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https://github.com/ANL-CEEESA/MIPLearn.jl.git
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Minor fixes
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@@ -17,6 +17,15 @@ Base.@kwdef mutable struct _JumpModelExtData
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cuts_separate::Union{Function,Nothing} = nothing
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end
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function JuMP.copy_extension_data(
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::_JumpModelExtData,
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new_model::AbstractModel,
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::AbstractModel,
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)
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# Do not transfer any extension data to the new model
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new_model.ext[:miplearn] = _JumpModelExtData()
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end
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# -----------------------------------------------------------------------------
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function _add_constrs(
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@@ -28,15 +28,13 @@ end
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function test_cuts()
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data_filenames = ["$BASEDIR/../fixtures/stab-n50-0000$i.pkl.gz" for i in 0:0]
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clf = pyimport("sklearn.neighbors").KNeighborsClassifier(n_neighbors=1)
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clf = pyimport("sklearn.dummy").DummyClassifier()
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extractor = H5FieldsExtractor(
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instance_fields=["static_var_obj_coeffs"],
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)
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comp = MemorizingCutsComponent(clf=clf, extractor=extractor)
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solver = LearningSolver(components=[comp])
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solver.fit(data_filenames)
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@show comp.n_features_
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@show comp.n_targets_
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stats = solver.optimize(
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data_filenames[1],
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data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
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@@ -6,11 +6,6 @@ using PyCall
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using SCIP
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function test_problems_stab()
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test_problems_stab_1()
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test_problems_stab_2()
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end
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function test_problems_stab_1()
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nx = pyimport("networkx")
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data = MaxWeightStableSetData(
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graph=nx.gnp_random_graph(25, 0.5, seed=42),
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