def test_qp_non_convex(self):
problem = cplex.Cplex()
problem.read(NONCONVEX_QP_PATH)
model = Model(problem=problem)
self.assertEqual(len(model.variables), 31)
self.assertEqual(len(model.constraints), 1)
for constraint in model.constraints:
self.assertTrue(
constraint.is_Linear,
"%s should be linear" % (str(constraint.expression)))
self.assertFalse(
constraint.is_Quadratic, "%s should not be quadratic" %
(str(constraint.expression)))
self.assertTrue(model.objective.is_Quadratic,
"objective should be quadratic")
self.assertFalse(model.objective.is_Linear,
"objective should not be linear")
model.configuration.solution_target = "convex"
self.assertRaises(CplexSolverError, model.optimize)
model.configuration.solution_target = "global"
model.optimize()
self.assertAlmostEqual(model.objective.value, 2441.999999971)
def test_get_primal(self):
self.assertEqual(self.var.primal, None)
problem = cplex.Cplex()
problem.read(TESTMODELPATH)
model = Model(problem=problem)
model.optimize()
self.assertEqual(model.status, 'optimal')
self.assertAlmostEqual(model.objective.value, 0.8739215069684305)
print([var.primal for var in model.variables])
for i, j in zip([var.primal for var in model.variables],
[0.8739215069684306, -16.023526143167608, 16.023526143167604, -14.71613956874283,
14.71613956874283, 4.959984944574658, 4.959984944574657, 4.959984944574658,
3.1162689467973905e-29, 2.926716099010601e-29, 0.0, 0.0, -6.112235045340358e-30,
-5.6659435396316186e-30, 0.0, -4.922925402711085e-29, 0.0, 9.282532599166613, 0.0,
6.00724957535033, 6.007249575350331, 6.00724957535033, -5.064375661482091,
1.7581774441067828, 0.0, 7.477381962160285, 0.0, 0.22346172933182767, 45.514009774517454,
8.39, 0.0, 6.007249575350331, 0.0, -4.541857463865631, 0.0, 5.064375661482091, 0.0, 0.0,
2.504309470368734, 0.0, 0.0, -22.809833310204958, 22.809833310204958, 7.477381962160285,
7.477381962160285, 1.1814980932459636, 1.496983757261567, -0.0, 0.0, 4.860861146496815,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.064375661482091, 0.0, 5.064375661482091, 0.0, 0.0,
1.496983757261567, 10.000000000000002, -10.0, 0.0, 0.0, 0.0, 0.0, 0.0, -29.175827135565804,
43.598985311997524, 29.175827135565804, 0.0, 0.0, 0.0, -1.2332237321082153e-29,
3.2148950476847613, 38.53460965051542, 5.064375661482091, 0.0, -1.2812714099825612e-29,
-1.1331887079263237e-29, 17.530865429786694, 0.0, 0.0, 0.0, 4.765319193197458,
-4.765319193197457, 21.79949265599876, -21.79949265599876, -3.2148950476847613, 0.0,
-2.281503094067127, 2.6784818505075303, 0.0]):
self.assertAlmostEqual(i, j)
def test_netlib(netlib_tar_path=os.path.join(
os.path.dirname(__file__), 'data/netlib_lp_problems.tar.gz')):
"""
Test netlib with glpk interface
"""
tar = tarfile.open(netlib_tar_path)
model_paths_in_tar = glob.fnmatch.filter(tar.getnames(), '*.SIF')
for model_path_in_tar in model_paths_in_tar:
print(model_path_in_tar)
netlib_id = os.path.basename(model_path_in_tar).replace(
'.SIF', '')
# TODO: get the following problems to work
# E226 seems to be a MPS related problem, see http://lists.gnu.org/archive/html/bug-glpk/2003-01/msg00003.html
if netlib_id in ('AGG', 'E226', 'SCSD6', 'BLEND', 'DFL001',
'FORPLAN', 'GFRD-PNC', 'SIERRA'):
# def test_skip(netlib_id):
# raise SkipTest('Skipping netlib problem %s ...' % netlib_id)
# test_skip(netlib_id)
# class TestWeirdNetlibProblems(unittest.TestCase):
# @unittest.skip('Skipping netlib problem')
# def test_fail():
# pass
continue
# TODO: For now, test only models that are covered by the final netlib results
else:
if netlib_id not in THE_FINAL_NETLIB_RESULTS.keys():
continue
fhandle = tar.extractfile(model_path_in_tar)
problem = read_netlib_sif_cplex(fhandle)
model = Model(problem=problem)
model.configuration.presolve = True
model.configuration.verbosity = 3
func = partial(check_dimensions, problem, model)
func.description = "test_netlib_check_dimensions_%s (%s)" % (
netlib_id, os.path.basename(str(__file__)))
yield func
model.optimize()
if model.status == 'optimal':
model_objval = model.objective.value
else:
raise Exception(
'No optimal solution found for netlib model %s' %
netlib_id)
func = partial(check_objval, problem, model_objval)
func.description = "test_netlib_check_objective_value_%s (%s)" % (
netlib_id, os.path.basename(str(__file__)))
yield func
func = partial(
check_objval_against_the_final_netlib_results,
netlib_id, model_objval)
func.description = "test_netlib_check_objective_value__against_the_final_netlib_results_%s (%s)" % (
netlib_id, os.path.basename(str(__file__)))
yield func
def test_changing_variable_names_is_reflected_in_the_solver(self):
model = Model(problem=cplex.Cplex(TESTMODELPATH))
for i, variable in enumerate(model.variables):
old_name = variable.name
variable.name = "var" + str(i)
self.assertEqual(variable.name, "var" + str(i))
self.assertEqual(model.problem.variables.get_names(i), "var" + str(i))
self.assertIn("var" + str(i), model._variables_to_constraints_mapping)
self.assertNotIn(old_name, model._variables_to_constraints_mapping)
def load_problem(mps_file):
prob_tmp_file = tempfile.mktemp(suffix='.mps')
with open(prob_tmp_file, 'wb') as tmp_handle:
f = gzip.open(mps_file, 'rb')
tmp_handle.write(f.read())
f.close()
problem = cplex.Cplex()
problem.read(prob_tmp_file)
model = Model(problem=problem)
model.configuration.presolve = True
model.configuration.timeout = 60 * 9
return problem, model
def test_cplex_setting_bounds(self):
problem = cplex.Cplex()
problem.read(TESTMODELPATH)
model = Model(problem=problem)
var = model.variables[0]
var.lb = 1
self.assertEqual(var.lb, 1)
model.update()
self.assertEqual(model.problem.variables.get_lower_bounds(var.name), 1)
var.ub = 2
self.assertEqual(var.ub, 2)
model.update()
self.assertEqual(model.problem.variables.get_upper_bounds(var.name), 2)
def test_qp_convex(self):
problem = cplex.Cplex()
problem.read(CONVEX_QP_PATH)
model = Model(problem=problem)
self.assertEqual(len(model.variables), 651)
self.assertEqual(len(model.constraints), 501)
for constraint in model.constraints:
self.assertTrue(constraint.is_Linear, "%s should be linear" % (str(constraint.expression)))
self.assertFalse(constraint.is_Quadratic, "%s should not be quadratic" % (str(constraint.expression)))
self.assertTrue(model.objective.is_Quadratic, "objective should be quadratic")
self.assertFalse(model.objective.is_Linear, "objective should not be linear")
model.optimize()
self.assertAlmostEqual(model.objective.value, 32.2291282)
def setUp(self):
self.model = Model()
self.x1 = Variable("x1", lb=0)
self.x2 = Variable("x2", lb=0)
self.c1 = Constraint(self.x1 + self.x2, lb=1)
self.model.add([self.x1, self.x2, self.c1])
def setUp(self):
self.model = Model()
self.configuration = self.model.configuration
def setUp(self):
problem = cplex.Cplex()
problem.read(TESTMODELPATH)
self.model = Model(problem=problem)
self.obj = self.model.objective
def test_changing_variable_names_is_reflected_in_the_solver(self):
model = Model(problem=cplex.Cplex(TESTMODELPATH))
for i, variable in enumerate(model.variables):
variable.name = "var" + str(i)
self.assertEqual(variable.name, "var" + str(i))
self.assertEqual(model.problem.variables.get_names(i), "var" + str(i))
