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MIPLearn.jl/test/src/components/test_lazy.jl

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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using GLPK
function gen_tsp()
np = pyimport("numpy")
uniform = pyimport("scipy.stats").uniform
randint = pyimport("scipy.stats").randint
np.random.seed(42)
gen = TravelingSalesmanGenerator(
x = uniform(loc = 0.0, scale = 1000.0),
y = uniform(loc = 0.0, scale = 1000.0),
n = randint(low = 20, high = 21),
gamma = uniform(loc = 1.0, scale = 0.25),
fix_cities = true,
round = true,
)
data = gen.generate(1)
data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix = "tsp-n20-")
collector = BasicCollector()
collector.collect(
data_filenames,
data -> build_tsp_model_jump(data, optimizer = GLPK.Optimizer),
progress = true,
verbose = true,
)
end
function test_lazy()
data_filenames = ["$BASEDIR/../fixtures/tsp-n20-00000.pkl.gz"]
clf = pyimport("sklearn.dummy").DummyClassifier()
extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
comp = MemorizingLazyComponent(clf = clf, extractor = extractor)
solver = LearningSolver(components = [comp])
solver.fit(data_filenames)
model, stats = solver.optimize(
data_filenames[1],
data -> build_tsp_model_jump(data, optimizer = GLPK.Optimizer),
)
@test stats["Lazy Constraints: AOT"] > 0
end