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Fix #13194: Ensure monotonic bin edges for KBinsDiscretizer strategy quantile #13195

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Fix #13194: Ensure monotonic bin edges for KBinsDiscretizer strategy quantile #13195

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4 changes: 4 additions & 0 deletions sklearn/preprocessing/_discretization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,6 +161,10 @@ def fit(self, X, y=None):
elif self.strategy == 'quantile':
quantiles = np.linspace(0, 100, n_bins[jj] + 1)
bin_edges[jj] = np.asarray(np.percentile(column, quantiles))
# Edge cases can lead to numerically unstable values in case of
# identical percentiles, but we need to make them monotonic
# https://github.com/numpy/numpy/issues/10373
np.maximum.accumulate(bin_edges[jj], out=bin_edges[jj])

elif self.strategy == 'kmeans':
from ..cluster import KMeans # fixes import loops
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11 changes: 10 additions & 1 deletion sklearn/preprocessing/tests/test_discretization.py
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Expand Up @@ -145,14 +145,23 @@ def test_numeric_stability():
assert_array_equal(Xt_expected, Xt)


def test_percentile_numeric_stability():
X = np.array([0.05, 0.05, 0.95]).reshape(-1, 1)
Xt_expected = np.array([5, 5, 9]).reshape(-1, 1)

Xt = KBinsDiscretizer(n_bins=10,
encode='ordinal',
strategy='quantile').fit_transform(X)
assert_array_equal(Xt_expected, Xt)


def test_invalid_encode_option():
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode='invalid-encode')
assert_raise_message(ValueError, "Valid options for 'encode' are "
"('onehot', 'onehot-dense', 'ordinal'). "
"Got encode='invalid-encode' instead.",
est.fit, X)


def test_encode_options():
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3],
encode='ordinal').fit(X)
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