Move tests to separate folder

tests/classifiers/test_cv.py

@@ -0,0 +1,49 @@
+#  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.linalg import norm
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+
+from miplearn.classifiers.cv import CrossValidatedClassifier
+
+E = 0.1
+
+
+def test_cv():
+    # 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)
+    n_samples = x_train.shape[0]
+
+    y_train = np.array(
+        [
+            1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
+            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.
+    clf = CrossValidatedClassifier(
+        classifier=SVC(probability=True, random_state=42),
+        threshold=0.90,
+        constant=0.0,
+        cv=30,
+    )
+    clf.fit(x_train, y_train)
+    assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
+
+    # Support vector machines with quadratic kernels perform almost perfectly
+    # on this data set, so predictor should return their prediction.
+    clf = CrossValidatedClassifier(
+        classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
+        threshold=0.90,
+        cv=30,
+    )
+    clf.fit(x_train, y_train)
+    print(y_train - clf.predict(x_train))
+    assert norm(y_train - clf.predict(x_train)) < E