211b624cde
* CMake has not been updated yet * bazel was compiling at least last week bazel: disable math opt facility_location.py missing some dependencies...
225 lines
8.3 KiB
Python
225 lines
8.3 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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from collections.abc import Callable
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import dataclasses
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from typing import Dict, Generic, Protocol, TypeVar, Union
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from absl.testing import absltest
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from absl.testing import parameterized
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from ortools.math_opt import sparse_containers_pb2
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from ortools.math_opt.python import linear_constraints
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from ortools.math_opt.python import model
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from ortools.math_opt.python import quadratic_constraints
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from ortools.math_opt.python import sparse_containers
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from ortools.math_opt.python import variables
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from ortools.math_opt.python.testing import compare_proto
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class SparseDoubleVectorTest(compare_proto.MathOptProtoAssertions, absltest.TestCase):
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def test_to_proto_empty(self) -> None:
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actual = sparse_containers.to_sparse_double_vector_proto({})
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self.assert_protos_equiv(
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actual, sparse_containers_pb2.SparseDoubleVectorProto()
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)
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def test_to_proto_vars(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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mod.add_binary_variable(name="y")
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z = mod.add_binary_variable(name="z")
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self.assert_protos_equiv(
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sparse_containers.to_sparse_double_vector_proto({z: 4.0, x: 1.0}),
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sparse_containers_pb2.SparseDoubleVectorProto(
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ids=[0, 2], values=[1.0, 4.0]
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),
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)
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def test_to_proto_lin_cons(self) -> None:
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mod = model.Model(name="test_model")
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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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self.assert_protos_equiv(
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sparse_containers.to_sparse_double_vector_proto({c: 4.0, d: 1.0}),
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sparse_containers_pb2.SparseDoubleVectorProto(
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ids=[0, 1], values=[4.0, 1.0]
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),
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)
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T = TypeVar("T")
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# We cannot use Callable here because we need to support a named argument.
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class ParseMap(Protocol, Generic[T]):
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def __call__(
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self,
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vec: sparse_containers_pb2.SparseDoubleVectorProto,
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mod: model.Model,
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*,
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validate: bool = True,
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) -> Dict[T, float]: ...
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@dataclasses.dataclass(frozen=True)
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class ParseMapAdapater(Generic[T]):
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add_element: Callable[[model.Model], T]
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get_element_no_validate: Callable[[model.Model, int], T]
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parse_map: ParseMap[T]
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_VAR_ADAPTER = ParseMapAdapater(
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model.Model.add_variable,
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lambda mod, id: mod.get_variable(id, validate=False),
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sparse_containers.parse_variable_map,
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)
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_LIN_CON_ADAPTER = ParseMapAdapater(
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model.Model.add_linear_constraint,
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lambda mod, id: mod.get_linear_constraint(id, validate=False),
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sparse_containers.parse_linear_constraint_map,
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)
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_QUAD_CON_ADAPTER = ParseMapAdapater(
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model.Model.add_quadratic_constraint,
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lambda mod, id: mod.get_quadratic_constraint(id, validate=False),
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sparse_containers.parse_quadratic_constraint_map,
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)
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_ADAPTERS = Union[
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ParseMapAdapater[variables.Variable],
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ParseMapAdapater[linear_constraints.LinearConstraint],
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ParseMapAdapater[quadratic_constraints.QuadraticConstraint],
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]
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@parameterized.named_parameters(
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("variable", _VAR_ADAPTER),
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("linear_constraint", _LIN_CON_ADAPTER),
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("quadratic_constraint", _QUAD_CON_ADAPTER),
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)
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class ParseVariableMapTest(
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compare_proto.MathOptProtoAssertions, parameterized.TestCase
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):
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def test_parse_map(self, adapter: _ADAPTERS) -> None:
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mod = model.Model()
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x = adapter.add_element(mod)
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adapter.add_element(mod)
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z = adapter.add_element(mod)
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actual = adapter.parse_map(
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sparse_containers_pb2.SparseDoubleVectorProto(
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ids=[0, 2], values=[1.0, 4.0]
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),
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mod,
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)
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self.assertDictEqual(actual, {x: 1.0, z: 4.0})
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def test_parse_map_empty(self, adapter: _ADAPTERS) -> None:
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mod = model.Model()
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adapter.add_element(mod)
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adapter.add_element(mod)
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actual = adapter.parse_map(sparse_containers_pb2.SparseDoubleVectorProto(), mod)
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self.assertDictEqual(actual, {})
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def test_parse_var_map_bad_var(self, adapter: _ADAPTERS) -> None:
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mod = model.Model()
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bad_proto = sparse_containers_pb2.SparseDoubleVectorProto(ids=[2], values=[4.0])
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actual = adapter.parse_map(bad_proto, mod, validate=False)
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bad_elem = adapter.get_element_no_validate(mod, 2)
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self.assertDictEqual(actual, {bad_elem: 4.0})
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with self.assertRaises(KeyError):
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adapter.parse_map(bad_proto, mod, validate=True)
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class SparseInt32VectorTest(compare_proto.MathOptProtoAssertions, absltest.TestCase):
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def test_to_proto_empty(self) -> None:
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self.assert_protos_equiv(
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sparse_containers.to_sparse_int32_vector_proto({}),
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sparse_containers_pb2.SparseInt32VectorProto(),
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)
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def test_to_proto_vars(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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mod.add_binary_variable(name="y")
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z = mod.add_binary_variable(name="z")
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self.assert_protos_equiv(
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sparse_containers.to_sparse_int32_vector_proto({z: 4, x: 1}),
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sparse_containers_pb2.SparseInt32VectorProto(ids=[0, 2], values=[1, 4]),
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)
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def test_to_proto_lin_cons(self) -> None:
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mod = model.Model(name="test_model")
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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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self.assert_protos_equiv(
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sparse_containers.to_sparse_int32_vector_proto({c: 4, d: 1}),
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sparse_containers_pb2.SparseInt32VectorProto(ids=[0, 1], values=[4, 1]),
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)
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class SparseVectorFilterTest(compare_proto.MathOptProtoAssertions, absltest.TestCase):
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def test_is_none(self) -> None:
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f = sparse_containers.SparseVectorFilter(skip_zero_values=True)
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self.assertTrue(f.skip_zero_values)
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self.assertIsNone(f.filtered_items)
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expected_proto = sparse_containers_pb2.SparseVectorFilterProto(
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skip_zero_values=True
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)
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self.assert_protos_equiv(f.to_proto(), expected_proto)
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def test_ids_is_empty(self) -> None:
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f = sparse_containers.SparseVectorFilter(filtered_items=[])
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self.assertFalse(f.skip_zero_values)
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self.assertEmpty(f.filtered_items)
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expected_proto = sparse_containers_pb2.SparseVectorFilterProto(
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filter_by_ids=True
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)
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self.assert_protos_equiv(f.to_proto(), expected_proto)
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def test_ids_are_lin_cons(self) -> None:
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mod = model.Model(name="test_model")
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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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f = sparse_containers.LinearConstraintFilter(
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skip_zero_values=True, filtered_items=[d]
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)
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self.assertTrue(f.skip_zero_values)
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self.assertSetEqual(f.filtered_items, {d})
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expected_proto = sparse_containers_pb2.SparseVectorFilterProto(
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skip_zero_values=True, filter_by_ids=True, filtered_ids=[1]
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)
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self.assert_protos_equiv(f.to_proto(), expected_proto)
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def test_ids_are_vars(self) -> None:
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mod = model.Model(name="test_model")
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w = mod.add_binary_variable(name="w")
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x = mod.add_binary_variable(name="x")
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mod.add_binary_variable(name="y")
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z = mod.add_binary_variable(name="z")
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f = sparse_containers.VariableFilter(filtered_items=(z, w, x))
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self.assertFalse(f.skip_zero_values)
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self.assertSetEqual(f.filtered_items, {w, x, z})
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expected_proto = sparse_containers_pb2.SparseVectorFilterProto(
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filter_by_ids=True, filtered_ids=[0, 1, 3]
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)
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self.assert_protos_equiv(f.to_proto(), expected_proto)
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if __name__ == "__main__":
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absltest.main()
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