ENH: Add OTOS oversampling algorithm

pyloras/__init__.py

@@ -1,3 +1,4 @@
 from ._loras import LORAS
 from ._prowras import ProWRAS
 from ._gamus import GAMUS
+from ._otos import OTOS

pyloras/_otos.py

@@ -0,0 +1,35 @@
+import ot
+from imblearn.over_sampling.base import BaseOverSampler
+from imblearn.utils import Substitution
+from imblearn.utils._docstring import (
+ _random_state_docstring,
+ _n_jobs_docstring,
+)
+import numpy as np
+
+from ._common import check_random_state, safe_random_state
+
+
+@Substitution(
+ sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
+ random_state=_random_state_docstring,
+ n_jobs=_n_jobs_docstring
+)
+class OTOS(BaseOverSampler):
+ def __init__(
+ self,
+ *,
+ sampling_strategy="auto",
+ svm_regularization=1.0,
+ ot_regularization=1.0,
+ tradeoff=1.0,
+ random_state=None,
+ ):
+ super().__init__(sampling_strategy=sampling_strategy)
+ self.svm_regularization = svm_regularization
+ self.ot_regularization = ot_regularization
+ self.tradeoff = tradeoff
+ self.random_state = random_state
+
+ def fit_resample(self, X, y):
+ return X, y

tests/test_otos.py

@@ -0,0 +1,23 @@
+import numpy as np
+import pytest
+from sklearn.datasets import make_classification
+
+from pyloras import OTOS
+
+
+@pytest.fixture
+def data():
+ return make_classification(n_samples=150, n_features=4, n_informative=4,
+ n_redundant=0, n_repeated=0, n_classes=3,
+ n_clusters_per_class=2,
+ weights=[0.01, 0.05, 0.94],
+ class_sep=0.8, random_state=0)
+
+
+def test_otos(data):
+ X, y = data
+ rng = np.random.RandomState(12345)
+ otos = OTOS(random_state=rng)
+ X_res, y_res = otos.fit_resample(X, y)
+ _, y_counts = np.unique(y_res, return_counts=True)
+ np.testing.assert_allclose(y_counts[0], y_counts[1])