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test_model.py
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import os
import pytest
from mip import (
CBC,
GUROBI,
Model,
MAXIMIZE,
MINIMIZE,
OptimizationStatus,
INTEGER,
CONTINUOUS,
BINARY,
)
TOL = 1e-4
SOLVERS = [CBC]
if "GUROBI_HOME" in os.environ:
SOLVERS += [GUROBI]
# Overall Optimization Tests
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("var_type", (CONTINUOUS, INTEGER))
def test_minimize_single_continuous_or_integer_variable_with_default_bounds(
solver, var_type
):
m = Model(solver_name=solver, sense=MINIMIZE)
x = m.add_var(name="x", var_type=var_type, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(x.x) < TOL
assert abs(m.objective_value) < TOL
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("var_type", (CONTINUOUS, INTEGER))
def test_maximize_single_continuous_or_integer_variable_with_default_bounds(
solver, var_type
):
m = Model(solver_name=solver, sense=MAXIMIZE)
x = m.add_var(name="x", var_type=var_type, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.UNBOUNDED
assert x.x is None
assert m.objective_value is None
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize(
"sense,status,xvalue,objvalue",
[
(MAXIMIZE, OptimizationStatus.OPTIMAL, 1, 1), # implicit upper bound 1
(MINIMIZE, OptimizationStatus.OPTIMAL, 0, 0), # implicit lower bound 0
],
)
def test_single_binary_variable_with_default_bounds(
solver, sense: str, status, xvalue, objvalue
):
m = Model(solver_name=solver, sense=sense)
x = m.add_var(name="x", var_type=BINARY, obj=1)
m.optimize()
# check result
assert m.status == status
assert abs(x.x - xvalue) < TOL
assert abs(m.objective_value - objvalue) < TOL
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("var_type", (CONTINUOUS, INTEGER))
@pytest.mark.parametrize(
"lb,ub,min_obj,max_obj",
(
(0, 0, 0, 0), # fixed to 0
(2, 2, 2, 2), # fixed to positive
(-2, -2, -2, -2), # fixed to negative
(1, 2, 1, 2), # positive range
(-3, 2, -3, 2), # negative range
(-4, 5, -4, 5), # range from positive to negative
),
)
def test_single_continuous_or_integer_variable_with_different_bounds(
solver, var_type, lb, ub, min_obj, max_obj
):
# Minimum Case
m = Model(solver_name=solver, sense=MINIMIZE)
m.add_var(name="x", var_type=var_type, lb=lb, ub=ub, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - min_obj) < TOL
# Maximum Case
m = Model(solver_name=solver, sense=MAXIMIZE)
m.add_var(name="x", var_type=var_type, lb=lb, ub=ub, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - max_obj) < TOL
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize(
"lb,ub,min_obj,max_obj",
(
(0, 1, 0, 1), # regular case
(0, 0, 0, 0), # fixed to 0
(1, 1, 1, 1), # fixed to 1
),
)
def test_binary_variable_with_different_bounds(solver, lb, ub, min_obj, max_obj):
# Minimum Case
m = Model(solver_name=solver, sense=MINIMIZE)
m.add_var(name="x", var_type=BINARY, lb=lb, ub=ub, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - min_obj) < TOL
# Maximum Case
m = Model(solver_name=solver, sense=MAXIMIZE)
m.add_var(name="x", var_type=BINARY, lb=lb, ub=ub, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - max_obj) < TOL
@pytest.mark.parametrize("solver", SOLVERS)
def test_binary_variable_illegal_bounds(solver):
m = Model(solver_name=solver)
# Illegal lower bound
with pytest.raises(ValueError):
m.add_var("x", lb=-1, var_type=BINARY)
# Illegal upper bound
with pytest.raises(ValueError):
m.add_var("x", ub=2, var_type=BINARY)
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("sense", (MINIMIZE, MAXIMIZE))
@pytest.mark.parametrize(
"var_type,lb,ub",
(
(CONTINUOUS, 3.5, 2),
(INTEGER, 5, 4),
(BINARY, 1, 0),
),
)
def test_contradictory_variable_bounds(solver, sense: str, var_type: str, lb, ub):
m = Model(solver_name=solver, sense=sense)
m.add_var(name="x", var_type=var_type, lb=lb, ub=ub, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.INFEASIBLE
@pytest.mark.parametrize("solver", SOLVERS)
def test_float_bounds_for_integer_variable(solver):
# Minimum Case
m = Model(solver_name=solver, sense=MINIMIZE)
m.add_var(name="x", var_type=INTEGER, lb=-1.5, ub=3.5, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - (-1)) < TOL
# Maximum Case
m = Model(solver_name=solver, sense=MAXIMIZE)
m.add_var(name="x", var_type=INTEGER, lb=-1.5, ub=3.5, obj=1)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - 3) < TOL
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("sense", (MINIMIZE, MAXIMIZE))
def test_single_default_variable_with_nothing_to_do(solver, sense):
m = Model(solver_name=solver, sense=sense)
m.add_var(name="x")
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value) < TOL
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("var_type", (CONTINUOUS, INTEGER, BINARY))
@pytest.mark.parametrize("obj", (1.2, 2))
def test_single_variable_with_different_non_zero_objectives(solver, var_type, obj):
# Maximize
m = Model(solver_name=solver, sense=MAXIMIZE)
x = m.add_var(name="x", var_type=var_type, lb=0, ub=1, obj=obj)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - obj) < TOL
assert abs(x.x - 1.0) < TOL
# Minimize with negative
m = Model(solver_name=solver, sense=MINIMIZE)
x = m.add_var(name="x", var_type=var_type, lb=0, ub=1, obj=-obj)
m.optimize()
# check result
assert m.status == OptimizationStatus.OPTIMAL
assert abs(m.objective_value - (-obj)) < TOL
assert abs(x.x - 1.0) < TOL