Reorganize directories

miplearn/problems/tests/test_stab.py

@@ -1,45 +0,0 @@
-#  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.
-
-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., 1., 1., 1., 1.]
-    instance = MaxWeightStableSetInstance(graph, weights)
-    solver = LearningSolver()
-    solver.solve(instance)
-    assert instance.model.OBJ() == 2.
-    
-    
-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),
-                                      p=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),
-                                      p=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.