def _get_base_model(self):
model = aml.ConcreteModel()
model.x = aml.Var()
model.y = aml.Var()
model.d1 = aml.Param(mutable=True, initialize=0.0)
model.d2 = aml.Param(mutable=True, initialize=0.0)
model.d3 = aml.Param(mutable=True, initialize=0.0)
model.cost = aml.Expression([1, 2])
model.cost[1].expr = model.x
model.cost[2].expr = model.d1 * model.y
model.o = aml.Objective(expr=model.cost[1] + model.cost[2])
model.c1 = aml.Constraint(expr=model.x >= 0)
model.c2 = aml.Constraint(expr=model.y * model.d2 >= model.d3)
model.varstage = VariableStageAnnotation()
model.varstage.declare(model.x, 1)
model.varstage.declare(model.y, 2)
model.stagecost = StageCostAnnotation()
model.stagecost.declare(model.cost[1], 1)
model.stagecost.declare(model.cost[2], 2)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.d1,
distribution=TableDistribution([0.0, 1.0]))
model.stochdata.declare(model.d2,
distribution=TableDistribution([0.0, 1.0]))
model.stochdata.declare(model.d3,
distribution=TableDistribution([0.0, 1.0]))
return model
def _get_baa99_sp(self):
model = baa99_basemodel.model.clone()
model.varstage = VariableStageAnnotation()
model.varstage.declare(model.x1, 1)
model.varstage.declare(model.x2, 1)
model.stagecost = StageCostAnnotation()
model.stagecost.declare(model.FirstStageCost, 1)
model.stagecost.declare(model.SecondStageCost, 2)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.d1_rhs,
distribution=TableDistribution(
model.d1_rhs_table))
model.stochdata.declare(model.d2_rhs,
distribution=TableDistribution(
model.d2_rhs_table))
return EmbeddedSP(model)
def test_stoch_range_constraint(self):
model = self._get_base_model()
model.q = aml.Param(mutable=True, initialize=0.0)
model.stochdata.declare(model.q,
distribution=TableDistribution([0.0, 1.0]))
model.c3 = aml.Constraint(expr=aml.inequality(model.q, model.y, 0))
sp = EmbeddedSP(model)
with self.assertRaises(ValueError) as cm:
pyomo.pysp.convert.smps.convert_embedded(self.tmpdir, 'test', sp)
self.assertEqual(
str(cm.exception),
("Cannot output embedded SP representation for component "
"'c3'. The embedded SMPS writer does not yet handle range "
"constraints that have stochastic data."))
def test_stoch_data_nontrivial_expression_constraint2(self):
model = self._get_base_model()
model.q = aml.Param(mutable=True, initialize=0.0)
model.stochdata.declare(model.q,
distribution=TableDistribution([0.0, 1.0]))
model.c2._body = (model.d2 + model.q) * model.y
sp = EmbeddedSP(model)
with self.assertRaises(ValueError) as cm:
pyomo.pysp.convert.smps.convert_embedded(self.tmpdir, 'test', sp)
self.assertEqual(
str(cm.exception),
("Cannot output embedded SP representation for component "
"'c2'. The embedded SMPS writer does not yet handle the "
"case where multiple stochastic data components appear "
"in an expression that defines a single variable's "
"coefficient. The coefficient for variable 'y' involves "
"stochastic parameters: ['d2', 'q']"))
def test_stoch_constraint_body_constant(self):
model = self._get_base_model()
model.q = aml.Param(mutable=True, initialize=0.0)
model.stochdata.declare(model.q,
distribution=TableDistribution([0.0, 1.0]))
model.c2._body = model.d2 * model.y + model.q
sp = EmbeddedSP(model)
with self.assertRaises(ValueError) as cm:
pyomo.pysp.convert.smps.convert_embedded(self.tmpdir, 'test', sp)
self.assertEqual(
str(cm.exception),
("Cannot output embedded SP representation for component "
"'c2'. The embedded SMPS writer does not yet handle the "
"case where a stochastic data appears in the body of a "
"constraint expression that must be moved to the bounds. "
"The constraint must be written so that the stochastic "
"element 'q' is a simple bound or a simple variable "
"coefficient."))
def test_TableDistrubtion(self):
with self.assertRaises(ValueError):
TableDistribution([])
with self.assertRaises(ValueError):
TableDistribution([1],weights=[])
with self.assertRaises(ValueError):
TableDistribution([1],weights=[0.5])
d = TableDistribution([1,2,3])
v = d.sample()
self.assertTrue(v in d.values)
d = TableDistribution([1,2], weights=[0.5, 0.5])
v = d.sample()
self.assertTrue(v in d.values)
d = TableDistribution([1,2], weights=[1, 0])
v = d.sample()
self.assertEqual(v, 1)
def create_embedded():
model = aml.ConcreteModel()
model.d1 = aml.Param(mutable=True, initialize=0)
model.d2 = aml.Param(mutable=True, initialize=0)
model.d3 = aml.Param(mutable=True, initialize=0)
model.d4 = aml.Param(mutable=True, initialize=0)
# first stage
model.x = aml.Var(bounds=(0, 10))
# first stage derived
model.y = aml.Expression(expr=model.x + 1)
model.fx = aml.Var()
# second stage
model.z = aml.Var(bounds=(-10, 10))
# second stage derived
model.q = aml.Expression(expr=model.z**2)
model.fz = aml.Var()
model.r = aml.Var()
# stage costs
model.StageCost = aml.Expression([1, 2])
model.StageCost.add(1, model.fx)
model.StageCost.add(2, -model.fz + model.r + model.d1)
model.o = aml.Objective(expr=aml.sum_product(model.StageCost))
model.c_first_stage = aml.Constraint(expr=model.x >= 0)
# test our handling of intermediate variables that
# are created by Piecewise but can not necessarily
# be declared on the scenario tree
model.p_first_stage = aml.Piecewise(model.fx,
model.x,
pw_pts=[0., 2., 5., 7., 10.],
pw_constr_type='EQ',
pw_repn='INC',
f_rule=[10., 10., 9., 10., 10.],
force_pw=True)
model.c_second_stage = aml.Constraint(
expr=model.x + model.r * model.d2 >= -100)
model.cL_second_stage = aml.Constraint(expr=model.d3 >= -model.r)
model.cU_second_stage = aml.Constraint(expr=model.r <= 0)
# exercise more of the code by making this an indexed
# block
model.p_second_stage = aml.Piecewise([1],
model.fz,
model.z,
pw_pts=[-10, -5., 0., 5., 10.],
pw_constr_type='EQ',
pw_repn='INC',
f_rule=[0., 0., -1., model.d4, 1.],
force_pw=True)
# annotate the model
model.varstage = VariableStageAnnotation()
# first stage
model.varstage.declare(model.x, 1)
model.varstage.declare(model.y, 1, derived=True)
model.varstage.declare(model.fx, 1, derived=True)
model.varstage.declare(model.p_first_stage, 1, derived=True)
# second stage
model.varstage.declare(model.z, 2)
model.varstage.declare(model.q, 2, derived=True)
model.varstage.declare(model.fz, 2, derived=True)
model.varstage.declare(model.r, 2, derived=True)
model.varstage.declare(model.p_second_stage, 2, derived=True)
model.stagecost = StageCostAnnotation()
for i in [1, 2]:
model.stagecost.declare(model.StageCost[i], i)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.d1,
distribution=TableDistribution([0.0, 1.0, 2.0]))
model.stochdata.declare(model.d2,
distribution=TableDistribution([0.0, 1.0, 2.0]))
model.stochdata.declare(model.d3,
distribution=TableDistribution([0.0, 1.0, 2.0]))
model.stochdata.declare(model.d4,
distribution=TableDistribution([0.0, 1.0, 2.0]))
return EmbeddedSP(model)
def test_TableDistrubtion(self):
with self.assertRaises(ValueError):
TableDistribution([])
with self.assertRaises(ValueError):
TableDistribution([1], weights=[])
with self.assertRaises(ValueError):
TableDistribution([1], weights=[0.5])
d = TableDistribution([1, 2, 3])
v = d.sample()
self.assertTrue(v in d.values)
d = TableDistribution([1, 2], weights=[0.5, 0.5])
v = d.sample()
self.assertTrue(v in d.values)
d = TableDistribution([1, 2], weights=[1, 0])
v = d.sample()
self.assertEqual(v, 1)