Improve stable set generator

miplearn/problems/tests/test_stab.py

@@ -0,0 +1,45 @@
+# MIPLearn, an extensible framework for Learning-Enhanced 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 MaxWeightStableSetInstance
+from miplearn.problems.stab import MaxWeightStableSetGenerator
+import networkx as nx
+import numpy as np
+from scipy.stats import uniform, randint
+
+
+def test_stab():
+    graph = nx.cycle_graph(5)
+    weights = [1., 2., 3., 4., 5.]
+    instance = MaxWeightStableSetInstance(graph, weights)
+    solver = LearningSolver()
+    solver.solve(instance)
+    assert instance.model.OBJ() == 8.
+    
+    
+def test_stab_generator_fixed_graph():
+    from miplearn.problems.stab import MaxWeightStableSetGenerator
+    gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
+                                      n=randint(low=10, high=11),
+                                      density=uniform(loc=0.05, scale=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():
+    from miplearn.problems.stab import MaxWeightStableSetGenerator
+    gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
+                                      n=randint(low=30, high=41),
+                                      density=uniform(loc=0.5, scale=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.