Add first model feature (constraint RHS)

tests/fixtures/knapsack.py

@@ -0,0 +1,45 @@
+#  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 BasePyomoSolver, GurobiSolver, InternalSolver, Instance
+from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
+from miplearn.solvers.learning import LearningSolver
+from tests.solvers import _is_subclass_or_instance
+
+
+def get_test_pyomo_instances():
+    instances = [
+        KnapsackInstance(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        ),
+        KnapsackInstance(
+            weights=[25.0, 30.0, 22.0, 18.0],
+            prices=[500.0, 365.0, 420.0, 150.0],
+            capacity=70.0,
+        ),
+    ]
+    models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for i in range(len(instances)):
+        solver.solve(instances[i], models[i])
+    return instances, models
+
+
+def get_knapsack_instance(solver: InternalSolver) -> Instance:
+    if _is_subclass_or_instance(solver, BasePyomoSolver):
+        return KnapsackInstance(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        )
+    elif _is_subclass_or_instance(solver, GurobiSolver):
+        return GurobiKnapsackInstance(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        )
+    else:
+        assert False