Move tests to separate folder

tests/classifiers/__init__.py

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+#  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.

tests/classifiers/test_counting.py

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+#  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 miplearn.classifiers.counting import CountingClassifier
+
+E = 0.1
+
+
+def test_counting():
+    clf = CountingClassifier()
+    clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
+    expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
+    actual_proba = clf.predict_proba(np.zeros((2, 25)))
+    assert norm(actual_proba - expected_proba) < E

tests/classifiers/test_cv.py

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+#  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

tests/classifiers/test_evaluator.py

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+#  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

tests/classifiers/test_threshold.py

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+#  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 unittest.mock import Mock
+
+import numpy as np
+
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.threshold import MinPrecisionThreshold
+
+
+def test_threshold_dynamic():
+    clf = Mock(spec=Classifier)
+    clf.predict_proba = Mock(
+        return_value=np.array(
+            [
+                [0.10, 0.90],
+                [0.10, 0.90],
+                [0.20, 0.80],
+                [0.30, 0.70],
+            ]
+        )
+    )
+    x_train = np.array([0, 1, 2, 3])
+    y_train = np.array([1, 1, 0, 0])
+
+    threshold = MinPrecisionThreshold(min_precision=1.0)
+    assert threshold.find(clf, x_train, y_train) == 0.90
+
+    threshold = MinPrecisionThreshold(min_precision=0.65)
+    assert threshold.find(clf, x_train, y_train) == 0.80
+
+    threshold = MinPrecisionThreshold(min_precision=0.50)
+    assert threshold.find(clf, x_train, y_train) == 0.70
+
+    threshold = MinPrecisionThreshold(min_precision=0.00)
+    assert threshold.find(clf, x_train, y_train) == 0.70