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159 lines
4.7 KiB
159 lines
4.7 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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 numpy as np
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from miplearn.features import (
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FeaturesExtractor,
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InstanceFeatures,
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VariableFeatures,
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ConstraintFeatures,
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)
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.tests import assert_equals
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inf = float("inf")
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def test_knapsack() -> None:
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solver = GurobiSolver()
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instance = solver.build_test_instance_knapsack()
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model = instance.to_model()
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solver.set_instance(instance, model)
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solver.solve_lp()
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features = FeaturesExtractor().extract(instance, solver)
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assert features.variables is not None
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assert features.instance is not None
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assert_equals(
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features.variables,
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VariableFeatures(
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names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
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basis_status=["U", "B", "U", "L", "U"],
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categories=["default", "default", "default", "default", None],
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lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
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obj_coeffs=[505.0, 352.0, 458.0, 220.0, 0.0],
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reduced_costs=[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
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sa_lb_down=[-inf, -inf, -inf, -0.111111, -inf],
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sa_lb_up=[1.0, 0.923077, 1.0, 1.0, 67.0],
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sa_obj_down=[311.384615, 317.777778, 270.769231, -inf, -13.538462],
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sa_obj_up=[inf, 570.869565, inf, 243.692308, inf],
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sa_ub_down=[0.913043, 0.923077, 0.9, 0.0, 43.0],
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sa_ub_up=[2.043478, inf, 2.2, inf, 69.0],
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types=["B", "B", "B", "B", "C"],
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upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
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user_features=[
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[23.0, 505.0],
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[26.0, 352.0],
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[20.0, 458.0],
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[18.0, 220.0],
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None,
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],
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values=[1.0, 0.923077, 1.0, 0.0, 67.0],
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alvarez_2017=[
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[1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 46.051702],
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[1.0, 0.229316, 0.0, 0.076923, 1.0, 1.0, 3.532875, 5.388476],
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[1.0, 0.298371, 0.0, 0.0, 1.0, 1.0, 5.232342, 46.051702],
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[1.0, 0.143322, 0.0, 0.0, 1.0, -1.0, 46.051702, 3.16515],
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[0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0],
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],
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),
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)
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assert_equals(
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features.constraints,
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ConstraintFeatures(
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basis_status=["N"],
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categories=["eq_capacity"],
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dual_values=[13.538462],
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names=["eq_capacity"],
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lazy=[False],
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lhs=[
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[
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("x[0]", 23.0),
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("x[1]", 26.0),
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("x[2]", 20.0),
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("x[3]", 18.0),
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("z", -1.0),
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],
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],
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rhs=[0.0],
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sa_rhs_down=[-24.0],
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sa_rhs_up=[2.0],
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senses=["="],
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slacks=[0.0],
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user_features=[[0.0]],
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),
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)
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assert_equals(
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features.instance,
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InstanceFeatures(
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user_features=[67.0, 21.75],
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lazy_constraint_count=0,
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),
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)
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def test_constraint_getindex() -> None:
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cf = ConstraintFeatures(
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names=["c1", "c2", "c3"],
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rhs=[1.0, 2.0, 3.0],
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senses=["=", "<", ">"],
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lhs=[
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[
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("x1", 1.0),
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("x2", 1.0),
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],
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[
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("x2", 2.0),
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("x3", 2.0),
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],
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[
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("x3", 3.0),
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("x4", 3.0),
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],
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],
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)
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assert_equals(
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cf[[True, False, True]],
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ConstraintFeatures(
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names=["c1", "c3"],
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rhs=[1.0, 3.0],
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senses=["=", ">"],
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lhs=[
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[
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("x1", 1.0),
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("x2", 1.0),
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],
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[
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("x3", 3.0),
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("x4", 3.0),
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],
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],
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),
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)
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def test_assert_equals() -> None:
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assert_equals("hello", "hello")
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assert_equals([1.0, 2.0], [1.0, 2.0])
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assert_equals(
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np.array([1.0, 2.0]),
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np.array([1.0, 2.0]),
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)
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assert_equals(
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np.array([[1.0, 2.0], [3.0, 4.0]]),
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np.array([[1.0, 2.0], [3.0, 4.0]]),
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)
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assert_equals(
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VariableFeatures(values=np.array([1.0, 2.0])), # type: ignore
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VariableFeatures(values=np.array([1.0, 2.0])), # type: ignore
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)
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assert_equals(
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np.array([True, True]),
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[True, True],
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)
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assert_equals((1.0,), (1.0,))
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assert_equals({"x": 10}, {"x": 10})
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