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182 lines
6.9 KiB
182 lines
6.9 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, 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 tempfile import NamedTemporaryFile
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import numpy as np
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import pytest
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from miplearn.h5 import H5File
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from miplearn.problems.setcover import (
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SetCoverData,
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build_setcover_model_gurobipy,
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build_setcover_model_pyomo,
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)
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from miplearn.solvers.abstract import AbstractModel
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inf = float("inf")
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@pytest.fixture
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def data() -> SetCoverData:
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return SetCoverData(
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costs=np.array([5, 10, 12, 6, 8]),
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incidence_matrix=np.array(
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[
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[1, 0, 0, 1, 0],
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[1, 1, 0, 0, 0],
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[0, 0, 1, 1, 1],
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],
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),
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)
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def test_gurobi(data: SetCoverData) -> None:
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_test_solver(build_setcover_model_gurobipy, data)
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def test_pyomo_persistent(data: SetCoverData) -> None:
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_test_solver(lambda d: build_setcover_model_pyomo(d, "gurobi_persistent"), data)
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def _test_solver(build_model, data):
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_test_extract(build_model(data))
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_test_add_constr(build_model(data))
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_test_fix_vars(build_model(data))
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_test_infeasible(build_model(data))
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def _test_extract(model):
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with NamedTemporaryFile() as tempfile:
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with H5File(tempfile.name) as h5:
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def test_scalar(key, expected_value):
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actual_value = h5.get_scalar(key)
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assert actual_value is not None
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assert actual_value == expected_value
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def test_array(key, expected_value):
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actual_value = h5.get_array(key)
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assert actual_value is not None
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assert actual_value.tolist() == expected_value
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def test_sparse(key, expected_value):
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actual_value = h5.get_sparse(key)
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assert actual_value is not None
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assert actual_value.todense().tolist() == expected_value
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model.extract_after_load(h5)
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test_sparse(
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"static_constr_lhs",
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[
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[1.0, 0.0, 0.0, 1.0, 0.0],
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[1.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 1.0, 1.0, 1.0],
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],
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)
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test_array("static_constr_names", [b"eqs[0]", b"eqs[1]", b"eqs[2]"])
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test_array("static_constr_rhs", [1, 1, 1])
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test_array("static_constr_sense", [b">", b">", b">"])
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test_scalar("static_obj_offset", 0.0)
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test_scalar("static_sense", "min")
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test_array("static_var_lower_bounds", [0.0, 0.0, 0.0, 0.0, 0.0])
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test_array(
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"static_var_names",
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[
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b"x[0]",
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b"x[1]",
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b"x[2]",
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b"x[3]",
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b"x[4]",
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],
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)
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test_array("static_var_obj_coeffs", [5.0, 10.0, 12.0, 6.0, 8.0])
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test_array("static_var_types", [b"B", b"B", b"B", b"B", b"B"])
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test_array("static_var_upper_bounds", [1.0, 1.0, 1.0, 1.0, 1.0])
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relaxed = model.relax()
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relaxed.optimize()
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relaxed.extract_after_lp(h5)
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test_array("lp_constr_dual_values", [0, 5, 6])
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test_array("lp_constr_slacks", [1, 0, 0])
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test_scalar("lp_obj_value", 11.0)
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test_array("lp_var_reduced_costs", [0.0, 5.0, 6.0, 0.0, 2.0])
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test_array("lp_var_values", [1.0, 0.0, 0.0, 1.0, 0.0])
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if model._supports_basis_status:
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test_array("lp_var_basis_status", [b"B", b"L", b"L", b"B", b"L"])
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test_array("lp_constr_basis_status", [b"B", b"N", b"N"])
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if model._supports_sensitivity_analysis:
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test_array("lp_constr_sa_rhs_up", [2, 1, 1])
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test_array("lp_constr_sa_rhs_down", [-inf, 0, 0])
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test_array("lp_var_sa_obj_up", [10.0, inf, inf, 8.0, inf])
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test_array("lp_var_sa_obj_down", [0.0, 5.0, 6.0, 0.0, 6.0])
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test_array("lp_var_sa_ub_up", [inf, inf, inf, inf, inf])
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test_array("lp_var_sa_ub_down", [1.0, 0.0, 0.0, 1.0, 0.0])
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test_array("lp_var_sa_lb_up", [1.0, 1.0, 1.0, 1.0, 1.0])
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test_array("lp_var_sa_lb_down", [-inf, 0.0, 0.0, -inf, 0.0])
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lp_wallclock_time = h5.get_scalar("lp_wallclock_time")
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assert lp_wallclock_time is not None
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assert lp_wallclock_time >= 0
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model.optimize()
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model.extract_after_mip(h5)
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test_array("mip_constr_slacks", [1, 0, 0])
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test_array("mip_var_values", [1.0, 0.0, 0.0, 1.0, 0.0])
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test_scalar("mip_gap", 0)
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test_scalar("mip_obj_bound", 11.0)
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test_scalar("mip_obj_value", 11.0)
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mip_wallclock_time = h5.get_scalar("mip_wallclock_time")
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assert mip_wallclock_time is not None
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assert mip_wallclock_time > 0
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if model._supports_node_count:
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count = h5.get_scalar("mip_node_count")
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assert count is not None
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assert count >= 0
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if model._supports_solution_pool:
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pool_var_values = h5.get_array("pool_var_values")
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pool_obj_values = h5.get_array("pool_obj_values")
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assert pool_var_values is not None
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assert pool_obj_values is not None
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assert len(pool_obj_values.shape) == 1
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n_sols = len(pool_obj_values)
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assert pool_var_values.shape == (n_sols, 5)
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def _test_add_constr(model: AbstractModel):
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with NamedTemporaryFile() as tempfile:
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with H5File(tempfile.name) as h5:
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model.add_constrs(
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np.array([b"x[2]", b"x[3]"], dtype="S"),
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np.array([[0, 1], [1, 0]]),
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np.array(["=", "="], dtype="S"),
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np.array([0, 0]),
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)
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model.optimize()
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model.extract_after_mip(h5)
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assert h5.get_array("mip_var_values").tolist() == [1, 0, 0, 0, 1]
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def _test_fix_vars(model: AbstractModel):
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with NamedTemporaryFile() as tempfile:
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with H5File(tempfile.name) as h5:
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model.fix_variables(
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var_names=np.array([b"x[2]", b"x[3]"], dtype="S"),
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var_values=np.array([0, 0]),
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)
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model.optimize()
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model.extract_after_mip(h5)
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assert h5.get_array("mip_var_values").tolist() == [1, 0, 0, 0, 1]
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def _test_infeasible(model: AbstractModel):
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with NamedTemporaryFile() as tempfile:
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with H5File(tempfile.name) as h5:
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model.fix_variables(
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var_names=np.array([b"x[0]", b"x[3]"], dtype="S"),
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var_values=np.array([0, 0]),
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)
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model.optimize()
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model.extract_after_mip(h5)
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assert h5.get_array("mip_var_values") is None
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