Implement BenchmarkRunner

miplearn/tests/test_benchmark.py

@@ -0,0 +1,41 @@
+# 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, BenchmarkRunner
+from miplearn.warmstart import KnnWarmStartPredictor
+from miplearn.problems.stab import MaxStableSetInstance, MaxStableSetGenerator
+import networkx as nx
+import numpy as np
+import pyomo.environ as pe
+
+
+def test_benchmark():
+    graph = nx.cycle_graph(10)
+    base_weights = np.random.rand(10)
+    
+    # Generate training and test instances
+    train_instances = MaxStableSetGenerator(graph=graph,
+                                            base_weights=base_weights,
+                                            perturbation_scale=1.0,
+                                           ).generate(5)
+    
+    test_instances  = MaxStableSetGenerator(graph=graph,
+                                            base_weights=base_weights,
+                                            perturbation_scale=1.0,
+                                           ).generate(3)
+
+    # Training phase...
+    training_solver = LearningSolver()
+    training_solver.parallel_solve(train_instances, n_jobs=10)
+    training_solver.save("data.bin")
+
+    # Test phase...
+    test_solvers = {
+        "Strategy A": LearningSolver(ws_predictor=None),
+        "Strategy B": LearningSolver(ws_predictor=None),
+    }
+    benchmark = BenchmarkRunner(test_solvers)
+    benchmark.load_fit("data.bin")
+    benchmark.parallel_solve(test_instances, n_jobs=2)
+    print(benchmark.raw_results())

miplearn/tests/test_solver.py

@@ -38,6 +38,6 @@ def test_parallel_solve():
                                    capacity=3.0)
                  for _ in range(10)]
     solver = LearningSolver()
-    solver.parallel_solve(instances, n_jobs=2)
+    solver.parallel_solve(instances, n_jobs=3)
     assert len(solver.x_train[0]) == 10
     assert len(solver.y_train[0]) == 10

miplearn/tests/test_stab.py

@@ -20,10 +20,10 @@ def test_stab():
 def test_stab_generator():
     graph = nx.cycle_graph(5)
     base_weights = [1.0, 2.0, 3.0, 4.0, 5.0]
-    generator = MaxStableSetGenerator(graph=graph,
-                                      base_weights=base_weights,
-                                      perturbation_scale=1.0)
-    instances = [generator.generate() for _ in range(100_000)]
+    instances = MaxStableSetGenerator(graph=graph,
+                                       base_weights=base_weights,
+                                       perturbation_scale=1.0,
+                                      ).generate(100_000)
     weights = np.array([instance.weights for instance in instances])
     weights_avg = np.round(np.average(weights, axis=0), 2)
     weights_std = np.round(np.std(weights, axis=0), 2)