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

miplearn/problems/tests/test_stab.py

@@ -11,36 +11,42 @@ from scipy.stats import uniform, randint
 
 def test_stab():
     graph = nx.cycle_graph(5)
-    weights = [1., 1., 1., 1., 1.]
+    weights = [1.0, 1.0, 1.0, 1.0, 1.0]
     instance = MaxWeightStableSetInstance(graph, weights)
     solver = LearningSolver()
     solver.solve(instance)
-    assert instance.lower_bound == 2.
-    
-    
+    assert instance.lower_bound == 2.0
+
+
 def test_stab_generator_fixed_graph():
     np.random.seed(42)
     from miplearn.problems.stab import MaxWeightStableSetGenerator
-    gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
-                                      n=randint(low=10, high=11),
-                                      p=uniform(loc=0.05, scale=0.),
-                                      fix_graph=True)
+
+    gen = MaxWeightStableSetGenerator(
+        w=uniform(loc=50.0, scale=10.0),
+        n=randint(low=10, high=11),
+        p=uniform(loc=0.05, scale=0.0),
+        fix_graph=True,
+    )
     instances = gen.generate(1_000)
     weights = np.array([instance.weights for instance in instances])
     weights_avg_actual = np.round(np.average(weights, axis=0))
     weights_avg_expected = [55.0] * 10
     assert list(weights_avg_actual) == weights_avg_expected
-    
-    
+
+
 def test_stab_generator_random_graph():
     np.random.seed(42)
     from miplearn.problems.stab import MaxWeightStableSetGenerator
-    gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
-                                      n=randint(low=30, high=41),
-                                      p=uniform(loc=0.5, scale=0.),
-                                      fix_graph=False)
+
+    gen = MaxWeightStableSetGenerator(
+        w=uniform(loc=50.0, scale=10.0),
+        n=randint(low=30, high=41),
+        p=uniform(loc=0.5, scale=0.0),
+        fix_graph=False,
+    )
     instances = gen.generate(1_000)
     n_nodes = [instance.graph.number_of_nodes() for instance in instances]
     n_edges = [instance.graph.number_of_edges() for instance in instances]
-    assert np.round(np.mean(n_nodes)) == 35.
-    assert np.round(np.mean(n_edges), -1) == 300.
+    assert np.round(np.mean(n_nodes)) == 35.0
+    assert np.round(np.mean(n_edges), -1) == 300.0