AdaptiveClassifier: Refactor and add tests

tests/classifiers/__init__.py

@@ -1,3 +1,39 @@
 #  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 Tuple
+
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+
+def _build_circle_training_data() -> Tuple[np.ndarray, np.ndarray]:
+    x_train = StandardScaler().fit_transform(
+        np.array(
+            [
+                [
+                    x1,
+                    x2,
+                ]
+                for x1 in range(-10, 11)
+                for x2 in range(-10, 11)
+            ]
+        )
+    )
+    y_train = np.array(
+        [
+            [
+                False,
+                True,
+            ]
+            if x1 * x1 + x2 * x2 <= 100
+            else [
+                True,
+                False,
+            ]
+            for x1 in range(-10, 11)
+            for x2 in range(-10, 11)
+        ]
+    )
+    return x_train, y_train

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

tests/classifiers/test_cv.py

@@ -4,44 +4,18 @@
 
 import numpy as np
 from numpy.linalg import norm
-from sklearn.preprocessing import StandardScaler
 from sklearn.svm import SVC
 
 from miplearn.classifiers import ScikitLearnClassifier
 from miplearn.classifiers.cv import CrossValidatedClassifier
+from tests.classifiers import _build_circle_training_data
 
 E = 0.1
 
 
 def test_cv() -> None:
-    # Training set: label is true if point is inside a 2D circle
-    x_train = np.array(
-        [
-            [
-                x1,
-                x2,
-            ]
-            for x1 in range(-10, 11)
-            for x2 in range(-10, 11)
-        ]
-    )
-    x_train = StandardScaler().fit_transform(x_train)
+    x_train, y_train = _build_circle_training_data()
     n_samples = x_train.shape[0]
-    y_train = np.array(
-        [
-            [
-                False,
-                True,
-            ]
-            if x1 * x1 + x2 * x2 <= 100
-            else [
-                True,
-                False,
-            ]
-            for x1 in range(-10, 11)
-            for x2 in range(-10, 11)
-        ]
-    )
 
     # Support vector machines with linear kernels do not perform well on this
     # data set, so predictor should return the given constant.

tests/classifiers/test_evaluator.py

@@ -1,20 +0,0 @@
-#  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 sklearn.neighbors import KNeighborsClassifier
-
-from miplearn.classifiers.evaluator import ClassifierEvaluator
-
-
-def test_evaluator():
-    clf_a = KNeighborsClassifier(n_neighbors=1)
-    clf_b = KNeighborsClassifier(n_neighbors=2)
-    x_train = np.array([[0, 0], [1, 0]])
-    y_train = np.array([0, 1])
-    clf_a.fit(x_train, y_train)
-    clf_b.fit(x_train, y_train)
-    ev = ClassifierEvaluator()
-    assert ev.evaluate(clf_a, x_train, y_train) == 1.0
-    assert ev.evaluate(clf_b, x_train, y_train) == 0.5