def prepareOperand(self,operand,inputLeadDataDict,executionStack): from Utils.Log import Log from Utils.TriumphKey import TriumphKey if len(executionStack) > 0: currentAgentDataDict=executionStack[-1].currentIterationData else: currentAgentDataDict=None if operand.opType == TriumphKey.OperandType.LITERAL: return operand.opTag elif operand.opType == TriumphKey.OperandType.FEATURE: if inputLeadDataDict.has_key(operand.opTag) is True: return inputLeadDataDict[operand.opTag] elif currentAgentDataDict is not None: #check for data in current execution stack Log.d(Allocation.TAG,currentAgentDataDict) if currentAgentDataDict.has_key(operand.opTag) is True: return currentAgentDataDict[operand.opTag] return None else: #TODO: Ideally allocation type of feature should be handled here Log.log(Allocation.TAG,'Could not find data for this feature'+str(operand.opTag)) return None elif operand.opType == TriumphKey.OperandType.OPERATION: subRule=operand.subRule[0] subRuleName=subRule.operands[0].opTag subWorkflow=self.workflowManager.getWorkflow(subRuleName,'tech',0,'sanket') if subWorkflow is not None: return self.runWorkflow(subWorkflow,inputLeadDataDict,executionStack) elif operand.opType == TriumphKey.OperandType.VAR: if operand.opTag=='agents': agentDataList=[] for key,agentData in self.inputAgentsData.iteritems(): agentDataList.append(agentData) return agentDataList
def allocate(self): from Utils.Log import Log from AllocationService import AllocationService from Operation import Operation # Save rules in a dictionary with key being the index #Log.log(Allocation.TAG, 'Input leads data is '+str(self.inputLeadsData)) for key,value in self.inputLeadsData.iteritems(): Log.log(Allocation.TAG,'Running loop for '+str(key)) self.workflowManager=AllocationService.getWorkflowManager() workflow=self.workflowManager.getWorkflow(self.workflowName,'tech',0,'sanket') self.runWorkflow(workflow,value,[])
def createExecutionContext(self):
from Utils.Log import Log
from mongoengine import connect
MONGO_DATABASE_NAME='testdb'
MONGO_HOST='127.0.0.1'
MONGO_PORT=27017
connect('testdb',host=MONGO_HOST,port=MONGO_PORT)
self.workflowName='termAllocation'
bu='tech'
version=0
developer='sanket'
from Executor.ExecutionContext import ExecutionContext
self.executionContext=ExecutionContext(self.workflowName,bu,version,developer)
self.executionContext.prepareDB()
self.executionContext.populateFeatureListObj(self.executionContext.workflow)
Log.log(Allocation.TAG,'Feature list is '+str(self.executionContext.featureList))
self.fetchLeadsAndAgentsData()
self.executionContext.fetchFeatureValueForLeads(self.inputLeadsData,self.inputAgentsData)
def fetchLeadsAndAgentsData(self): from Utils.Log import Log import sys this_function_name = sys._getframe().f_code.co_name Log.log(Allocation.TAG,this_function_name) from Executor.InputDataExtracter import InputDataExtracter inputDataExtracter=InputDataExtracter() inputDataExtracter.setInputLeadsQuery(None) inputDataExtracter.setInputAgentsQuery(None) self.inputLeadsData=inputDataExtracter.fetchInputLeads() self.inputAgentsData=inputDataExtracter.fetchInputAgents() self.leadAssignmentData=[] # Contains pair of lead against Agent if self.inputLeadsData is not None: for leadIdKey in self.inputLeadsData.keys(): self.leadsList.append(leadIdKey) else: Log.log(Allocation.TAG,'Input leads data is None') if self.inputAgentsData is not None: for key in self.inputAgentsData.keys(): pass
solutions = []
iterationNumber = 0
print("Starting...")
Net = Network(folderName="Topologia1")
allPossibilities = list(map(list, product(range(minimumValue, maximumValue + 1), repeat=sizeOfLinkBundles)))
for individual in allPossibilities:
print("Iteration Number: " + str(iterationNumber) + "\tpercentage: " + str(round(float(iterationNumber/len(allPossibilities) * 100), 2)))
result = evaluateNetwork(Net, individual)
if not result[1] >= 1.1:
ResultLog.log(result)
iterationNumber += 1
ResultLog.save()
stopTime = timeit.default_timer()
print('Execution Time:', stopTime - startTime)
with open(fileName + '.txt') as f:
lines = f.readlines()
solution_list = []
count = 0
for line in lines:
print('loading file, percentage: \t' + str(round(float(count/len(lines) * 100), 2)))
crossover=IntegerSBXCrossover(probability=0.3,
distribution_index=20),
termination_criterion=StoppingByEvaluationsCustom(
max_evaluations=max_evaluations,
reference_point=[5000, 2.1],
AlgorithmName='Reference')
# termination_criterion=stopCriterion
)
progress_bar = ProgressBarObserver(max=max_evaluations)
algorithm.observable.register(progress_bar)
algorithm.run()
solutions = algorithm.get_result()
for solution in solutions:
if not solution.objectives[1] >= 1.3:
solutionsResult.append(solution)
if executions == (timesToRun - 1):
frontResult = get_non_dominated_solutions(solutionsResult)
for sol in frontResult:
log_front.log(
str(sol.objectives[0]) + " " + str(sol.objectives[1]))
log_front.save()
plot_front = Plot(title='Pareto front approximation',
axis_labels=['Interfaces', 'TIRF'])
plot_front.plot(frontResult, label='NSGAII-OTN')
stopTime = timeit.default_timer()
print('Execution Time:', stopTime - startTime)