Make cuts component compatible with JuMP

test/src/components/test_cuts.jl

@@ -0,0 +1,45 @@
+#  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 SCIP
+
+function gen_stab()
+    np = pyimport("numpy")
+    uniform = pyimport("scipy.stats").uniform
+    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,
+    )
+    data = gen.generate(1)
+    data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix="stab-n50-")
+    collector = BasicCollector(write_mps=false)
+    collector.collect(
+        data_filenames,
+        data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
+        progress=true,
+        verbose=true,
+    )
+end
+
+function test_cuts()
+    data_filenames = ["$BASEDIR/../fixtures/stab-n50-0000$i.pkl.gz" for i in 0:0]
+    clf = pyimport("sklearn.neighbors").KNeighborsClassifier(n_neighbors=1)
+    extractor = H5FieldsExtractor(
+        instance_fields=["static_var_obj_coeffs"],
+    )
+    comp = MemorizingCutsComponent(clf=clf, extractor=extractor)
+    solver = LearningSolver(components=[comp])
+    solver.fit(data_filenames)
+    @show comp.n_features_
+    @show comp.n_targets_
+    stats = solver.optimize(
+        data_filenames[1],
+        data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
+    )
+    @test stats["Cuts: AOT"] > 0
+end