Move tests to separate folder

tests/problems/__init__.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.

tests/problems/test_knapsack.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 numpy as np
+from scipy.stats import uniform, randint
+
+from miplearn.problems.knapsack import MultiKnapsackGenerator
+
+
+def test_knapsack_generator():
+    gen = MultiKnapsackGenerator(
+        n=randint(low=100, high=101),
+        m=randint(low=30, high=31),
+        w=randint(low=0, high=1000),
+        K=randint(low=500, high=501),
+        u=uniform(loc=1.0, scale=1.0),
+        alpha=uniform(loc=0.50, scale=0.0),
+    )
+    instances = gen.generate(100)
+    w_sum = sum(instance.weights for instance in instances) / len(instances)
+    b_sum = sum(instance.capacities for instance in instances) / len(instances)
+    assert round(np.mean(w_sum), -1) == 500.0
+    assert round(np.mean(b_sum), -3) == 25000.0

tests/problems/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 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

tests/problems/test_tsp.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 numpy as np
+from numpy.linalg import norm
+from scipy.spatial.distance import pdist, squareform
+from scipy.stats import uniform, randint
+
+from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
+from miplearn.solvers.learning import LearningSolver
+
+
+def test_generator():
+    instances = TravelingSalesmanGenerator(
+        x=uniform(loc=0.0, scale=1000.0),
+        y=uniform(loc=0.0, scale=1000.0),
+        n=randint(low=100, high=101),
+        gamma=uniform(loc=0.95, scale=0.1),
+        fix_cities=True,
+    ).generate(100)
+    assert len(instances) == 100
+    assert instances[0].n_cities == 100
+    assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
+    d = [instance.distances[0, 1] for instance in instances]
+    assert np.std(d) > 0
+
+
+def test_instance():
+    n_cities = 4
+    distances = np.array(
+        [
+            [0.0, 1.0, 2.0, 1.0],
+            [1.0, 0.0, 1.0, 2.0],
+            [2.0, 1.0, 0.0, 1.0],
+            [1.0, 2.0, 1.0, 0.0],
+        ]
+    )
+    instance = TravelingSalesmanInstance(n_cities, distances)
+    solver = LearningSolver()
+    stats = solver.solve(instance)
+    x = instance.training_data[0]["Solution"]["x"]
+    assert x[0, 1] == 1.0
+    assert x[0, 2] == 0.0
+    assert x[0, 3] == 1.0
+    assert x[1, 2] == 1.0
+    assert x[1, 3] == 0.0
+    assert x[2, 3] == 1.0
+    assert stats["Lower bound"] == 4.0
+    assert stats["Upper bound"] == 4.0
+
+
+def test_subtour():
+    n_cities = 6
+    cities = np.array(
+        [
+            [0.0, 0.0],
+            [1.0, 0.0],
+            [2.0, 0.0],
+            [3.0, 0.0],
+            [0.0, 1.0],
+            [3.0, 1.0],
+        ]
+    )
+    distances = squareform(pdist(cities))
+    instance = TravelingSalesmanInstance(n_cities, distances)
+    solver = LearningSolver()
+    solver.solve(instance)
+    assert hasattr(instance, "found_violated_lazy_constraints")
+    assert hasattr(instance, "found_violated_user_cuts")
+    x = instance.training_data[0]["Solution"]["x"]
+    assert x[0, 1] == 1.0
+    assert x[0, 4] == 1.0
+    assert x[1, 2] == 1.0
+    assert x[2, 3] == 1.0
+    assert x[3, 5] == 1.0
+    assert x[4, 5] == 1.0
+    solver.fit([instance])
+    solver.solve(instance)