ANL-CEEESA/MIPLearn.jl
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Reformat source code

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This commit is contained in:
Alinson S. Xavier
2024-05-29 09:04:59 -05:00
parent e9deac94a5
commit 93e604817b
15 changed files with 106 additions and 107 deletions
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26
test/src/components/test_cuts.jl
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@@ -10,34 +10,32 @@ function gen_stab()
randint = pyimport("scipy.stats").randint
np.random.seed(42)
gen = MaxWeightStableSetGenerator(
w=uniform(10.0, scale=1.0),
n=randint(low=50, high=51),
p=uniform(loc=0.5, scale=0.0),
fix_graph=true,
w = uniform(10.0, scale = 1.0),
n = randint(low = 50, high = 51),
p = uniform(loc = 0.5, scale = 0.0),
fix_graph = true,
)
data = gen.generate(1)
data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix="stab-n50-")
data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix = "stab-n50-")
collector = BasicCollector()
collector.collect(
data_filenames,
data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
progress=true,
verbose=true,
data -> build_stab_model_jump(data, optimizer = SCIP.Optimizer),
progress = true,
verbose = true,
)
end
function test_cuts()
data_filenames = ["$BASEDIR/../fixtures/stab-n50-00000.pkl.gz"]
clf = pyimport("sklearn.dummy").DummyClassifier()
extractor = H5FieldsExtractor(
instance_fields=["static_var_obj_coeffs"],
)
comp = MemorizingCutsComponent(clf=clf, extractor=extractor)
solver = LearningSolver(components=[comp])
extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
comp = MemorizingCutsComponent(clf = clf, extractor = extractor)
solver = LearningSolver(components = [comp])
solver.fit(data_filenames)
stats = solver.optimize(
data_filenames[1],
data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
data -> build_stab_model_jump(data, optimizer = SCIP.Optimizer),
)
@test stats["Cuts: AOT"] > 0
end

30
test/src/components/test_lazy.jl
View File

@@ -11,36 +11,34 @@ function gen_tsp()
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,
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, "$BASEDIR/../fixtures", prefix="tsp-n20-")
data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix = "tsp-n20-")
collector = BasicCollector()
collector.collect(
data_filenames,
data -> build_tsp_model_jump(data, optimizer=GLPK.Optimizer),
progress=true,
verbose=true,
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])
extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
comp = MemorizingLazyComponent(clf = clf, extractor = extractor)
solver = LearningSolver(components = [comp])
solver.fit(data_filenames)
stats = solver.optimize(
data_filenames[1],
data -> build_tsp_model_jump(data, optimizer=GLPK.Optimizer),
data -> build_tsp_model_jump(data, optimizer = GLPK.Optimizer),
)
@test stats["Lazy Constraints: AOT"] > 0
end