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.expression -
(0.4 * y + 0.3 * x)).expand() - 0, 0)
self.assertEqual(self.model.objective.direction, "max")
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.expression -
(0.4 * y + 0.3 * x + 77.0 * z)).expand() - 0, 0)
self.assertEqual(self.model.objective.direction, "max")
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)
def test_constraint_set_problem_to_None_caches_the_latest_expression_from_solver_instance(
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.model.add(constraint)
z = Variable('z', lb=2, ub=5, type='integer')
constraint += 77. * z
self.model.remove(constraint)
self.assertEqual(constraint.__str__(),
'test: -100 <= 0.4*y + 0.3*x + 77.0*z')
def setUp(self):
self.var1 = var1 = Variable("var1", lb=0, ub=1, type="continuous")
self.var2 = var2 = Variable("var2", lb=0, ub=1, type="continuous")
self.const1 = const1 = Constraint(0.5 * var1, lb=0, ub=1, name="c1")
self.const2 = const2 = Constraint(0.1 * var2 + 0.4 * var1, name="c2")
self.model = model = Model()
model.add([var1, var2])
model.add([const1, const2])
model.objective = Objective(var1 + var2)
model.update()
self.json_string = json.dumps(model.to_json())
def _GetDualVariablesAndConstants(self):
# Define and apply the constraints on the concentrations
ln_conc_lb, ln_conc_ub = self._MakeLnConcentratonBounds()
# Create the driving force variable and add the relevant constraints
A, b, c = self._MakeDrivingForceConstraints(ln_conc_lb, ln_conc_ub)
w = [Variable('w%d' % i, lb=0) for i in range(self.Nr_active)]
g = [Variable('g%d' % i, lb=0) for i in range(2 * self.Nr)]
z = [Variable('z%d' % i, lb=0) for i in range(self.Nc)]
u = [Variable('u%d' % i, lb=0) for i in range(self.Nc)]
return A, b, c, w, g, z, u
def _GetPrimalVariablesAndConstants(self):
# Define and apply the constraints on the concentrations
ln_conc_lb, ln_conc_ub = self._MakeLnConcentratonBounds()
# Create the driving force variable and add the relevant constraints
A, b, c = self._MakeDrivingForceConstraints(ln_conc_lb, ln_conc_ub)
# the dG'0 covariance eigenvariables
y = [Variable("y%d" % i) for i in range(self.Nr)]
# ln-concentration variables
l = [Variable("l%d" % i) for i in range(self.Nc)]
return A, b, c, y, l
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 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_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"].expression -
(0.4 * y + 0.3 * x)).expand(), 0)
self.assertEqual(self.model.constraints["test"].lb, -100)
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"].expression -
(0.4 * y + 0.3 * x + 77.0 * z)).expand(), 0)
self.assertEqual(self.model.constraints["test"].lb, -100)
self.assertEqual(constraint._index, 73)
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_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)