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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.solvers.tests.test_internal_solver</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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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import logging
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from io import StringIO
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from warnings import warn
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import pyomo.environ as pe
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from miplearn.solvers import _RedirectOutput
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.pyomo.base import BasePyomoSolver
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from miplearn.solvers.tests import (
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_get_knapsack_instance,
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_get_internal_solvers,
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_get_infeasible_instance,
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)
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logger = logging.getLogger(__name__)
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def test_redirect_output():
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import sys
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original_stdout = sys.stdout
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io = StringIO()
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with _RedirectOutput([io]):
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print("Hello world")
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assert sys.stdout == original_stdout
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assert io.getvalue() == "Hello world\n"
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def test_internal_solver_warm_starts():
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for solver_class in _get_internal_solvers():
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logger.info("Solver: %s" % solver_class)
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instance = _get_knapsack_instance(solver_class)
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model = instance.to_model()
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solver = solver_class()
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solver.set_instance(instance, model)
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solver.set_warm_start(
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{
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"x": {
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0: 1.0,
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1: 0.0,
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2: 0.0,
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3: 1.0,
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}
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}
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)
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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 stats["Warm start value"] == 725.0
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else:
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warn(f"{solver_class.__name__} should set warm start value")
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solver.set_warm_start(
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{
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"x": {
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0: 1.0,
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1: 1.0,
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2: 1.0,
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3: 1.0,
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}
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}
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)
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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(
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{
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"x": {
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0: 1.0,
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1: 0.0,
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2: 0.0,
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3: 1.0,
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}
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}
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)
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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 test_internal_solver():
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for solver_class in _get_internal_solvers():
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logger.info("Solver: %s" % solver_class)
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instance = _get_knapsack_instance(solver_class)
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model = instance.to_model()
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solver = solver_class()
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solver.set_instance(instance, model)
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stats = solver.solve_lp()
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assert not solver.is_infeasible()
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assert round(stats["Optimal value"], 3) == 1287.923
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assert len(stats["Log"]) > 100
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solution = solver.get_solution()
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assert round(solution["x"][0], 3) == 1.000
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assert round(solution["x"][1], 3) == 0.923
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assert round(solution["x"][2], 3) == 1.000
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assert round(solution["x"][3], 3) == 0.000
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stats = solver.solve(tee=True)
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assert not solver.is_infeasible()
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assert len(stats["Log"]) > 100
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assert stats["Lower bound"] == 1183.0
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assert stats["Upper bound"] == 1183.0
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assert stats["Sense"] == "max"
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assert isinstance(stats["Wallclock time"], float)
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solution = solver.get_solution()
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assert solution["x"][0] == 1.0
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assert solution["x"][1] == 0.0
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assert solution["x"][2] == 1.0
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assert solution["x"][3] == 1.0
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# Add a brand new constraint
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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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# 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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if isinstance(solver, GurobiSolver):
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# Extract the new constraint
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cobj = solver.extract_constraint("cut")
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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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# New constraint should not be satisfied by current solution
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assert not solver.is_constraint_satisfied(cobj)
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# Re-add constraint
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solver.add_constraint(cobj)
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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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# New constraint should now be satisfied
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assert solver.is_constraint_satisfied(cobj)
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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 round(stats["Lower bound"]) == 1030.0
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assert round(solver.get_dual("eq_capacity")) == 0.0
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def test_relax():
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for solver_class in _get_internal_solvers():
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instance = _get_knapsack_instance(solver_class)
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solver = solver_class()
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solver.set_instance(instance)
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solver.relax()
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stats = solver.solve()
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assert round(stats["Lower bound"]) == 1288.0
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|
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def test_infeasible_instance():
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for solver_class in _get_internal_solvers():
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instance = _get_infeasible_instance(solver_class)
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solver = solver_class()
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solver.set_instance(instance)
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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 stats["Upper bound"] is None
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assert stats["Lower bound"] is None
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stats = solver.solve_lp()
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assert solver.get_solution() is None
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assert stats["Optimal value"] is None
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assert solver.get_value("x", 0) is None
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|
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def test_iteration_cb():
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for solver_class in _get_internal_solvers():
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logger.info("Solver: %s" % solver_class)
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instance = _get_knapsack_instance(solver_class)
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solver = solver_class()
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solver.set_instance(instance)
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count = 0
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def custom_iteration_cb():
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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</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-functions">Functions</h2>
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<dl>
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<dt id="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance"><code class="name flex">
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<span>def <span class="ident">test_infeasible_instance</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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<details class="source">
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<summary>
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|
<span>Expand source code</span>
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</summary>
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<pre><code class="python">def test_infeasible_instance():
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for solver_class in _get_internal_solvers():
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instance = _get_infeasible_instance(solver_class)
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solver = solver_class()
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solver.set_instance(instance)
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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 stats["Upper bound"] is None
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assert stats["Lower bound"] is None
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stats = solver.solve_lp()
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assert solver.get_solution() is None
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assert stats["Optimal value"] is None
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assert solver.get_value("x", 0) is None</code></pre>
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</details>
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</dd>
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<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver"><code class="name flex">
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<span>def <span class="ident">test_internal_solver</span></span>(<span>)</span>
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</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
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<span>Expand source code</span>
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</summary>
|
|
<pre><code class="python">def test_internal_solver():
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for solver_class in _get_internal_solvers():
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logger.info("Solver: %s" % solver_class)
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instance = _get_knapsack_instance(solver_class)
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model = instance.to_model()
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solver = solver_class()
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solver.set_instance(instance, model)
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stats = solver.solve_lp()
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assert not solver.is_infeasible()
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assert round(stats["Optimal value"], 3) == 1287.923
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assert len(stats["Log"]) > 100
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solution = solver.get_solution()
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assert round(solution["x"][0], 3) == 1.000
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assert round(solution["x"][1], 3) == 0.923
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assert round(solution["x"][2], 3) == 1.000
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assert round(solution["x"][3], 3) == 0.000
|
|
|
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stats = solver.solve(tee=True)
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assert not solver.is_infeasible()
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assert len(stats["Log"]) > 100
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assert stats["Lower bound"] == 1183.0
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assert stats["Upper bound"] == 1183.0
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assert stats["Sense"] == "max"
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assert isinstance(stats["Wallclock time"], float)
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|
|
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solution = solver.get_solution()
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assert solution["x"][0] == 1.0
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assert solution["x"][1] == 0.0
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assert solution["x"][2] == 1.0
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assert solution["x"][3] == 1.0
|
|
|
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# Add a brand new constraint
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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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# 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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|
|
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if isinstance(solver, GurobiSolver):
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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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# New constraint should not be satisfied by current solution
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assert not solver.is_constraint_satisfied(cobj)
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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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# New constraint should now be satisfied
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assert solver.is_constraint_satisfied(cobj)
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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 round(stats["Lower bound"]) == 1030.0
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assert round(solver.get_dual("eq_capacity")) == 0.0</code></pre>
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|
</details>
|
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</dd>
|
|
<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts"><code class="name flex">
|
|
<span>def <span class="ident">test_internal_solver_warm_starts</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
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<span>Expand source code</span>
|
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</summary>
|
|
<pre><code class="python">def test_internal_solver_warm_starts():
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for solver_class in _get_internal_solvers():
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logger.info("Solver: %s" % solver_class)
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instance = _get_knapsack_instance(solver_class)
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model = instance.to_model()
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solver = solver_class()
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solver.set_instance(instance, model)
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solver.set_warm_start(
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{
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"x": {
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0: 1.0,
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1: 0.0,
|
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2: 0.0,
|
|
3: 1.0,
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}
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}
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)
|
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stats = solver.solve(tee=True)
|
|
if stats["Warm start value"] is not None:
|
|
assert stats["Warm start value"] == 725.0
|
|
else:
|
|
warn(f"{solver_class.__name__} should set warm start value")
|
|
|
|
solver.set_warm_start(
|
|
{
|
|
"x": {
|
|
0: 1.0,
|
|
1: 1.0,
|
|
2: 1.0,
|
|
3: 1.0,
|
|
}
|
|
}
|
|
)
|
|
stats = solver.solve(tee=True)
|
|
assert stats["Warm start value"] is None
|
|
|
|
solver.fix(
|
|
{
|
|
"x": {
|
|
0: 1.0,
|
|
1: 0.0,
|
|
2: 0.0,
|
|
3: 1.0,
|
|
}
|
|
}
|
|
)
|
|
stats = solver.solve(tee=True)
|
|
assert stats["Lower bound"] == 725.0
|
|
assert stats["Upper bound"] == 725.0</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.tests.test_internal_solver.test_iteration_cb"><code class="name flex">
|
|
<span>def <span class="ident">test_iteration_cb</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_iteration_cb():
|
|
for solver_class in _get_internal_solvers():
|
|
logger.info("Solver: %s" % solver_class)
|
|
instance = _get_knapsack_instance(solver_class)
|
|
solver = solver_class()
|
|
solver.set_instance(instance)
|
|
count = 0
|
|
|
|
def custom_iteration_cb():
|
|
nonlocal count
|
|
count += 1
|
|
return count < 5
|
|
|
|
solver.solve(iteration_cb=custom_iteration_cb)
|
|
assert count == 5</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.tests.test_internal_solver.test_redirect_output"><code class="name flex">
|
|
<span>def <span class="ident">test_redirect_output</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_redirect_output():
|
|
import sys
|
|
|
|
original_stdout = sys.stdout
|
|
io = StringIO()
|
|
with _RedirectOutput([io]):
|
|
print("Hello world")
|
|
assert sys.stdout == original_stdout
|
|
assert io.getvalue() == "Hello world\n"</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.solvers.tests.test_internal_solver.test_relax"><code class="name flex">
|
|
<span>def <span class="ident">test_relax</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_relax():
|
|
for solver_class in _get_internal_solvers():
|
|
instance = _get_knapsack_instance(solver_class)
|
|
solver = solver_class()
|
|
solver.set_instance(instance)
|
|
solver.relax()
|
|
stats = solver.solve()
|
|
assert round(stats["Lower bound"]) == 1288.0</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
</article>
|
|
<nav id="sidebar">
|
|
<h1>Index</h1>
|
|
<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
|
<li><code><a title="miplearn.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-functions">Functions</a></h3>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance" href="#miplearn.solvers.tests.test_internal_solver.test_infeasible_instance">test_infeasible_instance</a></code></li>
|
|
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver">test_internal_solver</a></code></li>
|
|
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts">test_internal_solver_warm_starts</a></code></li>
|
|
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_iteration_cb" href="#miplearn.solvers.tests.test_internal_solver.test_iteration_cb">test_iteration_cb</a></code></li>
|
|
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_redirect_output" href="#miplearn.solvers.tests.test_internal_solver.test_redirect_output">test_redirect_output</a></code></li>
|
|
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_relax" href="#miplearn.solvers.tests.test_internal_solver.test_relax">test_relax</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</nav>
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