class:`~imblearn.over_sampling.SMOTENC` where the entries of the
one-hot encoding should be divided by `sqrt(2)` and not `2`, taking into account that
they are plugged into an Euclidean distance computation.
:pr:`1014` by :user:`Guillaume Lemaitre <glemaitre>`.


Version 0.11.0
==============
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9 changes: 7 additions & 2 deletions imblearn/over_sampling/_smote/base.py
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Expand Up @@ -671,13 +671,18 @@ def _fit_resample(self, X, y):

# In the edge case where the median of the std is equal to 0, the 1s
# entries will be also nullified. In this case, we store the original
# categorical encoding which will be later used for inversing the OHE
# categorical encoding which will be later used for inverting the OHE
if math.isclose(self.median_std_, 0):
self._X_categorical_minority_encoded = _safe_indexing(
X_ohe.toarray(), np.flatnonzero(y == class_minority)
)

X_ohe.data = np.ones_like(X_ohe.data, dtype=X_ohe.dtype) * self.median_std_ / 2
# With one-hot encoding, the median will be repeated twice. We need to divide
# by sqrt(2) such that we only have one median value contributing to the
# Euclidean distance
X_ohe.data = (
np.ones_like(X_ohe.data, dtype=X_ohe.dtype) * self.median_std_ / np.sqrt(2)
)
X_encoded = sparse.hstack((X_continuous, X_ohe), format="csr")

X_resampled, y_resampled = super()._fit_resample(X_encoded, y)
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