You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
56 lines
2.0 KiB
56 lines
2.0 KiB
# 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.
|
|
|
|
from miplearn import LearningSolver, PrimalSolutionComponent
|
|
from miplearn.problems.knapsack import KnapsackInstance
|
|
import numpy as np
|
|
import tempfile
|
|
|
|
|
|
def _get_instances():
|
|
instances = [
|
|
KnapsackInstance(
|
|
weights=[23., 26., 20., 18.],
|
|
prices=[505., 352., 458., 220.],
|
|
capacity=67.,
|
|
),
|
|
] * 5
|
|
models = [inst.to_model() for inst in instances]
|
|
solver = LearningSolver()
|
|
for i in range(len(instances)):
|
|
solver.solve(instances[i], models[i])
|
|
return instances, models
|
|
|
|
|
|
def test_predict():
|
|
instances, models = _get_instances()
|
|
comp = PrimalSolutionComponent()
|
|
comp.fit(instances)
|
|
solution = comp.predict(instances[0], models[0])
|
|
assert models[0].x in solution.keys()
|
|
for idx in range(4):
|
|
assert idx in solution[models[0].x].keys()
|
|
|
|
# def test_warm_start_save_load():
|
|
# state_file = tempfile.NamedTemporaryFile(mode="r")
|
|
# solver = LearningSolver(components={"warm-start": WarmStartComponent()})
|
|
# solver.parallel_solve(_get_instances(), n_jobs=2)
|
|
# solver.fit()
|
|
# comp = solver.components["warm-start"]
|
|
# assert comp.x_train["default"].shape == (8, 6)
|
|
# assert comp.y_train["default"].shape == (8, 2)
|
|
# assert ("default", 0) in comp.predictors.keys()
|
|
# assert ("default", 1) in comp.predictors.keys()
|
|
# solver.save_state(state_file.name)
|
|
|
|
# solver.solve(_get_instances()[0])
|
|
|
|
# solver = LearningSolver(components={"warm-start": WarmStartComponent()})
|
|
# solver.load_state(state_file.name)
|
|
# comp = solver.components["warm-start"]
|
|
# assert comp.x_train["default"].shape == (8, 6)
|
|
# assert comp.y_train["default"].shape == (8, 2)
|
|
# assert ("default", 0) in comp.predictors.keys()
|
|
# assert ("default", 1) in comp.predictors.keys()
|