AdaptiveClassifier: Refactor and add tests

tests/classifiers/test_adaptive.py

@@ -0,0 +1,41 @@
+#  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.
+from typing import cast
+
+from numpy.linalg import norm
+from sklearn.svm import SVC
+
+from miplearn import AdaptiveClassifier, ScikitLearnClassifier
+from miplearn.classifiers.adaptive import CandidateClassifierSpecs
+from tests.classifiers import _build_circle_training_data
+
+
+def test_adaptive() -> None:
+    clf = AdaptiveClassifier(
+        candidates={
+            "linear": CandidateClassifierSpecs(
+                classifier=lambda: ScikitLearnClassifier(
+                    SVC(
+                        probability=True,
+                        random_state=42,
+                    )
+                )
+            ),
+            "poly": CandidateClassifierSpecs(
+                classifier=lambda: ScikitLearnClassifier(
+                    SVC(
+                        probability=True,
+                        kernel="poly",
+                        degree=2,
+                        random_state=42,
+                    )
+                )
+            ),
+        }
+    )
+    x_train, y_train = _build_circle_training_data()
+    clf.fit(x_train, y_train)
+    proba = clf.predict_proba(x_train)
+    y_pred = (proba[:, 1] > 0.5).astype(float)
+    assert norm(y_train[:, 1] - y_pred) < 0.1