def _MakeMDFProblem(self):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MDF).
Does not set the objective function... leaves that to the caller.
Returns:
the linear problem object, and the three types of variables as arrays
"""
A, b, c, y, l = self._GetPrimalVariablesAndConstants()
B = Variable('mdf')
x = y + l + [B]
lp = Model(name="MDF_PRIMAL")
cnstr_names = ["driving_force_%02d" % j for j in range(self.Nr_active)] + \
["covariance_var_ub_%02d" % j for j in range(self.Nr)] + \
["covariance_var_lb_%02d" % j for j in range(self.Nr)] + \
["log_conc_ub_%02d" % j for j in range(self.Nc)] + \
["log_conc_lb_%02d" % j for j in range(self.Nc)]
constraints = []
for j in range(A.shape[0]):
row = [A[j, i] * x[i] for i in range(A.shape[1])]
constraints.append(
Constraint(sum(row), ub=b[j, 0], name=cnstr_names[j]))
lp.add(constraints)
row = [c[i, 0] * x[i] for i in range(c.shape[0])]
lp.objective = Objective(sum(row), direction='max')
return lp, y, l, B
def _MakeMDFProblemDual(self):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MDF).
Does not set the objective function... leaves that to the caller.
Returns:
the linear problem object, and the four types of variables as arrays
"""
A, b, c, w, g, z, u = self._GetDualVariablesAndConstants()
x = w + g + z + u
lp = Model(name="MDF_DUAL")
cnstr_names = ["y_%02d" % j for j in range(self.Nr)] + \
["l_%02d" % j for j in range(self.Nc)] + \
["MDF"]
constraints = []
for i in range(A.shape[1]):
row = [A[j, i] * x[j] for j in range(A.shape[0])]
constraints.append(
Constraint(sum(row),
lb=c[i, 0],
ub=c[i, 0],
name=cnstr_names[i]))
lp.add(constraints)
row = [b[i, 0] * x[i] for i in range(A.shape[0])]
lp.objective = Objective(sum(row), direction='min')
return lp, w, g, z, u
def setUp(self):
x = Variable("x", lb=0)
y = Variable("y", lb=0)
z = Variable("z", lb=0)
c1 = Constraint(x, ub=10)
c2 = Constraint(y - 2 * z, lb=0, ub=0)
c3 = Constraint(x - z, lb=3, ub=15)
obj = Objective(x + y + z)
model = Model()
model.add([c1, c2, c3])
model.objective = obj
self.model = model
self.c1 = c1
def _GetTotalEnergyProblem(self, min_driving_force=0.0, direction='min'):
A, b, _c, y, l = self._GetPrimalVariablesAndConstants()
x = y + l + [min_driving_force]
lp = Model(name='MDF')
constraints = []
for j in range(A.shape[0]):
row = [A[j, i] * x[i] for i in range(A.shape[1])]
constraints.append(
Constraint(sum(row), ub=b[j, 0], name='row_%d' % j))
total_g0 = float(self.fluxes @ self.dG0_r_prime)
total_reaction = self.S @ self.fluxes.T
row = [total_reaction[i, 0] * x[i] for i in range(self.Nc)]
total_g = total_g0 + sum(row)
lp.add(constraints)
lp.objective = Objective(total_g, direction=direction)
return lp
class SolverTestCase(unittest.TestCase):
def setUp(self):
problem = glpk_read_cplex(TESTMODELPATH)
self.model = Model(problem=problem)
def test_create_empty_model(self):
model = Model()
self.assertEqual(glp_get_num_cols(model.problem), 0)
self.assertEqual(glp_get_num_rows(model.problem), 0)
self.assertEqual(model.name, None)
self.assertEqual(glp_get_prob_name(model.problem), None)
model = Model(name="empty_problem")
self.assertEqual(glp_get_prob_name(model.problem), "empty_problem")
def test_pickle_ability(self):
self.model.optimize()
value = self.model.objective.value
pickle_string = pickle.dumps(self.model)
from_pickle = pickle.loads(pickle_string)
from_pickle.optimize()
self.assertAlmostEqual(value, from_pickle.objective.value)
self.assertEqual([(var.lb, var.ub, var.name, var.type) for var in from_pickle.variables.values()],
[(var.lb, var.ub, var.name, var.type) for var in self.model.variables.values()])
self.assertEqual([(constr.lb, constr.ub, constr.name) for constr in from_pickle.constraints],
[(constr.lb, constr.ub, constr.name) for constr in self.model.constraints])
# def test_copy(self):
# model_copy = copy.copy(self.model)
# self.assertNotEqual(id(self.model), id(model_copy))
# self.assertEqual(id(self.model.problem), id(model_copy.problem))
#
# def test_deepcopy(self):
# model_copy = copy.deepcopy(self.model)
# self.assertNotEqual(id(self.model), id(model_copy))
# self.assertNotEqual(id(self.model.problem), id(model_copy.problem))
def test_config_gets_copied_too(self):
self.assertEquals(self.model.configuration.verbosity, 0)
self.model.configuration.verbosity = 3
model_copy = copy.copy(self.model)
self.assertEquals(model_copy.configuration.verbosity, 3)
def test_init_from_existing_problem(self):
inner_prob = self.model.problem
self.assertEqual(len(self.model.variables), glp_get_num_cols(inner_prob))
self.assertEqual(len(self.model.constraints), glp_get_num_rows(inner_prob))
self.assertEqual(self.model.variables.keys(),
[glp_get_col_name(inner_prob, i) for i in range(1, glp_get_num_cols(inner_prob) + 1)])
self.assertEqual(self.model.constraints.keys(),
[glp_get_row_name(inner_prob, j) for j in range(1, glp_get_num_rows(inner_prob) + 1)])
def test_add_variable(self):
var = Variable('x')
self.assertEqual(var.index, None)
self.model.add(var)
self.assertTrue(var in self.model.variables.values())
self.assertEqual(self.model.variables.values().count(var), 1)
self.assertEqual(var.index, glp_get_num_cols(self.model.problem))
self.assertEqual(var.name, glp_get_col_name(self.model.problem, var.index))
self.assertEqual(self.model.variables['x'].problem, var.problem)
self.assertEqual(glp_get_col_kind(self.model.problem, var.index), GLP_CV)
var = Variable('y', lb=-13)
self.model.add(var)
self.assertTrue(var in self.model.variables.values())
self.assertEqual(var.name, glp_get_col_name(self.model.problem, var.index))
self.assertEqual(glp_get_col_kind(self.model.problem, var.index), GLP_CV)
self.assertEqual(self.model.variables['x'].lb, None)
self.assertEqual(self.model.variables['x'].ub, None)
self.assertEqual(self.model.variables['y'].lb, -13)
self.assertEqual(self.model.variables['x'].ub, None)
var = Variable('x_with_ridiculously_long_variable_name_asdffffffffasdfasdfasdfasdfasdfasdfasdf')
self.model.add(var)
self.assertTrue(var in self.model.variables.values())
self.assertEqual(self.model.variables.values().count(var), 1)
var = Variable('x_with_ridiculously_long_variable_name_asdffffffffasdfasdfasdfasdfasdfasdfasdf')
self.model.add(var)
# TODO: the following tests fail because transactions are not safe yet
# self.assertRaises(Exception, self.model.update, var)
# self.assertEqual(len(self.model.variables), glp_get_num_cols(self.model.problem))
def test_add_integer_var(self):
var = Variable('int_var', lb=-13, ub=500, type='integer')
self.model.add(var)
self.model.update()
print(var.index, 1)
self.assertEqual(self.model.variables['int_var'].type, 'integer')
self.assertEqual(glp_get_col_kind(self.model.problem, var.index), GLP_IV)
self.assertEqual(self.model.variables['int_var'].ub, 500)
self.assertEqual(self.model.variables['int_var'].lb, -13)
def test_add_non_cplex_conform_variable(self):
var = Variable('12x!!@#5_3', lb=-666, ub=666)
self.assertEqual(var.index, None)
self.model.add(var)
self.assertTrue(var in self.model.variables.values())
self.assertEqual(var.name, glp_get_col_name(self.model.problem, var.index))
self.assertEqual(self.model.variables['12x!!@#5_3'].lb, -666)
self.assertEqual(self.model.variables['12x!!@#5_3'].ub, 666)
repickled = pickle.loads(pickle.dumps(self.model))
var_from_pickle = repickled.variables['12x!!@#5_3']
self.assertEqual(var_from_pickle.name, glp_get_col_name(repickled.problem, var_from_pickle.index))
def test_remove_variable(self):
var = self.model.variables.values()[0]
self.assertEqual(self.model.constraints['M_atp_c'].__str__(),
'M_atp_c: 0.0 <= -1.0*R_ACKr - 1.0*R_ADK1 + 1.0*R_ATPS4r - 1.0*R_PGK - 1.0*R_SUCOAS - 59.81*R_Biomass_Ecoli_core_w_GAM - 1.0*R_GLNS - 1.0*R_GLNabc - 1.0*R_PFK - 1.0*R_PPCK - 1.0*R_PPS + 1.0*R_PYK - 1.0*R_ATPM <= 0.0')
self.assertEqual(var.problem, self.model)
self.model.remove(var)
self.model.update()
self.assertEqual(self.model.constraints['M_atp_c'].__str__(),
'M_atp_c: 0.0 <= -1.0*R_ACKr - 1.0*R_ADK1 + 1.0*R_ATPS4r - 1.0*R_PGK - 1.0*R_SUCOAS - 1.0*R_GLNS - 1.0*R_GLNabc - 1.0*R_PFK - 1.0*R_PPCK - 1.0*R_PPS + 1.0*R_PYK - 1.0*R_ATPM <= 0.0')
self.assertNotIn(var, self.model.variables.values())
self.assertEqual(glp_find_col(self.model.problem, var.name), 0)
self.assertEqual(var.problem, None)
def test_remove_variable_str(self):
var = self.model.variables.values()[0]
self.model.remove(var.name)
self.assertNotIn(var, self.model.variables.values())
self.assertEqual(glp_find_col(self.model.problem, var.name), 0)
self.assertEqual(var.problem, None)
def test_add_constraints(self):
x = Variable('x', lb=0, ub=1, type='binary')
y = Variable('y', lb=-181133.3, ub=12000., type='continuous')
z = Variable('z', lb=0., ub=10., type='integer')
constr1 = Constraint(0.3 * x + 0.4 * y + 66. * z, lb=-100, ub=0., name='test')
constr2 = Constraint(2.333 * x + y + 3.333, ub=100.33, name='test2')
constr3 = Constraint(2.333 * x + y + z, lb=-300)
constr4 = Constraint(x, lb=-300, ub=-300)
constr5 = Constraint(3*x)
self.model.add(constr1)
self.model.add(constr2)
self.model.add(constr3)
self.model.add([constr4, constr5])
self.assertIn(constr1.name, self.model.constraints)
self.assertIn(constr2.name, self.model.constraints)
self.assertIn(constr3.name, self.model.constraints)
self.assertIn(constr4.name, self.model.constraints)
self.assertIn(constr5.name, self.model.constraints)
# constr1
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr1.index, ia, da)
coeff_dict = dict()
for i in range(1, nnz+1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 0.3, 'y': 0.4, 'z': 66.})
self.assertEqual(glp_get_row_type(self.model.problem, constr1.index), GLP_DB)
self.assertEqual(glp_get_row_lb(self.model.problem, constr1.index), -100)
self.assertEqual(glp_get_row_ub(self.model.problem, constr1.index), 0)
# constr2
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr2.index, ia, da)
coeff_dict = dict()
for i in range(1, nnz+1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr2.index), GLP_UP)
self.assertEqual(glp_get_row_lb(self.model.problem, constr2.index), -1.7976931348623157e+308)
self.assertEqual(glp_get_row_ub(self.model.problem, constr2.index), 96.997)
# constr3
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr3.index, ia, da)
coeff_dict = dict()
for i in range(1, nnz+1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1., 'z': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr3.index), GLP_LO)
self.assertEqual(glp_get_row_lb(self.model.problem, constr3.index), -300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr3.index), 1.7976931348623157e+308)
# constr4
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr4.index, ia, da)
coeff_dict = dict()
for i in range(1, nnz+1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 1})
self.assertEqual(glp_get_row_type(self.model.problem, constr4.index), GLP_FX)
self.assertEqual(glp_get_row_lb(self.model.problem, constr4.index), -300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr4.index), -300)
# constr5
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr5.index, ia, da)
coeff_dict = dict()
for i in range(1, nnz+1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 3})
self.assertEqual(glp_get_row_type(self.model.problem, constr5.index), GLP_FR)
self.assertLess(glp_get_row_lb(self.model.problem, constr5.index), -1e30)
self.assertGreater(glp_get_row_ub(self.model.problem, constr5.index), 1e30)
def test_remove_constraints(self):
x = Variable('x', type='binary')
y = Variable('y', lb=-181133.3, ub=12000., type='continuous')
z = Variable('z', lb=3, ub=3, type='integer')
constr1 = Constraint(0.3 * x + 0.4 * y + 66. * z, lb=-100, ub=0., name='test')
self.assertEqual(constr1.problem, None)
self.model.add(constr1)
self.model.update()
self.assertEqual(constr1.problem, self.model)
self.assertIn(constr1.name, self.model.constraints)
print(constr1.index)
self.model.remove(constr1.name)
self.model.update()
self.assertEqual(constr1.problem, None)
self.assertNotIn(constr1, self.model.constraints)
def test_add_nonlinear_constraint_raises(self):
x = Variable('x', type='binary')
y = Variable('y', lb=-181133.3, ub=12000., type='continuous')
z = Variable('z', lb=10, type='integer')
self.assertRaises(ValueError, Constraint, 0.3 * x + 0.4 * y ** 2 + 66. * z, lb=-100, ub=0., name='test')
def test_change_of_constraint_is_reflected_in_low_level_solver(self):
x = Variable('x', lb=-83.3, ub=1324422.)
y = Variable('y', lb=-181133.3, ub=12000.)
constraint = Constraint(0.3 * x + 0.4 * y, lb=-100, name='test')
self.assertEqual(constraint.index, None)
self.model.add(constraint)
self.assertEqual(self.model.constraints['test'].__str__(), 'test: -100 <= 0.4*y + 0.3*x')
self.assertEqual(constraint.index, 73)
z = Variable('z', lb=3, ub=10, type='integer')
self.assertEqual(z.index, None)
constraint += 77. * z
self.assertEqual(z.index, 98)
self.assertEqual(self.model.constraints['test'].__str__(), 'test: -100 <= 0.4*y + 0.3*x + 77.0*z')
print(self.model)
self.assertEqual(constraint.index, 73)
def test_change_of_objective_is_reflected_in_low_level_solver(self):
x = Variable('x', lb=-83.3, ub=1324422.)
y = Variable('y', lb=-181133.3, ub=12000.)
objective = Objective(0.3 * x + 0.4 * y, name='test', direction='max')
self.model.objective = objective
self.assertEqual(self.model.objective.__str__(), 'Maximize\n0.4*y + 0.3*x')
self.assertEqual(glp_get_obj_coef(self.model.problem, x.index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y.index), 0.4)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x.index and i != y.index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
z = Variable('z', lb=4, ub=4, type='integer')
self.model.objective += 77. * z
self.assertEqual(self.model.objective.__str__(), 'Maximize\n0.4*y + 0.3*x + 77.0*z')
self.assertEqual(glp_get_obj_coef(self.model.problem, x.index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y.index), 0.4)
self.assertEqual(glp_get_obj_coef(self.model.problem, z.index), 77.)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x.index and i != y.index and i != z.index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
@unittest.skip('Skipping for now')
def test_absolute_value_objective(self):
# TODO: implement hack mentioned in http://www.aimms.com/aimms/download/manuals/aimms3om_linearprogrammingtricks.pdf
objective = Objective(sum(abs(variable) for variable in six.itervalues(self.model.variables)), name='test',
direction='max')
print(objective)
self.assertTrue(False)
def test_change_variable_bounds(self):
inner_prob = self.model.problem
inner_problem_bounds = [(glp_get_col_lb(inner_prob, i), glp_get_col_ub(inner_prob, i)) for i in
range(1, glp_get_num_cols(inner_prob) + 1)]
bounds = [(var.lb, var.ub) for var in self.model.variables.values()]
self.assertEqual(bounds, inner_problem_bounds)
for var in self.model.variables.values():
var.lb = random.uniform(-1000, 1000)
var.ub = random.uniform(var.lb, 1000)
inner_problem_bounds_new = [(glp_get_col_lb(inner_prob, i), glp_get_col_ub(inner_prob, i)) for i in
range(1, glp_get_num_cols(inner_prob) + 1)]
bounds_new = [(var.lb, var.ub) for var in self.model.variables.values()]
self.assertNotEqual(bounds, bounds_new)
self.assertNotEqual(inner_problem_bounds, inner_problem_bounds_new)
self.assertEqual(bounds_new, inner_problem_bounds_new)
def test_change_variable_type(self):
for variable in self.model.variables:
variable.type = 'integer'
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
self.assertEqual(glp_get_col_kind(self.model.problem, i), GLP_IV)
def test_change_constraint_bounds(self):
inner_prob = self.model.problem
inner_problem_bounds = [(glp_get_row_lb(inner_prob, i), glp_get_row_ub(inner_prob, i)) for i in
range(1, glp_get_num_rows(inner_prob) + 1)]
bounds = [(constr.lb, constr.ub) for constr in self.model.constraints]
self.assertEqual(bounds, inner_problem_bounds)
for constr in self.model.constraints:
constr.lb = random.uniform(-1000, constr.ub)
constr.ub = random.uniform(constr.lb, 1000)
inner_problem_bounds_new = [(glp_get_row_lb(inner_prob, i), glp_get_row_ub(inner_prob, i)) for i in
range(1, glp_get_num_rows(inner_prob) + 1)]
bounds_new = [(constr.lb, constr.ub) for constr in self.model.constraints]
self.assertNotEqual(bounds, bounds_new)
self.assertNotEqual(inner_problem_bounds, inner_problem_bounds_new)
self.assertEqual(bounds_new, inner_problem_bounds_new)
def test_initial_objective(self):
self.assertEqual(self.model.objective.expression.__str__(), '1.0*R_Biomass_Ecoli_core_w_GAM')
def test_optimize(self):
self.model.optimize()
self.assertEqual(self.model.status, 'optimal')
self.assertAlmostEqual(self.model.objective.value, 0.8739215069684303)
def test_optimize_milp(self):
problem = glpk_read_cplex(TESTMILPMODELPATH)
milp_model = Model(problem=problem)
milp_model.optimize()
self.assertEqual(milp_model.status, 'optimal')
self.assertAlmostEqual(milp_model.objective.value, 122.5)
for variable in milp_model.variables:
if variable.type == 'integer':
self.assertEqual(variable.primal % 1, 0)
def test_change_objective(self):
"""Test that all different kinds of linear objective specification work."""
print(self.model.variables.values()[0:2])
v1, v2 = self.model.variables.values()[0:2]
self.model.objective = Objective(1. * v1 + 1. * v2)
self.assertEqual(self.model.objective.__str__(), 'Maximize\n1.0*R_PGK + 1.0*R_Biomass_Ecoli_core_w_GAM')
self.model.objective = Objective(v1 + v2)
self.assertEqual(self.model.objective.__str__(), 'Maximize\n1.0*R_PGK + 1.0*R_Biomass_Ecoli_core_w_GAM')
def test_number_objective(self):
self.model.objective = Objective(0.)
self.assertEqual(self.model.objective.__str__(), 'Maximize\n0')
obj_coeff = list()
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
obj_coeff.append(glp_get_obj_coef(self.model.problem, i))
self.assertEqual(set(obj_coeff), {0.})
def test_raise_on_non_linear_objective(self):
"""Test that an exception is raised when a non-linear objective is added to the model."""
v1, v2 = self.model.variables.values()[0:2]
self.assertRaises(ValueError, Objective, v1*v2)
def test_iadd_objective(self):
v2, v3 = self.model.variables.values()[1:3]
self.model.objective += 2. * v2 - 3. * v3
obj_coeff = list()
for i in range(len(self.model.variables)):
obj_coeff.append(glp_get_obj_coef(self.model.problem, i))
self.assertEqual(obj_coeff,
[0.0, 1.0, 2.0, -3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0]
)
def test_imul_objective(self):
self.model.objective *= 2.
obj_coeff = list()
for i in range(len(self.model.variables)):
obj_coeff.append(glp_get_obj_coef(self.model.problem, i))
self.assertEqual(obj_coeff,
[0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0]
)
def test_set_copied_objective(self):
obj_copy = copy.copy(self.model.objective)
self.model.objective = obj_copy
self.assertEqual(self.model.objective.__str__(), 'Maximize\n1.0*R_Biomass_Ecoli_core_w_GAM')
def test_timeout(self):
self.model.configuration.timeout = 0
status = self.model.optimize()
self.assertEqual(status, 'time_limit')
def test_set_linear_objective_term(self):
self.model._set_linear_objective_term(self.model.variables.R_TPI, 666.)
self.assertEqual(glp_get_obj_coef(self.model.problem, self.model.variables.R_TPI.index), 666.)
def test_instantiating_model_with_non_glpk_problem_raises(self):
self.assertRaises(TypeError, Model, problem='Chicken soup')
def test__set_coefficients_low_level(self):
constraint = self.model.constraints.M_atp_c
constraint._set_coefficients_low_level({self.model.variables.R_Biomass_Ecoli_core_w_GAM: 666.})
num_cols = glp_get_num_cols(self.model.problem)
ia = intArray(num_cols + 1)
da = doubleArray(num_cols + 1)
index = constraint.index
num = glp_get_mat_row(self.model.problem, index, ia, da)
for i in range(1, num +1):
col_name = glp_get_col_name(self.model.problem, ia[i])
if col_name == 'R_Biomass_Ecoli_core_w_GAM':
self.assertEqual(da[i], 666.)
def test_primal_values(self):
self.model.optimize()
for k, v in self.model.primal_values.items():
self.assertEquals(v, self.model.variables[k].primal)
def test_reduced_costs(self):
self.model.optimize()
for k, v in self.model.reduced_costs.items():
self.assertEquals(v, self.model.variables[k].dual)
def test_dual_values(self):
self.model.optimize()
for k, v in self.model.dual_values.items():
self.assertEquals(v, self.model.constraints[k].primal)
def test_shadow_prices(self):
self.model.optimize()
for k, v in self.model.shadow_prices.items():
self.assertEquals(v, self.model.constraints[k].dual)
def test_clone_solver(self):
self.assertEquals(self.model.configuration.verbosity, 0)
self.model.configuration.verbosity = 3
cloned_model = Model.clone(self.model)
self.assertEquals(cloned_model.configuration.verbosity, 3)
self.assertEquals(len(cloned_model.variables), len(self.model.variables))
self.assertEquals(len(cloned_model.constraints), len(self.model.constraints))
