# Module `miplearn.problems.tests.test_stab`

Expand source code

    #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
    #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
    #  Released under the modified BSD license. See COPYING.md for more details.

    import networkx as nx
    import numpy as np
    from scipy.stats import uniform, randint

    from miplearn.problems.stab import MaxWeightStableSetInstance
    from miplearn.solvers.learning import LearningSolver

    def test_stab():
        graph = nx.cycle_graph(5)
        weights = [1.0, 1.0, 1.0, 1.0, 1.0]
        instance = MaxWeightStableSetInstance(graph, weights)
        solver = LearningSolver()
        stats = solver.solve(instance)
        assert stats["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=50.0, 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=50.0, 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)) == 35.0
        assert np.round(np.mean(n_edges), -1) == 300.0

## Functions

`def test_stab()`
  Expand source code

      def test_stab():
          graph = nx.cycle_graph(5)
          weights = [1.0, 1.0, 1.0, 1.0, 1.0]
          instance = MaxWeightStableSetInstance(graph, weights)
          solver = LearningSolver()
          stats = solver.solve(instance)
          assert stats["Lower bound"] == 2.0

`def test_stab_generator_fixed_graph()`
  Expand source code

      def test_stab_generator_fixed_graph():
          np.random.seed(42)
          from miplearn.problems.stab import MaxWeightStableSetGenerator

          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()`
  Expand source code

      def test_stab_generator_random_graph():
          np.random.seed(42)
          from miplearn.problems.stab import MaxWeightStableSetGenerator

          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)) == 35.0
          assert np.round(np.mean(n_edges), -1) == 300.0