Implement MultiKnapsackGenerator and MultiKnapsackInstance

miplearn/problems/tests/test_knapsack.py

@@ -0,0 +1,44 @@
+# 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.knapsack import MultiKnapsackGenerator, MultiKnapsackInstance
+from scipy.stats import uniform, randint
+import numpy as np
+
+
+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)
+    p_sum = sum(instance.prices 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.
+    assert round(np.mean(p_sum), -1) == 1250.
+    assert round(np.mean(b_sum), -3) == 25000.
+
+    
+def test_knapsack_instance():
+    instance = MultiKnapsackInstance(
+        prices=np.array([5.0, 10.0, 15.0]),
+        capacities=np.array([20.0, 30.0]),
+        weights=np.array([
+            [5.0,  5.0,  5.0],
+            [5.0, 10.0, 15.0],
+        ])
+    )
+    
+    assert (instance.get_instance_features() == np.array([
+        5.0, 10.0, 15.0, 20.0, 30.0, 5.0, 5.0, 5.0, 5.0, 10.0, 15.0
+    ])).all()
+    
+    solver = LearningSolver()
+    results = solver.solve(instance)
+    assert results["Problem"][0]["Lower bound"] == 30.0