MIPLearn v0.3

2025-12-10 11:28:51 -06:00 · 2023-06-08 11:25:39 -05:00
parent 6cc253a903
commit 1ea989d48a
172 changed files with 10495 additions and 24812 deletions
--- a/tests/test_solvers.py
+++ b/tests/test_solvers.py
@@ -0,0 +1,181 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from tempfile import NamedTemporaryFile
+
+import numpy as np
+import pytest
+
+from miplearn.h5 import H5File
+from miplearn.problems.setcover import (
+    SetCoverData,
+    build_setcover_model_gurobipy,
+    build_setcover_model_pyomo,
+)
+from miplearn.solvers.abstract import AbstractModel
+
+inf = float("inf")
+
+
+@pytest.fixture
+def data() -> SetCoverData:
+    return SetCoverData(
+        costs=np.array([5, 10, 12, 6, 8]),
+        incidence_matrix=np.array(
+            [
+                [1, 0, 0, 1, 0],
+                [1, 1, 0, 0, 0],
+                [0, 0, 1, 1, 1],
+            ],
+        ),
+    )
+
+
+def test_gurobi(data: SetCoverData) -> None:
+    _test_solver(build_setcover_model_gurobipy, data)
+
+
+def test_pyomo_persistent(data: SetCoverData) -> None:
+    _test_solver(lambda d: build_setcover_model_pyomo(d, "gurobi_persistent"), data)
+
+
+def _test_solver(build_model, data):
+    _test_extract(build_model(data))
+    _test_add_constr(build_model(data))
+    _test_fix_vars(build_model(data))
+    _test_infeasible(build_model(data))
+
+
+def _test_extract(model):
+    with NamedTemporaryFile() as tempfile:
+        with H5File(tempfile.name) as h5:
+
+            def test_scalar(key, expected_value):
+                actual_value = h5.get_scalar(key)
+                assert actual_value is not None
+                assert actual_value == expected_value
+
+            def test_array(key, expected_value):
+                actual_value = h5.get_array(key)
+                assert actual_value is not None
+                assert actual_value.tolist() == expected_value
+
+            def test_sparse(key, expected_value):
+                actual_value = h5.get_sparse(key)
+                assert actual_value is not None
+                assert actual_value.todense().tolist() == expected_value
+
+            model.extract_after_load(h5)
+            test_sparse(
+                "static_constr_lhs",
+                [
+                    [1.0, 0.0, 0.0, 1.0, 0.0],
+                    [1.0, 1.0, 0.0, 0.0, 0.0],
+                    [0.0, 0.0, 1.0, 1.0, 1.0],
+                ],
+            )
+            test_array("static_constr_names", [b"eqs[0]", b"eqs[1]", b"eqs[2]"])
+            test_array("static_constr_rhs", [1, 1, 1])
+            test_array("static_constr_sense", [b">", b">", b">"])
+            test_scalar("static_obj_offset", 0.0)
+            test_scalar("static_sense", "min")
+            test_array("static_var_lower_bounds", [0.0, 0.0, 0.0, 0.0, 0.0])
+            test_array(
+                "static_var_names",
+                [
+                    b"x[0]",
+                    b"x[1]",
+                    b"x[2]",
+                    b"x[3]",
+                    b"x[4]",
+                ],
+            )
+            test_array("static_var_obj_coeffs", [5.0, 10.0, 12.0, 6.0, 8.0])
+            test_array("static_var_types", [b"B", b"B", b"B", b"B", b"B"])
+            test_array("static_var_upper_bounds", [1.0, 1.0, 1.0, 1.0, 1.0])
+
+            relaxed = model.relax()
+            relaxed.optimize()
+            relaxed.extract_after_lp(h5)
+            test_array("lp_constr_dual_values", [0, 5, 6])
+            test_array("lp_constr_slacks", [1, 0, 0])
+            test_scalar("lp_obj_value", 11.0)
+            test_array("lp_var_reduced_costs", [0.0, 5.0, 6.0, 0.0, 2.0])
+            test_array("lp_var_values", [1.0, 0.0, 0.0, 1.0, 0.0])
+            if model._supports_basis_status:
+                test_array("lp_var_basis_status", [b"B", b"L", b"L", b"B", b"L"])
+                test_array("lp_constr_basis_status", [b"B", b"N", b"N"])
+            if model._supports_sensitivity_analysis:
+                test_array("lp_constr_sa_rhs_up", [2, 1, 1])
+                test_array("lp_constr_sa_rhs_down", [-inf, 0, 0])
+                test_array("lp_var_sa_obj_up", [10.0, inf, inf, 8.0, inf])
+                test_array("lp_var_sa_obj_down", [0.0, 5.0, 6.0, 0.0, 6.0])
+                test_array("lp_var_sa_ub_up", [inf, inf, inf, inf, inf])
+                test_array("lp_var_sa_ub_down", [1.0, 0.0, 0.0, 1.0, 0.0])
+                test_array("lp_var_sa_lb_up", [1.0, 1.0, 1.0, 1.0, 1.0])
+                test_array("lp_var_sa_lb_down", [-inf, 0.0, 0.0, -inf, 0.0])
+            lp_wallclock_time = h5.get_scalar("lp_wallclock_time")
+            assert lp_wallclock_time is not None
+            assert lp_wallclock_time >= 0
+
+            model.optimize()
+            model.extract_after_mip(h5)
+            test_array("mip_constr_slacks", [1, 0, 0])
+            test_array("mip_var_values", [1.0, 0.0, 0.0, 1.0, 0.0])
+            test_scalar("mip_gap", 0)
+            test_scalar("mip_obj_bound", 11.0)
+            test_scalar("mip_obj_value", 11.0)
+            mip_wallclock_time = h5.get_scalar("mip_wallclock_time")
+            assert mip_wallclock_time is not None
+            assert mip_wallclock_time > 0
+            if model._supports_node_count:
+                count = h5.get_scalar("mip_node_count")
+                assert count is not None
+                assert count >= 0
+            if model._supports_solution_pool:
+                pool_var_values = h5.get_array("pool_var_values")
+                pool_obj_values = h5.get_array("pool_obj_values")
+                assert pool_var_values is not None
+                assert pool_obj_values is not None
+                assert len(pool_obj_values.shape) == 1
+                n_sols = len(pool_obj_values)
+                assert pool_var_values.shape == (n_sols, 5)
+
+
+def _test_add_constr(model: AbstractModel):
+    with NamedTemporaryFile() as tempfile:
+        with H5File(tempfile.name) as h5:
+            model.add_constrs(
+                np.array([b"x[2]", b"x[3]"], dtype="S"),
+                np.array([[0, 1], [1, 0]]),
+                np.array(["=", "="], dtype="S"),
+                np.array([0, 0]),
+            )
+            model.optimize()
+            model.extract_after_mip(h5)
+            assert h5.get_array("mip_var_values").tolist() == [1, 0, 0, 0, 1]
+
+
+def _test_fix_vars(model: AbstractModel):
+    with NamedTemporaryFile() as tempfile:
+        with H5File(tempfile.name) as h5:
+            model.fix_variables(
+                var_names=np.array([b"x[2]", b"x[3]"], dtype="S"),
+                var_values=np.array([0, 0]),
+            )
+            model.optimize()
+            model.extract_after_mip(h5)
+            assert h5.get_array("mip_var_values").tolist() == [1, 0, 0, 0, 1]
+
+
+def _test_infeasible(model: AbstractModel):
+    with NamedTemporaryFile() as tempfile:
+        with H5File(tempfile.name) as h5:
+            model.fix_variables(
+                var_names=np.array([b"x[0]", b"x[3]"], dtype="S"),
+                var_values=np.array([0, 0]),
+            )
+            model.optimize()
+            model.extract_after_mip(h5)
+            assert h5.get_array("mip_var_values") is None