Sklearn: Handle the special case when all labels are the same

miplearn/classifiers/__init__.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from abc import ABC, abstractmethod
-from typing import Optional, Any
+from typing import Optional, Any, cast
 
 import numpy as np
 
@@ -139,6 +139,7 @@ class ScikitLearnClassifier(Classifier):
     def __init__(self, clf: Any) -> None:
         super().__init__()
         self.inner_clf = clf
+        self.constant: Optional[np.ndarray] = None
 
     def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
         super().fit(x_train, y_train)
@@ -147,11 +148,22 @@ class ScikitLearnClassifier(Classifier):
             f"Scikit-learn classifiers must have exactly two classes. "
             f"{n_classes} classes were provided instead."
         )
+
+        # When all samples belong to the same class, sklearn's predict_proba returns
+        # an array with a single column. The following check avoid this strange
+        # behavior.
+        mean = cast(np.ndarray, y_train.astype(float).mean(axis=0))
+        if mean.max() == 1.0:
+            self.constant = mean
+            return
+
         self.inner_clf.fit(x_train, y_train[:, 1])
 
     def predict_proba(self, x_test: np.ndarray) -> np.ndarray:
         super().predict_proba(x_test)
         n_samples = x_test.shape[0]
+        if self.constant is not None:
+            return np.array([self.constant for n in range(n_samples)])
         sklearn_proba = self.inner_clf.predict_proba(x_test)
         if isinstance(sklearn_proba, list):
             assert len(sklearn_proba) == self.n_classes

tests/classifiers/test_sklearn.py

@@ -0,0 +1,40 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import numpy as np
+from numpy.testing import assert_array_equal
+from sklearn.neighbors import KNeighborsClassifier
+
+from miplearn import ScikitLearnClassifier
+
+
+def test_constant_prediction():
+    x_train = np.array(
+        [
+            [0.0, 1.0],
+            [1.0, 0.0],
+        ]
+    )
+    y_train = np.array(
+        [
+            [True, False],
+            [True, False],
+        ]
+    )
+    clf = ScikitLearnClassifier(
+        KNeighborsClassifier(
+            n_neighbors=1,
+        )
+    )
+    clf.fit(x_train, y_train)
+    proba = clf.predict_proba(x_train)
+    assert_array_equal(
+        proba,
+        np.array(
+            [
+                [1.0, 0.0],
+                [1.0, 0.0],
+            ]
+        ),
+    )