def _apply_to(self, instance, **kwds):
if not instance.ctype in (Block, Disjunct):
raise GDP_Error(
"Transformation called on %s of type %s. 'instance'"
" must be a ConcreteModel, Block, or Disjunct (in "
"the case of nested disjunctions)." %
(instance.name, instance.ctype))
try:
self._config = self.CONFIG(kwds.pop('options', {}))
self._config.set_value(kwds)
self._transformation_blocks = {}
if not self._config.assume_fixed_vars_permanent:
fixed_vars = ComponentMap()
for v in get_vars_from_components(instance,
Constraint,
include_fixed=True,
active=True,
descend_into=(Block,
Disjunct)):
if v.fixed:
fixed_vars[v] = value(v)
v.fixed = False
self._apply_to_impl(instance)
finally:
# restore fixed variables
if not self._config.assume_fixed_vars_permanent:
for v, val in fixed_vars.items():
v.fix(val)
del self._config
del self._transformation_blocks
def generate_structured_model(self):
"""
Using the community map and the original model used to create this community map, we will create
structured_model, which will be based on the original model but will place variables, constraints, and
objectives into or outside of various blocks (communities) based on the community map.
Returns
-------
structured_model: Block
a Pyomo model that reflects the nature of the community map
"""
# Initialize a new model (structured_model) which will contain variables and constraints in blocks based on
# their respective communities within the CommunityMap
structured_model = ConcreteModel()
# Create N blocks (where N is the number of communities found within the model)
structured_model.b = Block([0, len(self.community_map) - 1,
1]) # values given for (start, stop, step)
# Initialize a ComponentMap that will map a variable from the model (for example, old_model.x1) used to
# create the CommunityMap to a list of variables in various blocks that were created based on this
# variable (for example, [structured_model.b[0].x1, structured_model.b[3].x1])
blocked_variable_map = ComponentMap()
# Example key-value pair -> {original_model.x1 : [structured_model.b[0].x1, structured_model.b[3].x1]}
# TODO - Consider changing structure of the next two for loops to be more efficient (maybe loop through
# constraints and add variables as you go) (but note that disconnected variables would be
# missed with this strategy)
# First loop through community_map to add all the variables to structured_model before we add constraints
# that use those variables
for community_key, community in self.community_map.items():
_, variables_in_community = community
# Loop through all of the variables (from the original model) in the given community
for stored_variable in variables_in_community:
# Construct a new_variable whose attributes are determined by querying the variable from the
# original model
new_variable = Var(domain=stored_variable.domain,
bounds=stored_variable.bounds)
# Add this new_variable to its block/community and name it using the string of the variable from the
# original model
structured_model.b[community_key].add_component(
str(stored_variable), new_variable)
# Since there could be multiple variables 'x1' (such as
# structured_model.b[0].x1, structured_model.b[3].x1, etc), we need to create equality constraints
# for all of the variables 'x1' within structured_model (this is the purpose of blocked_variable_map)
# Here we update blocked_variable_map to keep track of what equality constraints need to be made
variable_in_new_model = structured_model.find_component(
new_variable)
blocked_variable_map[
stored_variable] = blocked_variable_map.get(
stored_variable, []) + [variable_in_new_model]
# Now that we have all of our variables within the model, we will initialize a dictionary that used to
# replace variables within constraints to other variables (in our case, this will convert variables from the
# original model into variables from the new model (structured_model))
replace_variables_in_expression_map = dict()
# Loop through community_map again, this time to add constraints (with replaced variables)
for community_key, community in self.community_map.items():
constraints_in_community, _ = community
# Loop through all of the constraints (from the original model) in the given community
for stored_constraint in constraints_in_community:
# Now, loop through all of the variables within the given constraint expression
for variable_in_stored_constraint in identify_variables(
stored_constraint.expr):
# Loop through each of the "blocked" variables that a variable is mapped to and update
# replace_variables_in_expression_map if a variable has a "blocked" form in the given community
# What this means is that if we are looping through constraints in community 0, then it would be
# best to change a variable x1 into b[0].x1 as opposed to b[2].x1 or b[5].x1 (assuming all of these
# blocked versions of the variable x1 exist (which depends on the community map))
variable_in_current_block = False
for blocked_variable in blocked_variable_map[
variable_in_stored_constraint]:
if 'b[%d]' % community_key in str(blocked_variable):
# Update replace_variables_in_expression_map accordingly
replace_variables_in_expression_map[
id(variable_in_stored_constraint
)] = blocked_variable
variable_in_current_block = True
if not variable_in_current_block:
# Create a version of the given variable outside of blocks then add it to
# replace_variables_in_expression_map
new_variable = Var(
domain=variable_in_stored_constraint.domain,
bounds=variable_in_stored_constraint.bounds)
# Add the new variable just as we did above (but now it is not in any blocks)
structured_model.add_component(
str(variable_in_stored_constraint), new_variable)
# Update blocked_variable_map to keep track of what equality constraints need to be made
variable_in_new_model = structured_model.find_component(
new_variable)
blocked_variable_map[
variable_in_stored_constraint] = blocked_variable_map.get(
variable_in_stored_constraint,
[]) + [variable_in_new_model]
# Update replace_variables_in_expression_map accordingly
replace_variables_in_expression_map[
id(variable_in_stored_constraint
)] = variable_in_new_model
# TODO - Is there a better way to check whether something is actually an objective? (as done below)
# Check to see whether 'stored_constraint' is actually an objective (since constraints and objectives
# grouped together)
if self.with_objective and isinstance(
stored_constraint, (_GeneralObjectiveData, Objective)):
# If the constraint is actually an objective, we add it to the block as an objective
new_objective = Objective(expr=replace_expressions(
stored_constraint.expr,
replace_variables_in_expression_map))
structured_model.b[community_key].add_component(
str(stored_constraint), new_objective)
else:
# Construct a constraint based on the expression within stored_constraint and the dict we have
# created for the purpose of replacing the variables within the constraint expression
new_constraint = Constraint(expr=replace_expressions(
stored_constraint.expr,
replace_variables_in_expression_map))
# Add this new constraint to the corresponding community/block with its name as the string of the
# constraint from the original model
structured_model.b[community_key].add_component(
str(stored_constraint), new_constraint)
# If with_objective was set to False, that means we might have missed an objective function within the
# original model
if not self.with_objective:
# Construct a new dictionary for replacing the variables (replace_variables_in_objective_map) which will
# be specific to the variables in the objective function, since there is the possibility that the
# objective contains variables we have not yet seen (and thus not yet added to our new model)
for objective_function in self.model.component_data_objects(
ctype=Objective,
active=self.use_only_active_components,
descend_into=True):
for variable_in_objective in identify_variables(
objective_function):
# Add all of the variables in the objective function (not within any blocks)
# Check to make sure a form of the variable has not already been made outside of the blocks
if structured_model.find_component(
str(variable_in_objective)) is None:
new_variable = Var(domain=variable_in_objective.domain,
bounds=variable_in_objective.bounds)
structured_model.add_component(
str(variable_in_objective), new_variable)
# Again we update blocked_variable_map to keep track of what
# equality constraints need to be made
variable_in_new_model = structured_model.find_component(
new_variable)
blocked_variable_map[
variable_in_objective] = blocked_variable_map.get(
variable_in_objective,
[]) + [variable_in_new_model]
# Update the dictionary that we will use to replace the variables
replace_variables_in_expression_map[id(
variable_in_objective)] = variable_in_new_model
else:
for version_of_variable in blocked_variable_map[
variable_in_objective]:
if 'b[' not in str(version_of_variable):
replace_variables_in_expression_map[
id(variable_in_objective
)] = version_of_variable
# Now we will construct a new objective function based on the one from the original model and then
# add it to the new model just as we have done before
new_objective = Objective(expr=replace_expressions(
objective_function.expr,
replace_variables_in_expression_map))
structured_model.add_component(str(objective_function),
new_objective)
# Now, we need to create equality constraints for all of the different "versions" of a variable (such
# as x1, b[0].x1, b[2].x2, etc.)
# Create a constraint list for the equality constraints
structured_model.equality_constraint_list = ConstraintList(
doc="Equality Constraints for the different "
"forms of a given variable")
# Loop through blocked_variable_map and create constraints accordingly
for variable, duplicate_variables in blocked_variable_map.items():
# variable -> variable from the original model
# duplicate_variables -> list of variables in the new model
# Create a list of all the possible equality constraints that need to be made
equalities_to_make = combinations(duplicate_variables, 2)
# Loop through the list of two-variable tuples and create an equality constraint for those two variables
for variable_1, variable_2 in equalities_to_make:
structured_model.equality_constraint_list.add(
expr=variable_1 == variable_2)
# Return 'structured_model', which is essentially identical to the original model but now has all of the
# variables, constraints, and objectives placed into blocks based on the nature of the CommunityMap
return structured_model
def generate_model_graph(model, type_of_graph, with_objective=True, weighted_graph=True,
use_only_active_components=True):
"""
Creates a networkX graph of nodes and edges based on a Pyomo optimization model
This function takes in a Pyomo optimization model, then creates a graphical representation of the model with
specific features of the graph determined by the user (see Parameters below).
(This function is designed to be called by detect_communities, but can be used solely for the purpose of
creating model graphs as well.)
Parameters
----------
model: Block
a Pyomo model or block to be used for community detection
type_of_graph: str
a string that specifies the type of graph that is created from the model
'constraint' creates a graph based on constraint nodes,
'variable' creates a graph based on variable nodes,
'bipartite' creates a graph based on constraint and variable nodes (bipartite graph).
with_objective: bool, optional
a Boolean argument that specifies whether or not the objective function is included in the graph; the
default is True
weighted_graph: bool, optional
a Boolean argument that specifies whether a weighted or unweighted graph is to be created from the Pyomo
model; the default is True (type_of_graph='bipartite' creates an unweighted graph regardless of this parameter)
use_only_active_components: bool, optional
a Boolean argument that specifies whether inactive constraints/objectives are included in the networkX graph
Returns
-------
bipartite_model_graph/projected_model_graph: nx.Graph
a NetworkX graph with nodes and edges based on the given Pyomo optimization model
number_component_map: dict
a dictionary that (deterministically) maps a number to a component in the model
constraint_variable_map: dict
a dictionary that maps a numbered constraint to a list of (numbered) variables that appear in the constraint
"""
# Start off by making a bipartite graph (regardless of the value of type_of_graph), then if
# type_of_graph = 'variable' or 'constraint', we will "collapse" this bipartite graph into a variable node
# or constraint node graph
# Initialize the data structure needed to keep track of edges in the graph (this graph will be made
# without edge weights, because edge weights are not useful for this bipartite graph)
edge_set = set()
bipartite_model_graph = nx.Graph() # Initialize NetworkX graph for the bipartite graph
constraint_variable_map = {} # Initialize map of the variables in constraint equations
# Make a dict of all the components we need for the NetworkX graph (since we cannot use the components directly
# in the NetworkX graph)
if with_objective:
component_number_map = ComponentMap((component, number) for number, component in enumerate(
model.component_data_objects(ctype=(Constraint, Var, Objective), active=use_only_active_components,
descend_into=True,
sort=SortComponents.deterministic)))
else:
component_number_map = ComponentMap((component, number) for number, component in enumerate(
model.component_data_objects(ctype=(Constraint, Var), active=use_only_active_components, descend_into=True,
sort=SortComponents.deterministic)))
# Create the reverse of component_number_map, which will be used in detect_communities to convert the node numbers
# to their corresponding Pyomo modeling components
number_component_map = dict((number, comp) for comp, number in component_number_map.items())
# Add the components as nodes to the bipartite graph
bipartite_model_graph.add_nodes_from([node_number for node_number in range(len(component_number_map))])
# Loop through all constraints in the Pyomo model to determine what edges need to be created
for model_constraint in model.component_data_objects(ctype=Constraint, active=use_only_active_components,
descend_into=True):
numbered_constraint = component_number_map[model_constraint]
# Create a list of the variable numbers that occur in the given constraint equation
numbered_variables_in_constraint_equation = [component_number_map[constraint_variable]
for constraint_variable in
identify_variables(model_constraint.body)]
# Update constraint_variable_map
constraint_variable_map[numbered_constraint] = numbered_variables_in_constraint_equation
# Create a list of all the edges that need to be created based on the variables in this constraint equation
edges_between_nodes = [(numbered_constraint, numbered_variable_in_constraint)
for numbered_variable_in_constraint in numbered_variables_in_constraint_equation]
# Update edge_set based on the determined edges between nodes
edge_set.update(edges_between_nodes)
# This if statement will be executed if the user chooses to include the objective function as a node in
# the model graph
if with_objective:
# Use a loop to account for the possibility of multiple objective functions
for objective_function in model.component_data_objects(ctype=Objective, active=use_only_active_components,
descend_into=True):
numbered_objective = component_number_map[objective_function]
# Create a list of the variable numbers that occur in the given objective function
numbered_variables_in_objective = [component_number_map[objective_variable]
for objective_variable in identify_variables(objective_function)]
# Update constraint_variable_map
constraint_variable_map[numbered_objective] = numbered_variables_in_objective
# Create a list of all the edges that need to be created based on the variables in the objective function
edges_between_nodes = [(numbered_objective, numbered_variable_in_objective)
for numbered_variable_in_objective in numbered_variables_in_objective]
# Update edge_set based on the determined edges between nodes
edge_set.update(edges_between_nodes)
# Add edges to bipartite_model_graph (the order in which edges are added can affect community detection, so
# sorting prevents any unpredictable changes)
bipartite_model_graph.add_edges_from(sorted(edge_set))
if type_of_graph == 'bipartite': # This is the case where the user wants a bipartite graph, which we made above
# Log important information with the following logger function
_event_log(model, bipartite_model_graph, set(constraint_variable_map), type_of_graph, with_objective)
# Return the bipartite NetworkX graph, the dictionary of node numbers mapped to their respective Pyomo
# components, and the map of constraints to the variables they contain
return bipartite_model_graph, number_component_map, constraint_variable_map
# At this point of the code, we will create the projected version of the bipartite
# model graph (based on the specific value of type_of_graph)
constraint_nodes = set(constraint_variable_map)
if type_of_graph == 'constraint':
graph_nodes = constraint_nodes
else:
variable_nodes = set(number_component_map) - constraint_nodes
graph_nodes = variable_nodes
if weighted_graph:
projected_model_graph = nx.bipartite.weighted_projected_graph(bipartite_model_graph, graph_nodes)
else:
projected_model_graph = nx.bipartite.projected_graph(bipartite_model_graph, graph_nodes)
# Log important information with the following logger function
_event_log(model, projected_model_graph, set(constraint_variable_map), type_of_graph, with_objective)
# Return the projected NetworkX graph, the dictionary of node numbers mapped to their respective Pyomo
# components, and the map of constraints to the variables they contain
return projected_model_graph, number_component_map, constraint_variable_map
