Reformat source code with Black; add pre-commit hooks and CI checks

miplearn/classifiers/tests/test_counting.py

@@ -12,7 +12,6 @@ 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]])
+    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

miplearn/classifiers/tests/test_cv.py

@@ -13,34 +13,36 @@ 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 = 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)])
+    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 = 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 = 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

miplearn/classifiers/tests/test_evaluator.py

@@ -17,4 +17,3 @@ def test_evaluator():
     ev = ClassifierEvaluator()
     assert ev.evaluate(clf_a, x_train, y_train) == 1.0
     assert ev.evaluate(clf_b, x_train, y_train) == 0.5
-

miplearn/classifiers/tests/test_threshold.py

@@ -11,12 +11,16 @@ 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],
-    ]))
+    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])
 
@@ -31,4 +35,3 @@ def test_threshold_dynamic():
 
     threshold = MinPrecisionThreshold(min_precision=0.00)
     assert threshold.find(clf, x_train, y_train) == 0.70
-