test_cons.py

from pyscipopt import Model, quicksum
import random
import pytest


def test_getConsNVars():
    n_vars = random.randint(100, 1000)
    m = Model()
    x = {}
    for i in range(n_vars):
        x[i] = m.addVar("%i" % i)

    c = m.addCons(quicksum(x[i] for i in x) <= 10)
    assert m.getConsNVars(c) == n_vars

    m.optimize()
    assert m.getConsNVars(c) == n_vars


def test_getConsVars():
    n_vars = random.randint(100, 1000)
    m = Model()
    x = {}
    for i in range(n_vars):
        x[i] = m.addVar("%i" % i)

    c = m.addCons(quicksum(x[i] for i in x) <= 1)
    assert m.getConsVars(c) == [x[i] for i in x]


def test_constraint_option_setting():
    m = Model()
    x = m.addVar()
    c = m.addCons(x >= 3)

    for option in [True, False]:
        m.setCheck(c, option)
        m.setEnforced(c, option)
        m.setRemovable(c, option)
        m.setInitial(c, option)

        assert c.isChecked() == option
        assert c.isEnforced() == option
        assert c.isRemovable() == option
        assert c.isInitial() == option


def test_cons_logical():
    m = Model()
    x1 = m.addVar(vtype="B")
    x2 = m.addVar(vtype="B")
    x3 = m.addVar(vtype="B")
    x4 = m.addVar(vtype="B")
    result1 = m.addVar(vtype="B")
    result2 = m.addVar(vtype="B")

    m.addCons(x3 == 1 - x1)
    m.addCons(x4 == 1 - x2)

    # result1 true
    m.addConsAnd([x1, x2], result1)
    m.addConsOr([x1, x2], result1)
    m.addConsXor([x1, x3], True)

    # result2 false
    m.addConsOr([x3, x4], result2)
    m.addConsAnd([x1, x3], result2)
    m.addConsXor([x1, x2], False)

    m.optimize()

    assert m.isEQ(m.getVal(result1), 1)
    assert m.isEQ(m.getVal(result2), 0)


def test_SOScons():
    m = Model()
    x = {}
    for i in range(6):
        x[i] = m.addVar(vtype="B", obj=-i)

    c1 = m.addConsSOS1([x[0]], [1])
    c2 = m.addConsSOS2([x[1]], [1])

    m.addVarSOS1(c1, x[2], 1)
    m.addVarSOS2(c2, x[3], 1)

    m.appendVarSOS1(c1, x[4])
    m.appendVarSOS2(c2, x[5])

    m.optimize()

    assert m.isEQ(m.getVal(x[0]), 0)
    assert m.isEQ(m.getVal(x[1]), 0)
    assert m.isEQ(m.getVal(x[2]), 0)
    assert m.isEQ(m.getVal(x[3]), 1)
    assert m.isEQ(m.getVal(x[4]), 1)
    assert m.isEQ(m.getVal(x[5]), 1)
    assert c1.getConshdlrName() == "SOS1"
    assert c2.getConshdlrName() == "SOS2"


def test_cons_indicator():
    m = Model()
    x = m.addVar(lb=0)
    binvar = m.addVar(vtype="B", lb=1)

    c = m.addConsIndicator(x >= 1, binvar)

    slack = m.getSlackVarIndicator(c)

    m.optimize()

    assert m.isEQ(m.getVal(slack), 0)
    assert m.isEQ(m.getVal(binvar), 1)
    assert m.isEQ(m.getVal(x), 1)
    assert c.getConshdlrName() == "indicator"


@pytest.mark.xfail(reason="addConsIndicator doesn't behave as expected when binary variable is False. See Issue #717.")
def test_cons_indicator_fail():
    m = Model()
    binvar = m.addVar(vtype="B")
    x = m.addVar(vtype="C", lb=1, ub=3)
    m.addConsIndicator(x <= 2, binvar)

    m.setObjective(x, "maximize")

    sol = m.createSol(None)
    m.setSolVal(sol, x, 3)
    m.setSolVal(sol, binvar, 0)
    assert m.checkSol(sol)  # solution should be feasible


def test_addConsCardinality():
    m = Model()
    x = {}
    for i in range(5):
        x[i] = m.addVar(ub=1, obj=-1)

    m.addConsCardinality([x[i] for i in range(5)], 3)
    m.optimize()

    assert m.isEQ(m.getVal(quicksum(x[i] for i in range(5))), 3)


def test_printCons():
    m = Model()
    x = m.addVar()
    y = m.addVar()
    c = m.addCons(x * y <= 5)

    m.printCons(c)


@pytest.mark.skip(reason="TODO: test getValsLinear()")
def test_getValsLinear():
    assert True


@pytest.mark.skip(reason="TODO: test getRowLinear()")
def test_getRowLinear():
    assert True