5bf70b691f
* CMake has not been updated yet * bazel was compiling at least last week bazel: disable math opt facility_location.py missing some dependencies...
187 lines
7.8 KiB
Python
187 lines
7.8 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2025 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import datetime
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from google.protobuf import duration_pb2
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from absl.testing import absltest
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from ortools.math_opt import model_parameters_pb2
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from ortools.math_opt import solution_pb2
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from ortools.math_opt import sparse_containers_pb2
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from ortools.math_opt.python import model
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from ortools.math_opt.python import model_parameters
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from ortools.math_opt.python import solution
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from ortools.math_opt.python import sparse_containers
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from ortools.math_opt.python.testing import compare_proto
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class ModelParametersTest(compare_proto.MathOptProtoAssertions, absltest.TestCase):
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def test_solution_hint_round_trip(self) -> None:
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mod = model.Model(name="test_model")
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x = mod.add_binary_variable(name="x")
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y = mod.add_binary_variable(name="y")
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c = mod.add_linear_constraint(lb=0.0, ub=1.0, name="c")
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d = mod.add_linear_constraint(lb=0.0, ub=1.0, name="d")
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hint = model_parameters.SolutionHint(
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variable_values={x: 2.0, y: 3.0}, dual_values={c: 4.0, d: 5.0}
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)
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hint_round_trip = model_parameters.parse_solution_hint(hint.to_proto(), mod)
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self.assertDictEqual(hint_round_trip.variable_values, hint.variable_values)
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self.assertDictEqual(hint_round_trip.dual_values, hint.dual_values)
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def test_objective_parameters_empty_round_trip(self) -> None:
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params = model_parameters.ObjectiveParameters()
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proto = model_parameters_pb2.ObjectiveParametersProto()
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self.assert_protos_equiv(params.to_proto(), proto)
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self.assertEqual(model_parameters.parse_objective_parameters(proto), params)
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def test_objective_parameters_full_round_trip(self) -> None:
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params = model_parameters.ObjectiveParameters(
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objective_degradation_absolute_tolerance=4.1,
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objective_degradation_relative_tolerance=4.2,
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time_limit=datetime.timedelta(minutes=1),
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)
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proto = model_parameters_pb2.ObjectiveParametersProto(
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objective_degradation_absolute_tolerance=4.1,
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objective_degradation_relative_tolerance=4.2,
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time_limit=duration_pb2.Duration(seconds=60),
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)
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self.assert_protos_equiv(params.to_proto(), proto)
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self.assertEqual(model_parameters.parse_objective_parameters(proto), params)
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def test_model_parameters_to_proto_no_basis(self) -> None:
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mod = model.Model(name="test_model")
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x = mod.add_binary_variable(name="x")
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y = mod.add_binary_variable(name="y")
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c = mod.add_linear_constraint(lb=0.0, ub=1.0, name="c")
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# Ensure q and c have different ids.
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mod.add_quadratic_constraint()
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q = mod.add_quadratic_constraint(name="q")
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params = model_parameters.ModelSolveParameters()
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params.variable_values_filter = sparse_containers.SparseVectorFilter(
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filtered_items=(y,)
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)
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params.reduced_costs_filter = sparse_containers.SparseVectorFilter(
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skip_zero_values=True
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)
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params.dual_values_filter = sparse_containers.SparseVectorFilter(
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filtered_items=(c,)
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)
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params.quadratic_dual_values_filter = sparse_containers.SparseVectorFilter(
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filtered_items=(q,)
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)
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params.solution_hints.append(
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model_parameters.SolutionHint(
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variable_values={x: 1.0, y: 1.0}, dual_values={c: 3.0}
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)
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)
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params.solution_hints.append(
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model_parameters.SolutionHint(variable_values={y: 0.0})
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)
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params.branching_priorities[y] = 2
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actual = params.to_proto()
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expected = model_parameters_pb2.ModelSolveParametersProto(
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variable_values_filter=sparse_containers_pb2.SparseVectorFilterProto(
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filter_by_ids=True, filtered_ids=(1,)
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),
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reduced_costs_filter=sparse_containers_pb2.SparseVectorFilterProto(
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skip_zero_values=True
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),
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dual_values_filter=sparse_containers_pb2.SparseVectorFilterProto(
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filter_by_ids=True, filtered_ids=(0,)
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),
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quadratic_dual_values_filter=sparse_containers_pb2.SparseVectorFilterProto(
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filter_by_ids=True, filtered_ids=(1,)
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),
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branching_priorities=sparse_containers_pb2.SparseInt32VectorProto(
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ids=[1], values=[2]
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),
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)
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h1 = expected.solution_hints.add()
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h1.variable_values.ids[:] = [0, 1]
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h1.variable_values.values[:] = [1.0, 1.0]
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h1.dual_values.ids[:] = [0]
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h1.dual_values.values[:] = [3]
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h2 = expected.solution_hints.add()
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h2.variable_values.ids.append(1)
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h2.variable_values.values.append(0.0)
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self.assert_protos_equiv(actual, expected)
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def test_model_parameters_to_proto_with_basis(self) -> None:
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mod = model.Model(name="test_model")
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x = mod.add_binary_variable(name="x")
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params = model_parameters.ModelSolveParameters()
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params.initial_basis = solution.Basis()
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params.initial_basis.variable_status[x] = solution.BasisStatus.AT_UPPER_BOUND
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actual = params.to_proto()
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expected = model_parameters_pb2.ModelSolveParametersProto()
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expected.initial_basis.variable_status.ids.append(0)
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expected.initial_basis.variable_status.values.append(
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solution_pb2.BASIS_STATUS_AT_UPPER_BOUND
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)
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self.assert_protos_equiv(expected, actual)
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def test_model_parameters_to_proto_with_objective_params(self) -> None:
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mod = model.Model()
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aux1 = mod.add_auxiliary_objective(priority=1)
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mod.add_auxiliary_objective(priority=2)
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aux3 = mod.add_auxiliary_objective(priority=3)
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def make_param(abs_tol: float) -> model_parameters.ObjectiveParameters:
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return model_parameters.ObjectiveParameters(
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objective_degradation_absolute_tolerance=abs_tol
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)
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def make_proto_param(
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abs_tol: float,
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) -> model_parameters_pb2.ObjectiveParametersProto:
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return model_parameters_pb2.ObjectiveParametersProto(
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objective_degradation_absolute_tolerance=abs_tol
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)
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model_params = model_parameters.ModelSolveParameters(
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objective_parameters={
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mod.objective: make_param(0.1),
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aux1: make_param(0.2),
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aux3: make_param(0.3),
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}
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)
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expected = model_parameters_pb2.ModelSolveParametersProto(
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primary_objective_parameters=make_proto_param(0.1),
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auxiliary_objective_parameters={
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0: make_proto_param(0.2),
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2: make_proto_param(0.3),
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},
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)
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self.assert_protos_equiv(model_params.to_proto(), expected)
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def test_model_parameters_to_proto_with_lazy_constraints(self) -> None:
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mod = model.Model()
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c0 = mod.add_linear_constraint()
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mod.add_linear_constraint()
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c2 = mod.add_linear_constraint()
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model_params = model_parameters.ModelSolveParameters(
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lazy_linear_constraints={c0, c2}
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)
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expected = model_parameters_pb2.ModelSolveParametersProto(
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lazy_linear_constraint_ids=[0, 2]
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)
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self.assert_protos_equiv(model_params.to_proto(), expected)
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if __name__ == "__main__":
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absltest.main()
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