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FIX: get the right index when tie breaking in SMOTE NC #497

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Nov 6, 2018
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FIX: get the right index when tie breaking in SMOTE NC #497

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10 changes: 5 additions & 5 deletions doc/over_sampling.rst
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Expand Up @@ -188,11 +188,11 @@ features or a boolean mask marking these features::
>>> print(sorted(Counter(y_resampled).items()))
[(0, 30), (1, 30)]
>>> print(X_resampled[-5:])
[['B' 0.5246469549655818 0]
['A' -0.3657680728116921 0]
['B' 0.9344237230779993 0]
['A' 0.3710891618824609 0]
['A' 0.3327240726719727 0]]
[['A' 0.5246469549655818 2]
['B' -0.3657680728116921 2]
['A' 0.9344237230779993 2]
['B' 0.3710891618824609 2]
['B' 0.3327240726719727 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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6 changes: 5 additions & 1 deletion doc/whats_new/v0.4.rst
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Expand Up @@ -18,10 +18,14 @@ Bug fixes
target or multilabel targets. Imbalanced-learn does not support this case.
By :user:`Guillaume Lemaitre <glemaitre>` in :issue:`490`.

- Fix a bug in :class:`imblearn.over_sampling.SMOTENC` in which an sparse
- Fix a bug in :class:`imblearn.over_sampling.SMOTENC` in which a sparse
matrices were densify during ``inverse_transform``.
By :user:`Guillaume Lemaitre <glemaitre>` in :issue:`495`.

- Fix a bug in :class:`imblearn.over_sampling.SMOTE_NC` in which a the tie
breaking was wrongly sampling.
By :user:`Guillaume Lemaitre <glemaitre>` in :issue:`497`.

Version 0.4
===========

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4 changes: 3 additions & 1 deletion imblearn/over_sampling/_smote.py
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Expand Up @@ -1032,11 +1032,13 @@ def _generate_sample(self, X, nn_data, nn_num, row, col, step):

categories_size = ([self.continuous_features_.size] +
[cat.size for cat in self.ohe_.categories_])

for start_idx, end_idx in zip(np.cumsum(categories_size)[:-1],
np.cumsum(categories_size)[1:]):
col_max = all_neighbors[:, start_idx:end_idx].sum(axis=0)
# tie breaking argmax
col_sel = rng.choice(col_max == col_max.max())
col_sel = rng.choice(np.flatnonzero(
np.isclose(col_max, col_max.max())))
sample[start_idx:end_idx] = 0
sample[start_idx + col_sel] = 1

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