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[WIP] ENH: SMOTE for pure categorical data #565

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[WIP] ENH: SMOTE for pure categorical data #565

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d1ac5bd
Add SMOTE for pure categorical data
ThomasKluiters May 5, 2019
e17ba76
Fix PEP issues
ThomasKluiters May 5, 2019
5f1ca14
Fix failing tests
ThomasKluiters May 5, 2019
c2fc4da
Fix doctest error
ThomasKluiters May 5, 2019
9db1071
Change order of counter keys in docstring
ThomasKluiters May 5, 2019
c2fe8c2
Rephrase docstring
ThomasKluiters May 5, 2019
bc96223
Refactor SMOTEN and SMOTENC to be more unified
ThomasKluiters May 7, 2019
c7e7036
Add documentation for SMOTEN
ThomasKluiters May 7, 2019
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Rephrase docstring
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@ThomasKluiters
ThomasKluiters committed May 5, 2019
commit c2fe8c2a39620153cedd6ff2e5810bc1699b4a2f
13 changes: 6 additions & 7 deletions imblearn/over_sampling/_smote.py
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Expand Up @@ -1096,8 +1096,8 @@ def _generate_sample(self, X, nn_data, nn_num, row, col, step):
# sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
# random_state=_random_state_docstring)
class SMOTEN(SMOTE):
"""Synthetic Minority Over-sampling Technique for Nominal
(SMOTE-NC).
"""Synthetic Minority Over-sampling Technique for Nominal data
(SMOTE-N).

Unlike :class:`SMOTE`, SMOTE-N operates on datasets containing categorical
features.
Expand Down Expand Up @@ -1200,14 +1200,13 @@ class SMOTEN(SMOTE):
... n_features=5, n_clusters_per_class=1, n_samples=1000, random_state=10)
>>> print('Original dataset shape (%s, %s)' % X.shape)
Original dataset shape (1000, 5)
>>> print('Original dataset samples per class {}'.format(Counter(y)))
Original dataset samples per class Counter({1: 900, 0: 100})
>>> # simulate the 2 last columns to be categorical features
>>> print('Original dataset samples in class 0: {}'.format(sum(y == 0)))
Original dataset samples in class 0: 100
>>> X[:, ] = RandomState(10).randint(0, 4, size=(1000, 5))
>>> sm = SMOTEN(random_state=42)
>>> X_res, y_res = sm.fit_resample(X, y)
>>> print('Resampled dataset samples per class {}'.format(Counter(y_res)))
Resampled dataset samples per class Counter({1: 900, 0: 900})
>>> print('Resampled dataset samples in class 0: {}'.format(sum(y_res == 0)))
Resampled dataset samples in class 0: 900

"""

Expand Down