def test_bad_distribution_constraint(self):
model = self._get_base_model()
del model.stochdata
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.d2,
distribution=UniformDistribution(0.0, 1.0))
sp = EmbeddedSP(model)
with self.assertRaises(ValueError) as cm:
pyomo.pysp.convert.smps.convert_embedded(self.tmpdir, 'test', sp)
self.assertEqual(
str(cm.exception),
("Invalid distribution type 'UniformDistribution' for stochastic "
"parameter 'd2'. The embedded SMPS writer currently "
"only supports discrete table distributions of type "
"pyomo.pysp.embeddedsp.TableDistribution."))
def test_UniformDistrubtion(self):
d = UniformDistribution(1,10)
v = d.sample()
self.assertTrue(1 <= v <= 10)
# and stochastic parameters.
# (2) Obtains a solution using the deterministic
# approximation, where stochastic parameters
# are set to the expected value of their
# distribution.
# (3) Generates an "external" SP by sampling from
# the EmbeddedSP and uses DDSIP solver interface.
# The DDSIP solver requires explicit annotations
# that describe the locations of the stochastic data.
# These annotations are automatically added when
# the SP is created from an EmbeddedSP object.
# (4) The EmbeddedSP object is then re-sampled to
# creating a test set that is used to evaluate
# the solutions from steps (2) and (3).
d_dist = UniformDistribution(0, 100)
train_N = 150
test_N = 150
cplex = SolverFactory("cplex")
saa_solver = SPSolverFactory("ddsip") # "ef"
#
# Define the reference model
#
model = ConcreteModel()
model.x = Var(domain=Integers)
model.y = Var(domain=Integers)
model.t = Var(domain=Integers)
model.c = 1.0
model.b = 1.5
model.h = 0.1
def test_UniformDistrubtion(self):
d = UniformDistribution(1, 10)
v = d.sample()
self.assertTrue(1 <= v <= 10)
def test_compute_time_stage(self):
model = ConcreteModel()
model.x = Var()
model.y = Var([0, 1])
model.z = Var()
model.p = Param(mutable=True)
model.cost = Expression([0, 1])
model.cost[0] = model.x + model.y[0]
model.cost[1] = model.p + model.y[1] + model.y[0] * model.p
model.o = Objective(expr=model.cost[0] + model.cost[1])
model.c = ConstraintList()
model.c.add(model.x >= 1) # 1
model.c.add(model.y[0] >= 1) # 2
model.c.add(model.p * model.y[0] >= 1) # 3
model.c.add(model.y[0] >= model.p) # 4
model.c.add(model.p <= model.y[1]) # 5
model.c.add(model.y[1] <= 1) # 6
model.c.add(model.x >= model.p) # 7
model.c.add(model.z == 1) # 8
model.varstage = VariableStageAnnotation()
model.varstage.declare(model.x, 1)
model.varstage.declare(model.y[0], 1, derived=True)
model.varstage.declare(model.y[1], 2)
model.varstage.declare(model.z, 2, derived=True)
model.stagecost = StageCostAnnotation()
model.stagecost.declare(model.cost[0], 1)
model.stagecost.declare(model.cost[1], 2)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.p,
distribution=UniformDistribution(0, 1))
sp = EmbeddedSP(model)
#
# check variables
#
self.assertEqual(sp.compute_time_stage(model.x), min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.x, derived_last_stage=True),
min(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.y[0]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.y[0], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.y[1]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.y[1], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.z), max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.z, derived_last_stage=True),
max(sp.time_stages))
#
# check constraints
#
self.assertEqual(sp.compute_time_stage(model.c[1]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[1], derived_last_stage=True),
min(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[2]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[2], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[3]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[3], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[4]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[4], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[5]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[5], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[6]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[6], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[7]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[7], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[8]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[8], derived_last_stage=True),
max(sp.time_stages))
#
# check objectives and expressions
#
self.assertEqual(sp.compute_time_stage(model.cost[0]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.cost[0], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.cost[1]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.cost[1], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.o), max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.o, derived_last_stage=True),
max(sp.time_stages))