mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 01:18:52 -06:00
Reorganize internal solver tests
This commit is contained in:
@@ -6,7 +6,7 @@ import re
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import sys
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from io import StringIO
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from random import randint
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from typing import List, Any, Dict, Optional
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from typing import List, Any, Dict, Optional, Hashable
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from overrides import overrides
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@@ -517,3 +517,8 @@ class GurobiTestInstanceKnapsack(PyomoTestInstanceKnapsack):
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gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
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)
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return model
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@overrides
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def build_lazy_constraint(self, model: Any, violation: Hashable) -> Any:
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x = model.getVarByName("x[0]")
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return x <= 0.0
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@@ -6,8 +6,6 @@ import logging
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional
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from overrides import EnforceOverrides
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from miplearn.instance.base import Instance
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from miplearn.types import (
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LPSolveStats,
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@@ -178,7 +176,7 @@ class InternalSolver(ABC):
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pass
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@abstractmethod
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def add_constraint(self, cobj: Constraint) -> None:
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def add_constraint(self, cobj: Constraint, name: str = "") -> None:
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"""
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Adds a single constraint to the model.
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"""
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@@ -6,7 +6,7 @@ import logging
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import re
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import sys
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from io import StringIO
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from typing import Any, List, Dict, Optional
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from typing import Any, List, Dict, Optional, Hashable
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import pyomo
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from overrides import overrides
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@@ -230,7 +230,7 @@ class BasePyomoSolver(InternalSolver):
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self._pyomo_solver.update_var(var)
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@overrides
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def add_constraint(self, constraint: Any) -> Any:
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def add_constraint(self, constraint: Any, name: str = "") -> Any:
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self._pyomo_solver.add_constraint(constraint)
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self._update_constrs()
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@@ -425,3 +425,8 @@ class PyomoTestInstanceKnapsack(Instance):
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self.weights[item],
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self.prices[item],
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]
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@overrides
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def build_lazy_constraint(self, model: Any, violation: Hashable) -> Any:
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model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
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return model.cut
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@@ -2,22 +2,24 @@
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# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from miplearn.solvers.internal import InternalSolver
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from miplearn.instance.base import Instance
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from typing import Any
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from miplearn.solvers.internal import InternalSolver
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def assert_equals(left: Any, right: Any) -> None:
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assert left == right, f"{left} != {right}"
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# NOTE:
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# This file is in the main source folder, so that it can be called from Julia.
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def test_internal_solver(
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solver: InternalSolver,
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instance: Instance,
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model: Any,
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) -> None:
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def run_internal_solver_tests(solver: InternalSolver) -> None:
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run_basic_usage_tests(solver.clone())
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run_warm_start_tests(solver.clone())
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run_infeasibility_tests(solver.clone())
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def run_basic_usage_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_knapsack()
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model = instance.to_model()
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solver.set_instance(instance, model)
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assert_equals(
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solver.get_variable_names(),
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["x[0]", "x[1]", "x[2]", "x[3]"],
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@@ -64,18 +66,129 @@ def test_internal_solver(
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assert_equals(solution["x[2]"], 1.0)
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assert_equals(solution["x[3]"], 1.0)
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assert_equals(solver.get_constraint_ids(), ["eq_capacity"])
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assert_equals(
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solver.get_constraint_rhs("eq_capacity"),
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67.0,
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)
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assert_equals(
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solver.get_constraint_lhs("eq_capacity"),
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{
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"x[0]": 23.0,
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"x[1]": 26.0,
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"x[2]": 20.0,
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"x[3]": 18.0,
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},
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)
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assert_equals(solver.get_constraint_sense("eq_capacity"), "<")
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# assert_equals(solver.get_constraint_ids(), ["eq_capacity"])
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# assert_equals(
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# solver.get_constraint_rhs("eq_capacity"),
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# 67.0,
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# )
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# assert_equals(
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# solver.get_constraint_lhs("eq_capacity"),
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# {
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# "x[0]": 23.0,
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# "x[1]": 26.0,
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# "x[2]": 20.0,
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# "x[3]": 18.0,
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# },
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# )
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# assert_equals(solver.get_constraint_sense("eq_capacity"), "<")
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# if isinstance(solver, BasePyomoSolver):
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# model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
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# solver.add_constraint(model.cut)
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# elif isinstance(solver, GurobiSolver):
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# x = model.getVarByName("x[0]")
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# solver.add_constraint(x <= 0.0, name="cut")
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# else:
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# raise Exception("Illegal state")
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# # Add a brand new constraint
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cut = instance.build_lazy_constraint(model, "cut")
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assert cut is not None
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solver.add_constraint(cut, name="cut")
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# New constraint should affect solution and should be listed in
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# constraint ids
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assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
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stats = solver.solve()
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assert stats["Lower bound"] == 1030.0
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assert solver.get_sense() == "max"
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assert solver.get_constraint_sense("cut") == "<"
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assert solver.get_constraint_sense("eq_capacity") == "<"
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# Verify slacks
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assert solver.get_inequality_slacks() == {
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"cut": 0.0,
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"eq_capacity": 3.0,
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}
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# # Extract the new constraint
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# cobj = solver.extract_constraint("cut")
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#
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# # New constraint should no longer affect solution and should no longer
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# # be listed in constraint ids
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# assert solver.get_constraint_ids() == ["eq_capacity"]
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# stats = solver.solve()
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# assert stats["Lower bound"] == 1183.0
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#
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# # New constraint should not be satisfied by current solution
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# assert not solver.is_constraint_satisfied(cobj)
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#
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# # Re-add constraint
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# solver.add_constraint(cobj)
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#
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# # Constraint should affect solution again
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# assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
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# stats = solver.solve()
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# assert stats["Lower bound"] == 1030.0
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#
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# # New constraint should now be satisfied
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# assert solver.is_constraint_satisfied(cobj)
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#
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# # Relax problem and make cut into an equality constraint
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# solver.relax()
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# solver.set_constraint_sense("cut", "=")
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# stats = solver.solve()
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# assert stats["Lower bound"] is not None
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# assert round(stats["Lower bound"]) == 1030.0
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# assert round(solver.get_dual("eq_capacity")) == 0.0
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def run_warm_start_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_knapsack()
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model = instance.to_model()
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solver.set_instance(instance, model)
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solver.set_warm_start({"x[0]": 1.0, "x[1]": 0.0, "x[2]": 0.0, "x[3]": 1.0})
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stats = solver.solve(tee=True)
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if stats["Warm start value"] is not None:
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assert_equals(stats["Warm start value"], 725.0)
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solver.set_warm_start({"x[0]": 1.0, "x[1]": 1.0, "x[2]": 1.0, "x[3]": 1.0})
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stats = solver.solve(tee=True)
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assert stats["Warm start value"] is None
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solver.fix({"x[0]": 1.0, "x[1]": 0.0, "x[2]": 0.0, "x[3]": 1.0})
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stats = solver.solve(tee=True)
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assert stats["Lower bound"] == 725.0
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assert stats["Upper bound"] == 725.0
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def run_infeasibility_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_infeasible()
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solver.set_instance(instance)
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mip_stats = solver.solve()
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assert solver.is_infeasible()
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assert solver.get_solution() is None
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assert mip_stats["Upper bound"] is None
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assert mip_stats["Lower bound"] is None
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lp_stats = solver.solve_lp()
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assert solver.get_solution() is None
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assert lp_stats["LP value"] is None
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def run_iteration_cb_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_knapsack()
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solver.set_instance(instance)
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count = 0
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def custom_iteration_cb() -> bool:
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nonlocal count
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count += 1
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return count < 5
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solver.solve(iteration_cb=custom_iteration_cb)
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assert count == 5
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def assert_equals(left: Any, right: Any) -> None:
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assert left == right, f"{left} != {right}"
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