def parseCCTP(inFilename):
e = xml.etree.ElementTree.parse(inFilename).getroot()
tpnu_name = e.find('NAME').text
tpnu = Tpnu('', tpnu_name)
# In TPN format every node (or event in TPN terminology) has a non-unique name
# and an unique id. Both of those are strings. For efficiency DC checking algorithms
# denote each node by a number, such that we can cheaply check their equality.
# parse the event
event_ids = set()
tpnu.node_id_to_name = {}
# tpnu.node_name_to_number = {}
tpnu.node_number_to_id = {}
tpnu.node_id_to_number = {}
for event_obj in e.findall('EVENT'):
eid, ename = event_obj.find('ID').text, event_obj.find('NAME').text
event_ids.add(eid)
tpnu.node_id_to_name[eid] = ename
for eid in event_ids:
next_number = len(tpnu.node_number_to_id) + 1
tpnu.node_number_to_id[next_number] = eid
tpnu.node_id_to_number[eid] = next_number
# tpnu.node_name_to_number[tpnu.node_id_to_name[eid]] = next_number
tpnu.num_nodes = len(tpnu.node_number_to_id)
start_id = e.find('START').text
tpnu.start_node = tpnu.node_id_to_number[start_id]
if (tpnu.num_nodes < 1):
return None
# parse the decision variables
assignment_map_with_id = {}
assignment_map_with_name = {}
for variable_obj in e.findall('DECISION-VARIABLE'):
dv_name = variable_obj.find('DECISION-NAME').text
if variable_obj.find('ID') is not None:
dv_id = variable_obj.find('ID').text
else:
dv_id = dv_name
decision_variable = DecisionVariable(dv_id, dv_name)
# construct the assignment for the variable
for value_obj in variable_obj.findall('VALUE'):
# value_id = value_obj.find('ID').text
value_name = value_obj.find('VALUE-NAME').text
value_utility = float(value_obj.find('VALUE-UTILITY').text)
assignment = Assignment(decision_variable, value_name,
value_utility)
# add the assignment to the variable, and a dictionary for future reference
decision_variable.add_domain_value(assignment)
# using the id of the variable and the value of the assignment as key
assignment_map_with_id[(dv_id, value_name)] = assignment
assignment_map_with_name[(dv_name, value_name)] = assignment
tpnu.add_decision_variable(decision_variable)
# parse variables' guards
for variable_obj in e.findall('DECISION-VARIABLE'):
if variable_obj.find('ID') is not None:
dv_id = variable_obj.find('ID').text
else:
dv_id = variable_obj.find('DECISION-NAME').text
decision_variable = tpnu.decision_variables[dv_id]
# the guard could be a conjunctive set of assignment
for guard_obj in variable_obj.findall('GUARD'):
# guard_id = guard_obj.find('ID').text
guard_variable = guard_obj.find('GUARD-VARIABLE').text
guard_value = guard_obj.find('GUARD-VALUE').text
# retrieve the assignment
if (guard_variable, guard_value) in assignment_map_with_id:
guard_assignment = assignment_map_with_id[(guard_variable,
guard_value)]
elif (guard_variable, guard_value) in assignment_map_with_name:
guard_assignment = assignment_map_with_name[(
guard_variable, guard_value)]
# and add to the guards of this decision variable
decision_variable.add_guard(guard_assignment)
# parse the temporal constraints and episodes
# if line below confuses you, that's expected... We need better automated code generation for parsing...
for constraint_obj in e.findall('CONSTRAINT'):
# duration can be one of three types - controllable, uncertain and probabilistic
# here we are only handling the first two cases, which are sorted into two lists
# in our resulting stnu class.
lower_bound = float(constraint_obj.find('LOWERBOUND').text)
upper_bound = float(constraint_obj.find('UPPERBOUND').text)
from_event = constraint_obj.find('START').text
to_event = constraint_obj.find('END').text
constraint_id = constraint_obj.find('ID').text
constraint_name = constraint_obj.find('NAME').text
constraint = TemporalConstraint(constraint_id, constraint_name,
tpnu.node_id_to_number[from_event],
tpnu.node_id_to_number[to_event],
lower_bound, upper_bound)
# check if the constraint is controllable and relaxable
type = constraint_obj.find('TYPE').text
if "Controllable" in type:
constraint.controllable = True
elif "Uncontrollable" in type:
constraint.controllable = False
if constraint_obj.find('MEAN') is not None:
constraint.mean = float(constraint_obj.find('MEAN').text)
if constraint_obj.find('VARIANCE') is not None:
constraint.probabilistic = True
constraint.std = np.sqrt(
float(constraint_obj.find('VARIANCE').text))
if constraint_obj.find('LBRELAXABLE') is not None:
if "T" in constraint_obj.find('LBRELAXABLE').text:
constraint.relaxable_lb = True
lb_cost = constraint_obj.find('LB-RELAX-COST-RATIO').text
constraint.relax_cost_lb = float(lb_cost)
if constraint_obj.find('UBRELAXABLE') is not None:
if "T" in constraint_obj.find('UBRELAXABLE').text:
constraint.relaxable_ub = True
ub_cost = constraint_obj.find('UB-RELAX-COST-RATIO').text
constraint.relax_cost_ub = float(ub_cost)
# Next we deal with the guard conditions
# the approach is identical to that for decision variables
for guard_obj in constraint_obj.findall('GUARD'):
# guard_id = guard_obj.find('ID').text
guard_variable = guard_obj.find('GUARD-VARIABLE').text
guard_value = guard_obj.find('GUARD-VALUE').text
# retrieve the assignment
if (guard_variable, guard_value) in assignment_map_with_id:
guard_assignment = assignment_map_with_id[(guard_variable,
guard_value)]
elif (guard_variable, guard_value) in assignment_map_with_name:
guard_assignment = assignment_map_with_name[(
guard_variable, guard_value)]
# and add to the guards of this decision variable
constraint.add_guard(guard_assignment)
tpnu.add_temporal_constraint(constraint)
# Parse Chance Constraint
# risk_bound = float(e.find('RISK-BOUND').text)
# risk_bound_relax_cost = float(e.find('RISK-BOUND-RELAX-COST').text)
risk_bound = 0.05
risk_bound_relax_cost = 100
cc_constraint = ChanceConstraint("CC-1", "CC-Constraint", risk_bound)
if risk_bound_relax_cost > 0:
cc_constraint.relaxable_bound = True
cc_constraint.relax_cost = risk_bound_relax_cost
tpnu.add_chance_constraint(cc_constraint)
tpnu.start_node = tpnu.node_id_to_number[start_id]
return tpnu
def parseCCTP(inFilename):
e = xml.etree.ElementTree.parse(inFilename).getroot()
tpnu_name = e.find('NAME').text
tpnu = Tpnu('', tpnu_name)
# In TPN format every node (or event in TPN terminology) has a non-unique name
# and an unique id. Both of those are strings. For efficiency DC checking algorithms
# denote each node by a number, such that we can cheaply check their equality.
# parse the event
event_ids = set()
tpnu.node_id_to_name = {}
tpnu.node_number_to_id = {}
tpnu.node_id_to_number = {}
for event_obj in e.findall('EVENT'):
eid, ename = event_obj.find('ID').text, event_obj.find('NAME').text
event_ids.add(eid)
tpnu.node_id_to_name[eid] = ename
for eid in event_ids:
next_number = len(tpnu.node_number_to_id) + 1
tpnu.node_number_to_id[next_number] = eid
tpnu.node_id_to_number[eid] = next_number
tpnu.num_nodes = len(tpnu.node_number_to_id)
start_id = e.find('START').text
tpnu.start_node = tpnu.node_id_to_number[start_id]
if (tpnu.num_nodes < 1):
return None
# parse the decision variables
assignment_map_with_id = {}
assignment_map_with_name = {}
for variable_obj in e.findall('DECISION-VARIABLE'):
dv_name = variable_obj.find('DECISION-NAME').text
if variable_obj.find('ID') is not None:
dv_id = variable_obj.find('ID').text
else:
dv_id = dv_name
decision_variable = DecisionVariable(dv_id,dv_name)
# construct the assignment for the variable
for value_obj in variable_obj.findall('VALUE'):
# value_id = value_obj.find('ID').text
value_name = value_obj.find('VALUE-NAME').text
value_utility = float(value_obj.find('VALUE-UTILITY').text)
assignment = Assignment(decision_variable, value_name, value_utility)
# add the assignment to the variable, and a dictionary for future reference
decision_variable.add_domain_value(assignment)
# using the id of the variable and the value of the assignment as key
assignment_map_with_id[(dv_id,value_name)] = assignment
assignment_map_with_name[(dv_name,value_name)] = assignment
tpnu.add_decision_variable(decision_variable)
# parse variables' guards
for variable_obj in e.findall('DECISION-VARIABLE'):
if variable_obj.find('ID') is not None:
dv_id = variable_obj.find('ID').text
else:
dv_id = variable_obj.find('DECISION-NAME').text
decision_variable = tpnu.decision_variables[dv_id]
# the guard could be a conjunctive set of assignment
for guard_obj in variable_obj.findall('GUARD'):
# guard_id = guard_obj.find('ID').text
guard_variable = guard_obj.find('GUARD-VARIABLE').text
guard_value = guard_obj.find('GUARD-VALUE').text
# retrieve the assignment
if (guard_variable,guard_value) in assignment_map_with_id:
guard_assignment = assignment_map_with_id[(guard_variable,guard_value)]
elif (guard_variable,guard_value) in assignment_map_with_name:
guard_assignment = assignment_map_with_name[(guard_variable,guard_value)]
# and add to the guards of this decision variable
decision_variable.add_guard(guard_assignment)
# parse the temporal constraints and episodes
# if line below confuses you, that's expected... We need better automated code generation for parsing...
for constraint_obj in e.findall('CONSTRAINT'):
# duration can be one of three types - controllable, uncertain and probabilistic
# here we are only handling the first two cases, which are sorted into two lists
# in our resulting stnu class.
lower_bound = float(constraint_obj.find('LOWERBOUND').text)
upper_bound = float(constraint_obj.find('UPPERBOUND').text)
from_event = constraint_obj.find('START').text
to_event = constraint_obj.find('END').text
constraint_id = constraint_obj.find('ID').text
constraint_name = constraint_obj.find('NAME').text
constraint = TemporalConstraint(constraint_id, constraint_name, tpnu.node_id_to_number[from_event], tpnu.node_id_to_number[to_event], lower_bound, upper_bound)
# check if the constraint is controllable and relaxable
type = constraint_obj.find('TYPE').text
if "Controllable" in type:
constraint.controllable = True
elif "Uncontrollable" in type:
constraint.controllable = False
if constraint_obj.find('MEAN') is not None:
constraint.mean = float(constraint_obj.find('MEAN').text)
if constraint_obj.find('VARIANCE') is not None:
constraint.probabilistic = True
constraint.std = np.sqrt(float(constraint_obj.find('VARIANCE').text))
if constraint_obj.find('LBRELAXABLE') is not None:
if "T" in constraint_obj.find('LBRELAXABLE').text:
constraint.relaxable_lb = True
lb_cost = constraint_obj.find('LB-RELAX-COST-RATIO').text
constraint.relax_cost_lb = float(lb_cost)
if constraint_obj.find('UBRELAXABLE') is not None:
if "T" in constraint_obj.find('UBRELAXABLE').text:
constraint.relaxable_ub = True
ub_cost = constraint_obj.find('UB-RELAX-COST-RATIO').text
constraint.relax_cost_ub = float(ub_cost)
# Next we deal with the guard conditions
# the approach is identical to that for decision variables
for guard_obj in constraint_obj.findall('GUARD'):
# guard_id = guard_obj.find('ID').text
guard_variable = guard_obj.find('GUARD-VARIABLE').text
guard_value = guard_obj.find('GUARD-VALUE').text
# retrieve the assignment
if (guard_variable,guard_value) in assignment_map_with_id:
guard_assignment = assignment_map_with_id[(guard_variable,guard_value)]
elif (guard_variable,guard_value) in assignment_map_with_name:
guard_assignment = assignment_map_with_name[(guard_variable,guard_value)]
# and add to the guards of this decision variable
constraint.add_guard(guard_assignment)
tpnu.add_temporal_constraint(constraint)
# Parse Chance Constraint
# risk_bound = float(e.find('RISK-BOUND').text)
# risk_bound_relax_cost = float(e.find('RISK-BOUND-RELAX-COST').text)
risk_bound = 0.05
risk_bound_relax_cost = 100
cc_constraint = ChanceConstraint("CC-1", "CC-Constraint", risk_bound)
if risk_bound_relax_cost > 0:
cc_constraint.relaxable_bound = True
cc_constraint.relax_cost = risk_bound_relax_cost
tpnu.add_chance_constraint(cc_constraint)
tpnu.start_node = tpnu.node_id_to_number[start_id]
return tpnu
# print('Updating G['+str(2 * idx)+']: ' + str(G[2 * idx]))
# print('Updating G['+str(2 * idx+1)+']: ' + str(G[2 * idx + 1]))
if __name__ == "__main__":
candidate = Candidate()
ev1 = 1
ev2 = 2
ev3 = 3
ep1 = TemporalConstraint('ep-1','ep-1', ev1, ev2, 15,30)
ep2 = TemporalConstraint('ep-2','ep-2', ev2, ev3, 15,30)
ep3 = TemporalConstraint('ep-3','ep-3', ev1, ev3, 40,40)
ep1.controllable = False
ep1.probabilistic = True
ep1.mean = 150
ep1.std = 5
ep2.controllable = False
ep2.probabilistic = True
ep2.mean = 150
ep2.std = 5
ep3.relaxable_ub = True
ep3.relax_cost_ub = 0.1
new_cycle = NegativeCycle()
new_cycle.add_constraint(ep1,0,1)
new_cycle.add_constraint(ep1,1,-1)
new_cycle.add_constraint(ep2,0,1)
# print('Updating G['+str(2 * idx)+']: ' + str(G[2 * idx]))
# print('Updating G['+str(2 * idx+1)+']: ' + str(G[2 * idx + 1]))
if __name__ == "__main__":
candidate = Candidate()
ev1 = 1
ev2 = 2
ev3 = 3
ep1 = TemporalConstraint('ep-1', 'ep-1', ev1, ev2, 15, 30)
ep2 = TemporalConstraint('ep-2', 'ep-2', ev2, ev3, 15, 30)
ep3 = TemporalConstraint('ep-3', 'ep-3', ev1, ev3, 40, 40)
ep1.controllable = False
ep1.probabilistic = True
ep1.mean = 150
ep1.std = 5
ep2.controllable = False
ep2.probabilistic = True
ep2.mean = 150
ep2.std = 5
ep3.relaxable_ub = True
ep3.relax_cost_ub = 0.1
new_cycle = NegativeCycle()
new_cycle.add_constraint(ep1, 0, 1)
new_cycle.add_constraint(ep1, 1, -1)
new_cycle.add_constraint(ep2, 0, 1)
