Add Maximum-Weight Stable Set Problem

miplearn/tests/test_stab.py

@@ -0,0 +1,31 @@
+# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
+# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+# Written by Alinson S. Xavier <axavier@anl.gov>
+
+from miplearn import LearningSolver
+from miplearn.problems.stab import MaxStableSetInstance, MaxStableSetGenerator
+import networkx as nx
+import numpy as np
+
+
+def test_stab():
+    graph = nx.cycle_graph(5)
+    weights = [1.0, 2.0, 3.0, 4.0, 5.0]
+    instance = MaxStableSetInstance(graph, weights)
+    solver = LearningSolver()
+    solver.solve(instance)
+    assert instance.model.OBJ() == 8.0
+    
+    
+def test_stab_generator():
+    graph = nx.cycle_graph(5)
+    base_weights = [1.0, 2.0, 3.0, 4.0, 5.0]
+    generator = MaxStableSetGenerator(graph=graph,
+                                      base_weights=base_weights,
+                                      perturbation_scale=1.0)
+    instances = [generator.generate() for _ in range(100_000)]
+    weights = np.array([instance.weights for instance in instances])
+    weights_avg = np.round(np.average(weights, axis=0), 2)
+    weights_std = np.round(np.std(weights, axis=0), 2)
+    assert list(weights_avg) == [1.50, 2.50, 3.50, 4.50, 5.50]
+    assert list(weights_std) == [0.29] * 5