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50 lines
1.9 KiB
50 lines
1.9 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from miplearn import LearningSolver, BenchmarkRunner
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from miplearn.warmstart import KnnWarmStartPredictor
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from miplearn.problems.stab import MaxStableSetInstance, MaxStableSetGenerator
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import networkx as nx
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import numpy as np
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import pyomo.environ as pe
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import os.path
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def test_benchmark():
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graph = nx.cycle_graph(10)
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base_weights = np.random.rand(10)
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# Generate training and test instances
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train_instances = MaxStableSetGenerator(graph=graph,
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base_weights=base_weights,
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perturbation_scale=1.0,
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).generate(5)
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test_instances = MaxStableSetGenerator(graph=graph,
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base_weights=base_weights,
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perturbation_scale=1.0,
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).generate(3)
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# Training phase...
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training_solver = LearningSolver()
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training_solver.parallel_solve(train_instances, n_jobs=10)
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training_solver.save("data.bin")
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# Test phase...
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test_solvers = {
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"Strategy A": LearningSolver(ws_predictor=None),
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"Strategy B": LearningSolver(ws_predictor=None),
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}
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benchmark = BenchmarkRunner(test_solvers)
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benchmark.load_fit("data.bin")
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benchmark.parallel_solve(test_instances, n_jobs=2)
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assert benchmark.raw_results().values.shape == (6,6)
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benchmark.save_results("/tmp/benchmark.csv")
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assert os.path.isfile("/tmp/benchmark.csv")
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benchmark = BenchmarkRunner(test_solvers)
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benchmark.load_results("/tmp/benchmark.csv")
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assert benchmark.raw_results().values.shape == (6,6)
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