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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from miplearn import LearningSolver, BenchmarkRunner
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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from scipy.stats import randint
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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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# Generate training and test instances
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train_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(5)
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test_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).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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# Test phase...
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test_solvers = {
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"Strategy A": LearningSolver(),
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"Strategy B": LearningSolver(),
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}
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benchmark = BenchmarkRunner(test_solvers)
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benchmark.fit(train_instances)
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benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
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assert benchmark.raw_results().values.shape == (12,16)
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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 == (12,16)
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from miplearn.problems.knapsack import KnapsackInstance
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from miplearn import (LearningSolver,
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SolutionExtractor,
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InstanceFeaturesExtractor,
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VariableFeaturesExtractor,
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)
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import numpy as np
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import pyomo.environ as pe
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def _get_instances():
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instances = [
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KnapsackInstance(weights=[1., 2., 3.],
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prices=[10., 20., 30.],
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capacity=2.5,
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),
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KnapsackInstance(weights=[3., 4., 5.],
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prices=[20., 30., 40.],
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capacity=4.5,
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),
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]
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models = [instance.to_model() for instance in instances]
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solver = LearningSolver()
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for (i, instance) in enumerate(instances):
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solver.solve(instances[i], models[i])
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return instances, models
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def test_solution_extractor():
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instances, models = _get_instances()
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features = SolutionExtractor().extract(instances)
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assert isinstance(features, dict)
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assert "default" in features.keys()
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assert isinstance(features["default"], np.ndarray)
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assert features["default"].shape == (6, 2)
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assert features["default"].ravel().tolist() == [
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1., 0.,
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0., 1.,
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1., 0.,
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1., 0.,
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0., 1.,
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1., 0.,
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]
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def test_instance_features_extractor():
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instances, models = _get_instances()
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features = InstanceFeaturesExtractor().extract(instances)
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assert features.shape == (2,3)
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def test_variable_features_extractor():
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instances, models = _get_instances()
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features = VariableFeaturesExtractor().extract(instances)
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assert isinstance(features, dict)
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assert "default" in features
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assert features["default"].shape == (6,5)
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@@ -1,51 +0,0 @@
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from miplearn import LearningSolver, BranchPriorityComponent
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from miplearn.problems.knapsack import KnapsackInstance
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def _get_instance():
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return KnapsackInstance(
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weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.,
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)
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def test_solver():
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instance = _get_instance()
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for mode in ["exact", "heuristic"]:
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for internal_solver in ["cplex", "gurobi"]:
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solver = LearningSolver(time_limit=300,
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gap_tolerance=1e-3,
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threads=1,
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solver=internal_solver,
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mode=mode,
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)
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results = solver.solve(instance)
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assert instance.solution["x"][0] == 1.0
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assert instance.solution["x"][1] == 0.0
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assert instance.solution["x"][2] == 1.0
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assert instance.solution["x"][3] == 1.0
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assert instance.lower_bound == 1183.0
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assert instance.upper_bound == 1183.0
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assert round(instance.lp_solution["x"][0], 3) == 1.000
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assert round(instance.lp_solution["x"][1], 3) == 0.923
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assert round(instance.lp_solution["x"][2], 3) == 1.000
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assert round(instance.lp_solution["x"][3], 3) == 0.000
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assert round(instance.lp_value, 3) == 1287.923
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solver.fit()
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solver.solve(instance)
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def test_parallel_solve():
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instances = [_get_instance() for _ in range(10)]
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solver = LearningSolver()
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results = solver.parallel_solve(instances, n_jobs=3)
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assert len(results) == 10
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for instance in instances:
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assert len(instance.solution["x"].keys()) == 4
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