Move python files to root folder; remove built docs

miplearn/problems/tests/test_stab.py

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+#  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 miplearn import LearningSolver
+from miplearn.problems.stab import MaxWeightStableSetInstance
+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.lower_bound == 2.
+    
+    
+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)
+    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)
+    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.