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FIX handle heterogeneous data type in categorical feature in SMOTENC #1002

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Jul 7, 2023
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FIX handle heterogeneous data type in categorical feature in SMOTENC #1002

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FIX handle heterogeneous data type in categorical feature in SMOTENC
glemaitre Jul 7, 2023
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glemaitre Jul 7, 2023
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21 changes: 14 additions & 7 deletions doc/whats_new/v0.11.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,23 +6,30 @@ Version 0.11.0 (Under development)
Changelog
---------

Bug fixes
.........

- :class:`~imblearn.over_sampling.SMOTENC` now handles mix types of data type such as
`bool` and `pd.category` by delegating the conversion to scikit-learn encoder.
:pr:`1002` by :user:`Guillaume Lemaitre <glemaitre>`.

Compatibility
.............

- Maintenance release for being compatible with scikit-learn >= 1.3.0.
:pr:`999` by :user:`Guillaume Lemaitre <glemaitre>`.

Deprecation
...........

- The fitted attribute `ohe_` in :class:`~imblearn.over_sampling.SMOTENC` is deprecated
and will be removed in version 0.13. Use `categorical_encoder_` instead.
:pr:`1000` by :user:`Guillaume Lemaitre <glemaitre>`.

Enhancements
............

- :class:`~imblearn.over_sampling.SMOTENC` now accepts a parameter `categorical_encoder`
allowing to specify a :class:`~sklearn.preprocessing.OneHotEncoder` with custom
parameters.
:pr:`1000` by :user:`Guillaume Lemaitre <glemaitre>`.

Deprecation
...........

- The fitted attribute `ohe_` in :class:`~imblearn.over_sampling.SMOTENC` is deprecated
and will be removed in version 0.13. Use `categorical_encoder_` instead.
:pr:`1000` by :user:`Guillaume Lemaitre <glemaitre>`.
13 changes: 7 additions & 6 deletions imblearn/over_sampling/_smote/base.py
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Expand Up @@ -20,6 +20,7 @@
csc_mean_variance_axis0,
csr_mean_variance_axis0,
)
from sklearn.utils.validation import _num_features

from ...metrics.pairwise import ValueDifferenceMetric
from ...utils import Substitution, check_neighbors_object, check_target_type
Expand Down Expand Up @@ -557,9 +558,9 @@ def _check_X_y(self, X, y):
features.
"""
y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
X, y = self._validate_data(
X, y, reset=True, dtype=None, accept_sparse=["csr", "csc"]
)
if not (hasattr(X, "__array__") or sparse.issparse(X)):
X = check_array(X, dtype=object)
self._check_n_features(X, reset=True)
return X, y, binarize_y

def _validate_estimator(self):
Expand Down Expand Up @@ -596,14 +597,14 @@ def _fit_resample(self, X, y):
FutureWarning,
)

self.n_features_ = X.shape[1]
self.n_features_ = _num_features(X)
self._validate_estimator()

# compute the median of the standard deviation of the minority class
target_stats = Counter(y)
class_minority = min(target_stats, key=target_stats.get)

X_continuous = X[:, self.continuous_features_]
X_continuous = _safe_indexing(X, self.continuous_features_, axis=1)
X_continuous = check_array(X_continuous, accept_sparse=["csr", "csc"])
X_minority = _safe_indexing(X_continuous, np.flatnonzero(y == class_minority))

Expand All @@ -616,7 +617,7 @@ def _fit_resample(self, X, y):
var = X_minority.var(axis=0)
self.median_std_ = np.median(np.sqrt(var))

X_categorical = X[:, self.categorical_features_]
X_categorical = _safe_indexing(X, self.categorical_features_, axis=1)
if X_continuous.dtype.name != "object":
dtype_ohe = X_continuous.dtype
else:
Expand Down
34 changes: 34 additions & 0 deletions imblearn/over_sampling/_smote/tests/test_smote_nc.py
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Expand Up @@ -290,3 +290,37 @@ def test_smotenc_param_validation():
name = smote.__class__.__name__
_set_checking_parameters(smote)
check_param_validation(name, smote)


def test_smotenc_bool_categorical():
"""Check that we don't try to early convert the full input data to numeric when
handling a pandas dataframe.

Non-regression test for:
https://github.com/scikit-learn-contrib/imbalanced-learn/issues/974
"""
pd = pytest.importorskip("pandas")

X = pd.DataFrame(
{
"c": pd.Categorical([x for x in "abbacaba" * 3]),
"f": [0.3, 0.5, 0.1, 0.2] * 6,
"b": [False, False, True] * 8,
}
)
y = pd.DataFrame({"out": [1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0] * 2})
smote = SMOTENC(categorical_features=[0])

X_res, y_res = smote.fit_resample(X, y)
pd.testing.assert_series_equal(X_res.dtypes, X.dtypes)
assert len(X_res) == len(y_res)

smote.set_params(categorical_features=[0, 2])
X_res, y_res = smote.fit_resample(X, y)
pd.testing.assert_series_equal(X_res.dtypes, X.dtypes)
assert len(X_res) == len(y_res)

X = X.astype({"b": "category"})
X_res, y_res = smote.fit_resample(X, y)
pd.testing.assert_series_equal(X_res.dtypes, X.dtypes)
assert len(X_res) == len(y_res)