def _generate_base_scenario_tree(self, model, variable_stage_assignments):
stage_cost_annotation = locate_annotations(
model,
PySP_StageCostAnnotation,
max_allowed=1)
if len(stage_cost_annotation) == 0:
raise ValueError("Reference model is missing stage cost "
"annotation: %s" % (PySP_StageCostAnnotation.__name__))
else:
assert len(stage_cost_annotation) == 1
stage_cost_annotation = stage_cost_annotation[0][1]
stage_cost_assignments = ComponentMap(
stage_cost_annotation.expand_entries())
stage1_cost = None
stage2_cost = None
for cdata, stagenum in stage_cost_assignments.items():
if stagenum == 1:
stage1_cost = cdata
elif stagenum == 2:
stage2_cost = cdata
if stage1_cost is None:
raise ValueError("Missing stage cost annotation for time stage: 1")
if stage2_cost is None:
raise ValueError("Missing stage cost annotation for time stage: 2")
assert stage1_cost != stage2_cost
#
# Create a dummy 1-scenario scenario tree
#
stm = CreateAbstractScenarioTreeModel()
stm.Stages.add('Stage1')
stm.Stages.add('Stage2')
stm.Nodes.add('RootNode')
stm.Nodes.add('LeafNode')
stm.Scenarios.add('ReferenceScenario')
stm = stm.create_instance()
stm.NodeStage['RootNode'] = 'Stage1'
stm.ConditionalProbability['RootNode'] = 1.0
stm.NodeStage['LeafNode'] = 'Stage2'
stm.Children['RootNode'].add('LeafNode')
stm.Children['LeafNode'].clear()
stm.ConditionalProbability['LeafNode'] = 1.0
stm.ScenarioLeafNode['ReferenceScenario'] = 'LeafNode'
stm.StageCost['Stage1'] = stage1_cost.name
stm.StageCost['Stage2'] = stage2_cost.name
for var, (stagenum, derived) in variable_stage_assignments.items():
stagelabel = 'Stage'+str(stagenum)
if not derived:
stm.StageVariables[stagelabel].add(var.name)
else:
stm.StageDerivedVariables[second_stage].add(var.name)
scenario_tree = ScenarioTree(scenariotreeinstance=stm)
scenario_tree.linkInInstances(
{'ReferenceScenario': self.reference_model})
return scenario_tree
def _generate_base_scenario_tree(self, model, variable_stage_assignments):
stage_cost_annotation = locate_annotations(model,
PySP_StageCostAnnotation,
max_allowed=1)
if len(stage_cost_annotation) == 0:
raise ValueError("Reference model is missing stage cost "
"annotation: %s" %
(PySP_StageCostAnnotation.__name__))
else:
assert len(stage_cost_annotation) == 1
stage_cost_annotation = stage_cost_annotation[0][1]
stage_cost_assignments = ComponentMap(
stage_cost_annotation.expand_entries())
stage1_cost = None
stage2_cost = None
for cdata, stagenum in stage_cost_assignments.items():
if stagenum == 1:
stage1_cost = cdata
elif stagenum == 2:
stage2_cost = cdata
if stage1_cost is None:
raise ValueError("Missing stage cost annotation for time stage: 1")
if stage2_cost is None:
raise ValueError("Missing stage cost annotation for time stage: 2")
assert stage1_cost != stage2_cost
#
# Create a dummy 1-scenario scenario tree
#
stm = CreateAbstractScenarioTreeModel()
stm.Stages.add('Stage1')
stm.Stages.add('Stage2')
stm.Nodes.add('RootNode')
stm.Nodes.add('LeafNode')
stm.Scenarios.add('ReferenceScenario')
stm = stm.create_instance()
stm.NodeStage['RootNode'] = 'Stage1'
stm.ConditionalProbability['RootNode'] = 1.0
stm.NodeStage['LeafNode'] = 'Stage2'
stm.Children['RootNode'].add('LeafNode')
stm.Children['LeafNode'].clear()
stm.ConditionalProbability['LeafNode'] = 1.0
stm.ScenarioLeafNode['ReferenceScenario'] = 'LeafNode'
stm.StageCost['Stage1'] = stage1_cost.name
stm.StageCost['Stage2'] = stage2_cost.name
for var, (stagenum, derived) in variable_stage_assignments.items():
stagelabel = 'Stage' + str(stagenum)
if not derived:
stm.StageVariables[stagelabel].add(var.name)
else:
stm.StageDerivedVariables[second_stage].add(var.name)
scenario_tree = ScenarioTree(scenariotreeinstance=stm)
scenario_tree.linkInInstances(
{'ReferenceScenario': self.reference_model})
return scenario_tree
def _map_variable_stages(self, model):
variable_stage_annotation = locate_annotations(
model,
PySP_VariableStageAnnotation,
max_allowed=1)
if len(variable_stage_annotation) == 0:
raise ValueError(
"Reference model is missing variable stage "
"annotation: %s" % (PySP_VariableStageAnnotation.__name__))
else:
assert len(variable_stage_annotation) == 1
variable_stage_annotation = variable_stage_annotation[0][1]
variable_stage_assignments = ComponentMap(
variable_stage_annotation.expand_entries(
expand_containers=False))
if len(variable_stage_assignments) == 0:
raise ValueError("At least one variable stage assignment is required.")
min_stagenumber = min(variable_stage_assignments.values(),
key=lambda x: x[0])[0]
max_stagenumber = max(variable_stage_assignments.values(),
key=lambda x: x[0])[0]
if max_stagenumber > 2:
for vardata, (stagenum, derived) in variable_stage_assignments.items():
if stagenum > 2:
raise ValueError(
"Implicit stochastic programs must be two-stage, "
"but variable with name '%s' has been annotated with "
"stage number: %s" % (vardata.name, stagenum))
stage_to_variables_map = {}
stage_to_variables_map[1] = []
stage_to_variables_map[2] = []
for vardata in model.component_data_objects(
Var,
active=True,
descend_into=True,
sort=SortComponents.alphabetizeComponentAndIndex):
stagenumber, derived = variable_stage_assignments.get(vardata, (2, False))
if (stagenumber != 1) and (stagenumber != 2):
raise ValueError("Invalid stage annotation for variable with "
"name '%s'. Stage assignment must be 1 or 2. "
"Current value: %s"
% (vardata.name, stagenumber))
if (stagenumber == 1):
stage_to_variables_map[1].append((vardata, derived))
else:
assert stagenumber == 2
stage_to_variables_map[2].append((vardata, derived))
variable_to_stage_map = ComponentMap()
for stagenum, stagevars in stage_to_variables_map.items():
for vardata, derived in stagevars:
variable_to_stage_map[vardata] = (stagenum, derived)
return (stage_to_variables_map,
variable_to_stage_map,
variable_stage_assignments)
def _map_variable_stages(model):
variable_stage_annotation = locate_annotations(model,
VariableStageAnnotation,
max_allowed=1)
if len(variable_stage_annotation) == 0:
raise ValueError("Reference model is missing variable stage "
"annotation: %s" % (VariableStageAnnotation.__name__))
else:
assert len(variable_stage_annotation) == 1
variable_stage_annotation = variable_stage_annotation[0][1]
variable_stage_assignments = ComponentMap(
variable_stage_annotation.expand_entries())
if len(variable_stage_assignments) == 0:
raise ValueError("At least one variable stage assignment "
"is required.")
min_stagenumber = min(variable_stage_assignments.values(),
key=lambda x: x[0])[0]
max_stagenumber = max(variable_stage_assignments.values(),
key=lambda x: x[0])[0]
if max_stagenumber > 2:
for var, (stagenum, derived) in \
variable_stage_assignments.items():
if stagenum > 2:
raise ValueError(
"Embedded stochastic programs must be two-stage "
"(for now), but variable with name '%s' has been "
"annotated with stage number: %s" % (var.name, stagenum))
stage_to_variables_map = {}
stage_to_variables_map[1] = []
stage_to_variables_map[2] = []
for var in model.component_data_objects(
Var,
active=True,
descend_into=True,
sort=SortComponents.alphabetizeComponentAndIndex):
stagenumber, derived = \
variable_stage_assignments.get(var, (2, False))
if (stagenumber != 1) and (stagenumber != 2):
raise ValueError("Invalid stage annotation for variable with "
"name '%s'. Stage assignment must be 1 or 2. "
"Current value: %s" % (var.name, stagenumber))
if (stagenumber == 1):
stage_to_variables_map[1].append((var, derived))
else:
assert stagenumber == 2
stage_to_variables_map[2].append((var, derived))
variable_to_stage_map = ComponentMap()
for stagenum, stagevars in stage_to_variables_map.items():
for var, derived in stagevars:
variable_to_stage_map[var] = (stagenum, derived)
return (stage_to_variables_map, variable_to_stage_map,
variable_stage_assignments)
def __init__(self, reference_model):
self.reference_model = None
self.objective = None
self.time_stages = None
self.stage_to_variables_map = {}
self.variable_to_stage_map = {}
# the set of stochastic data objects
# (possibly mapped to some distribution)
self.stochastic_data = None
# maps between variables and objectives
self.variable_to_objectives_map = ComponentMap()
self.objective_to_variables_map = ComponentMap()
# maps between variables and constraints
self.variable_to_constraints_map = ComponentMap()
self.constraint_to_variables_map = ComponentMap()
# maps between stochastic data and objectives
self.stochastic_data_to_objectives_map = ComponentMap()
self.objective_to_stochastic_data_map = ComponentMap()
# maps between stochastic data and constraints
self.stochastic_data_to_constraints_map = ComponentMap()
self.constraint_to_stochastic_data_map = ComponentMap()
# maps between stochastic data and variable lower and upper bounds
self.stochastic_data_to_variables_lb_map = ComponentMap()
self.variable_to_stochastic_data_lb_map = ComponentMap()
self.stochastic_data_to_variables_ub_map = ComponentMap()
self.variable_to_stochastic_data_ub_map = ComponentMap()
self.variable_symbols = ComponentMap()
if not isinstance(reference_model, Block):
raise TypeError("reference model input must be a Pyomo model")
self.reference_model = reference_model
#
# Extract stochastic parameters from the
# StochasticDataAnnotation object
#
self.stochastic_data = \
_extract_stochastic_data(self.reference_model)
#
# Get the variable stages from the
# VariableStageAnnotation object
#
(self.stage_to_variables_map,
self.variable_to_stage_map,
self._variable_stage_assignments) = \
_map_variable_stages(self.reference_model)
self.time_stages = tuple(sorted(self.stage_to_variables_map))
assert self.time_stages[0] == 1
self.variable_symbols = generate_cuid_names(self.reference_model,
ctype=Var)
# remove the parent blocks from this map
keys_to_delete = []
for var in self.variable_symbols:
if var.parent_component().ctype is not Var:
keys_to_delete.append(var)
for key in keys_to_delete:
del self.variable_symbols[key]
#
# Get the stage cost components from the StageCostAnnotation
# and generate a dummy single-scenario scenario tree
#
stage_cost_annotation = locate_annotations(self.reference_model,
StageCostAnnotation,
max_allowed=1)
if len(stage_cost_annotation) == 0:
raise ValueError("Reference model is missing stage cost "
"annotation: %s" % (StageCostAnnotation.__name__))
else:
assert len(stage_cost_annotation) == 1
stage_cost_annotation = stage_cost_annotation[0][1]
stage_cost_assignments = ComponentMap(
stage_cost_annotation.expand_entries())
stage1_cost = None
stage2_cost = None
for cdata, stagenum in stage_cost_assignments.items():
if stagenum == 1:
stage1_cost = cdata
elif stagenum == 2:
stage2_cost = cdata
if stage1_cost is None:
raise ValueError("Missing stage cost annotation "
"for time stage: 1")
if stage2_cost is None:
raise ValueError("Missing stage cost annotation "
"for time stage: 2")
assert stage1_cost is not stage2_cost
self._stage1_cost = stage1_cost
self._stage2_cost = stage2_cost
#
# Extract the locations of variables and stochastic data
# within the model
#
sto_obj = StochasticObjectiveAnnotation()
for objcntr, obj in enumerate(
self.reference_model.component_data_objects(Objective,
active=True,
descend_into=True),
1):
if objcntr > 1:
raise ValueError(
"Reference model can not contain more than one "
"active objective")
self.objective = obj
self.objective_sense = obj.sense
obj_params = tuple(
self._collect_mutable_parameters(obj.expr).values())
self.objective_to_stochastic_data_map[obj] = []
for paramdata in obj_params:
if paramdata in self.stochastic_data:
self.stochastic_data_to_objectives_map.\
setdefault(paramdata, []).append(obj)
self.objective_to_stochastic_data_map[obj].\
append(paramdata)
if len(self.objective_to_stochastic_data_map[obj]) == 0:
del self.objective_to_stochastic_data_map[obj]
else:
# TODO: Can we make this declaration sparse
# by identifying which variables have
# stochastic coefficients? How to handle
# non-linear expressions?
sto_obj.declare(obj)
obj_variables = tuple(self._collect_variables(obj.expr).values())
self.objective_to_variables_map[obj] = []
for var in obj_variables:
self.variable_to_objectives_map.\
setdefault(var, []).append(obj)
self.objective_to_variables_map[obj].append(var)
if len(self.objective_to_variables_map[obj]) == 0:
del self.objective_to_variables_map[obj]
sto_conbounds = StochasticConstraintBoundsAnnotation()
sto_conbody = StochasticConstraintBodyAnnotation()
for con in self.reference_model.component_data_objects(
Constraint, active=True, descend_into=True):
lower_params = tuple(
self._collect_mutable_parameters(con.lower).values())
body_params = tuple(
self._collect_mutable_parameters(con.body).values())
upper_params = tuple(
self._collect_mutable_parameters(con.upper).values())
# TODO: Can we make this declaration sparse
# by idenfifying which variables have
# stochastic coefficients? How to handle
# non-linear expressions? Currently, this
# code also fails to detect that mutable
# "constant" expressions might fall out
# of the body and into the bounds.
if len(body_params):
sto_conbody.declare(con)
if len(body_params) or \
len(lower_params) or \
len(upper_params):
sto_conbounds.declare(
con,
lb=bool(len(lower_params) or len(body_params)),
ub=bool(len(upper_params) or len(body_params)))
all_stochastic_params = {}
for param in itertools.chain(lower_params, body_params,
upper_params):
if param in self.stochastic_data:
all_stochastic_params[id(param)] = param
if len(all_stochastic_params) > 0:
self.constraint_to_stochastic_data_map[con] = []
# no params will appear twice in this iteration
for param in all_stochastic_params.values():
self.stochastic_data_to_constraints_map.\
setdefault(param, []).append(con)
self.constraint_to_stochastic_data_map[con].\
append(param)
body_variables = tuple(self._collect_variables(con.body).values())
self.constraint_to_variables_map[con] = []
for var in body_variables:
self.variable_to_constraints_map.\
setdefault(var, []).append(con)
self.constraint_to_variables_map[con].append(var)
# For now, it is okay to have SOSConstraints in the
# representation of a problem, but the SOS
# constraints can't have custom weights that
# represent stochastic data
for soscon in self.reference_model.component_data_objects(
SOSConstraint, active=True, descend_into=True):
for var, weight in soscon.get_items():
weight_params = tuple(
self._collect_mutable_parameters(weight).values())
if param in self.stochastic_data:
raise ValueError(
"SOSConstraints with stochastic data are currently"
" not supported in embedded stochastic programs. "
"The SOSConstraint component '%s' has a weight "
"term for variable '%s' that references stochastic"
" parameter '%s'" %
(soscon.name, var.name, param.name))
self.variable_to_constraints_map.\
setdefault(var, []).append(soscon)
self.constraint_to_variables_map.\
setdefault(soscon, []).append(var)
sto_varbounds = StochasticVariableBoundsAnnotation()
for var in self.reference_model.component_data_objects(
Var, descend_into=True):
lower_params = tuple(
self._collect_mutable_parameters(var.lb).values())
upper_params = tuple(
self._collect_mutable_parameters(var.ub).values())
if (len(lower_params) > 0) or \
(len(upper_params) > 0):
sto_varbounds.declare(var,
lb=bool(len(lower_params) > 0),
ub=bool(len(upper_params) > 0))
self.variable_to_stochastic_data_lb_map[var] = []
for param in lower_params:
if param in self.stochastic_data:
self.stochastic_data_to_variables_lb_map.\
setdefault(param, []).append(var)
self.variable_to_stochastic_data_lb_map[var].\
append(param)
if len(self.variable_to_stochastic_data_lb_map[var]) == 0:
del self.variable_to_stochastic_data_lb_map[var]
self.variable_to_stochastic_data_ub_map[var] = []
for param in upper_params:
if param in self.stochastic_data:
self.stochastic_data_to_variables_ub_map.\
setdefault(param, []).append(var)
self.variable_to_stochastic_data_ub_map[var].\
append(param)
if len(self.variable_to_stochastic_data_ub_map[var]) == 0:
del self.variable_to_stochastic_data_ub_map[var]
#
# Generate the explicit annotations
#
# first make sure these annotations do not already exist
if len(
locate_annotations(self.reference_model,
StochasticConstraintBoundsAnnotation)) > 0:
raise ValueError(
"Reference model can not contain "
"a StochasticConstraintBoundsAnnotation declaration.")
if len(
locate_annotations(self.reference_model,
StochasticConstraintBodyAnnotation)) > 0:
raise ValueError(
"Reference model can not contain "
"a StochasticConstraintBodyAnnotation declaration.")
if len(
locate_annotations(self.reference_model,
StochasticObjectiveAnnotation)) > 0:
raise ValueError("Reference model can not contain "
"a StochasticObjectiveAnnotation declaration.")
# now add any necessary annotations
if sto_obj.has_declarations:
assert not hasattr(
self.reference_model,
".pyspembeddedsp_stochastic_objective_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_objective_annotation", sto_obj)
if sto_conbody.has_declarations:
assert not hasattr(
self.reference_model,
".pyspembeddedsp_stochastic_constraint_body_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_constraint_body_annotation",
sto_conbody)
if sto_conbounds.has_declarations:
assert not hasattr(
self.reference_model,
".pyspembeddedsp_stochastic_constraint_bounds_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_constraint_bounds_annotation",
sto_conbounds)
if sto_varbounds.has_declarations:
assert not hasattr(
self.reference_model,
".pyspembeddedsp_stochastic_variable_bounds_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_variable_bounds_annotation",
sto_varbounds)
def __init__(self, reference_model):
self.reference_model = None
self.objective = None
self.time_stages = None
self.stage_to_variables_map = {}
self.variable_to_stage_map = {}
# the set of stochastic data objects
# (possibly mapped to some distribution)
self.stochastic_data = None
# maps between variables and objectives
self.variable_to_objectives_map = ComponentMap()
self.objective_to_variables_map = ComponentMap()
# maps between variables and constraints
self.variable_to_constraints_map = ComponentMap()
self.constraint_to_variables_map = ComponentMap()
# maps between stochastic data and objectives
self.stochastic_data_to_objectives_map = ComponentMap()
self.objective_to_stochastic_data_map = ComponentMap()
# maps between stochastic data and constraints
self.stochastic_data_to_constraints_map = ComponentMap()
self.constraint_to_stochastic_data_map = ComponentMap()
# maps between stochastic data and variable lower and upper bounds
self.stochastic_data_to_variables_lb_map = ComponentMap()
self.variable_to_stochastic_data_lb_map = ComponentMap()
self.stochastic_data_to_variables_ub_map = ComponentMap()
self.variable_to_stochastic_data_ub_map = ComponentMap()
self.variable_symbols = ComponentMap()
if not isinstance(reference_model, Block):
raise TypeError("reference model input must be a Pyomo model")
self.reference_model = reference_model
#
# Extract stochastic parameters from the
# StochasticDataAnnotation object
#
self.stochastic_data = \
_extract_stochastic_data(self.reference_model)
#
# Get the variable stages from the
# VariableStageAnnotation object
#
(self.stage_to_variables_map,
self.variable_to_stage_map,
self._variable_stage_assignments) = \
_map_variable_stages(self.reference_model)
self.time_stages = tuple(sorted(self.stage_to_variables_map))
assert self.time_stages[0] == 1
self.variable_symbols = generate_cuid_names(self.reference_model,
ctype=Var)
# remove the parent blocks from this map
keys_to_delete = []
for var in self.variable_symbols:
if var.parent_component().type() is not Var:
keys_to_delete.append(var)
for key in keys_to_delete:
del self.variable_symbols[key]
#
# Get the stage cost components from the StageCostAnnotation
# and generate a dummy single-scenario scenario tree
#
stage_cost_annotation = locate_annotations(
self.reference_model,
StageCostAnnotation,
max_allowed=1)
if len(stage_cost_annotation) == 0:
raise ValueError(
"Reference model is missing stage cost "
"annotation: %s" % (StageCostAnnotation.__name__))
else:
assert len(stage_cost_annotation) == 1
stage_cost_annotation = stage_cost_annotation[0][1]
stage_cost_assignments = ComponentMap(
stage_cost_annotation.expand_entries())
stage1_cost = None
stage2_cost = None
for cdata, stagenum in stage_cost_assignments.items():
if stagenum == 1:
stage1_cost = cdata
elif stagenum == 2:
stage2_cost = cdata
if stage1_cost is None:
raise ValueError("Missing stage cost annotation "
"for time stage: 1")
if stage2_cost is None:
raise ValueError("Missing stage cost annotation "
"for time stage: 2")
assert stage1_cost is not stage2_cost
self._stage1_cost = stage1_cost
self._stage2_cost = stage2_cost
#
# Extract the locations of variables and stochastic data
# within the model
#
sto_obj = StochasticObjectiveAnnotation()
for objcntr, obj in enumerate(
self.reference_model.component_data_objects(
Objective,
active=True,
descend_into=True), 1):
if objcntr > 1:
raise ValueError(
"Reference model can not contain more than one "
"active objective")
self.objective = obj
self.objective_sense = obj.sense
obj_params = tuple(
self._collect_mutable_parameters(obj.expr).values())
self.objective_to_stochastic_data_map[obj] = []
for paramdata in obj_params:
if paramdata in self.stochastic_data:
self.stochastic_data_to_objectives_map.\
setdefault(paramdata, []).append(obj)
self.objective_to_stochastic_data_map[obj].\
append(paramdata)
if len(self.objective_to_stochastic_data_map[obj]) == 0:
del self.objective_to_stochastic_data_map[obj]
else:
# TODO: Can we make this declaration sparse
# by identifying which variables have
# stochastic coefficients? How to handle
# non-linear expressions?
sto_obj.declare(obj)
obj_variables = tuple(
self._collect_variables(obj.expr).values())
self.objective_to_variables_map[obj] = []
for var in obj_variables:
self.variable_to_objectives_map.\
setdefault(var, []).append(obj)
self.objective_to_variables_map[obj].append(var)
if len(self.objective_to_variables_map[obj]) == 0:
del self.objective_to_variables_map[obj]
sto_conbounds = StochasticConstraintBoundsAnnotation()
sto_conbody = StochasticConstraintBodyAnnotation()
for con in self.reference_model.component_data_objects(
Constraint,
active=True,
descend_into=True):
lower_params = tuple(
self._collect_mutable_parameters(con.lower).values())
body_params = tuple(
self._collect_mutable_parameters(con.body).values())
upper_params = tuple(
self._collect_mutable_parameters(con.upper).values())
# TODO: Can we make this declaration sparse
# by idenfifying which variables have
# stochastic coefficients? How to handle
# non-linear expressions? Currently, this
# code also fails to detect that mutable
# "constant" expressions might fall out
# of the body and into the bounds.
if len(body_params):
sto_conbody.declare(con)
if len(body_params) or \
len(lower_params) or \
len(upper_params):
sto_conbounds.declare(con,
lb=bool(len(lower_params) or len(body_params)),
ub=bool(len(upper_params) or len(body_params)))
all_stochastic_params = {}
for param in itertools.chain(lower_params,
body_params,
upper_params):
if param in self.stochastic_data:
all_stochastic_params[id(param)] = param
if len(all_stochastic_params) > 0:
self.constraint_to_stochastic_data_map[con] = []
# no params will appear twice in this iteration
for param in all_stochastic_params.values():
self.stochastic_data_to_constraints_map.\
setdefault(param, []).append(con)
self.constraint_to_stochastic_data_map[con].\
append(param)
body_variables = tuple(
self._collect_variables(con.body).values())
self.constraint_to_variables_map[con] = []
for var in body_variables:
self.variable_to_constraints_map.\
setdefault(var, []).append(con)
self.constraint_to_variables_map[con].append(var)
# For now, it is okay to have SOSConstraints in the
# representation of a problem, but the SOS
# constraints can't have custom weights that
# represent stochastic data
for soscon in self.reference_model.component_data_objects(
SOSConstraint,
active=True,
descend_into=True):
for var, weight in soscon.get_items():
weight_params = tuple(
self._collect_mutable_parameters(weight).values())
if param in self.stochastic_data:
raise ValueError(
"SOSConstraints with stochastic data are currently"
" not supported in embedded stochastic programs. "
"The SOSConstraint component '%s' has a weight "
"term for variable '%s' that references stochastic"
" parameter '%s'"
% (soscon.name,
var.name,
param.name))
self.variable_to_constraints_map.\
setdefault(var, []).append(soscon)
self.constraint_to_variables_map.\
setdefault(soscon, []).append(var)
sto_varbounds = StochasticVariableBoundsAnnotation()
for var in self.reference_model.component_data_objects(
Var,
descend_into=True):
lower_params = tuple(
self._collect_mutable_parameters(var.lb).values())
upper_params = tuple(
self._collect_mutable_parameters(var.ub).values())
if (len(lower_params) > 0) or \
(len(upper_params) > 0):
sto_varbounds.declare(var,
lb=bool(len(lower_params) > 0),
ub=bool(len(upper_params) > 0))
self.variable_to_stochastic_data_lb_map[var] = []
for param in lower_params:
if param in self.stochastic_data:
self.stochastic_data_to_variables_lb_map.\
setdefault(param, []).append(var)
self.variable_to_stochastic_data_lb_map[var].\
append(param)
if len(self.variable_to_stochastic_data_lb_map[var]) == 0:
del self.variable_to_stochastic_data_lb_map[var]
self.variable_to_stochastic_data_ub_map[var] = []
for param in upper_params:
if param in self.stochastic_data:
self.stochastic_data_to_variables_ub_map.\
setdefault(param, []).append(var)
self.variable_to_stochastic_data_ub_map[var].\
append(param)
if len(self.variable_to_stochastic_data_ub_map[var]) == 0:
del self.variable_to_stochastic_data_ub_map[var]
#
# Generate the explicit annotations
#
# first make sure these annotations do not already exist
if len(locate_annotations(self.reference_model,
StochasticConstraintBoundsAnnotation)) > 0:
raise ValueError("Reference model can not contain "
"a StochasticConstraintBoundsAnnotation declaration.")
if len(locate_annotations(self.reference_model,
StochasticConstraintBodyAnnotation)) > 0:
raise ValueError("Reference model can not contain "
"a StochasticConstraintBodyAnnotation declaration.")
if len(locate_annotations(self.reference_model,
StochasticObjectiveAnnotation)) > 0:
raise ValueError("Reference model can not contain "
"a StochasticObjectiveAnnotation declaration.")
# now add any necessary annotations
if sto_obj.has_declarations:
assert not hasattr(self.reference_model,
".pyspembeddedsp_stochastic_objective_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_objective_annotation",
sto_obj)
if sto_conbody.has_declarations:
assert not hasattr(self.reference_model,
".pyspembeddedsp_stochastic_constraint_body_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_constraint_body_annotation",
sto_conbody)
if sto_conbounds.has_declarations:
assert not hasattr(self.reference_model,
".pyspembeddedsp_stochastic_constraint_bounds_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_constraint_bounds_annotation",
sto_conbounds)
if sto_varbounds.has_declarations:
assert not hasattr(self.reference_model,
".pyspembeddedsp_stochastic_variable_bounds_annotation")
setattr(self.reference_model,
".pyspembeddedsp_stochastic_variable_bounds_annotation",
sto_varbounds)
