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

miplearn/components/primal.py

@@ -19,10 +19,12 @@ class PrimalSolutionComponent(Component):
     A component that predicts primal solutions.
     """
 
-    def __init__(self,
-                 classifier=AdaptiveClassifier(),
-                 mode="exact",
-                 threshold=MinPrecisionThreshold(0.98)):
+    def __init__(
+        self,
+        classifier=AdaptiveClassifier(),
+        mode="exact",
+        threshold=MinPrecisionThreshold(0.98),
+    ):
         self.mode = mode
         self.classifiers = {}
         self.thresholds = {}
@@ -51,9 +53,10 @@ class PrimalSolutionComponent(Component):
         features = VariableFeaturesExtractor().extract(training_instances)
         solutions = SolutionExtractor().extract(training_instances)
 
-        for category in tqdm(features.keys(),
-                             desc="Fit (primal)",
-                            ):
+        for category in tqdm(
+            features.keys(),
+            desc="Fit (primal)",
+        ):
             x_train = features[category]
             for label in [0, 1]:
                 y_train = solutions[category][:, label].astype(int)
@@ -74,9 +77,15 @@ class PrimalSolutionComponent(Component):
 
                 # Find threshold (dynamic or static)
                 if isinstance(self.threshold_prototype, DynamicThreshold):
-                    self.thresholds[category, label] = self.threshold_prototype.find(clf, x_train, y_train)
+                    self.thresholds[category, label] = self.threshold_prototype.find(
+                        clf,
+                        x_train,
+                        y_train,
+                    )
                 else:
-                    self.thresholds[category, label] = deepcopy(self.threshold_prototype)
+                    self.thresholds[category, label] = deepcopy(
+                        self.threshold_prototype
+                    )
 
                 self.classifiers[category, label] = clf
 
@@ -98,18 +107,21 @@ class PrimalSolutionComponent(Component):
                     ws = np.array([[1 - clf, clf] for _ in range(n)])
                 else:
                     ws = clf.predict_proba(x_test[category])
-                assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (n, ws.shape)
+                assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
+                    n,
+                    ws.shape,
+                )
                 for (i, (var, index)) in enumerate(var_split[category]):
                     if ws[i, 1] >= self.thresholds[category, label]:
                         solution[var][index] = label
         return solution
 
     def evaluate(self, instances):
-        ev = {"Fix zero": {},
-              "Fix one": {}}
-        for instance_idx in tqdm(range(len(instances)),
-                                 desc="Evaluate (primal)",
-                                ):
+        ev = {"Fix zero": {}, "Fix one": {}}
+        for instance_idx in tqdm(
+            range(len(instances)),
+            desc="Evaluate (primal)",
+        ):
             instance = instances[instance_idx]
             solution_actual = instance.solution
             solution_pred = self.predict(instance)
@@ -143,6 +155,10 @@ class PrimalSolutionComponent(Component):
             tn_one = len(pred_one_negative & vars_zero)
             fn_one = len(pred_one_negative & vars_one)
 
-            ev["Fix zero"][instance_idx] = classifier_evaluation_dict(tp_zero, tn_zero, fp_zero, fn_zero)
-            ev["Fix one"][instance_idx] = classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one)
+            ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
+                tp_zero, tn_zero, fp_zero, fn_zero
+            )
+            ev["Fix one"][instance_idx] = classifier_evaluation_dict(
+                tp_one, tn_one, fp_one, fn_one
+            )
         return ev