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 runOperation(self,workflowName,curRule,operandList,executionStack,inputLeadDataDict): #Process Operator on gathered list of operands from Utils.TriumphKey import TriumphKey from Utils.Log import Log result=0 if curRule.operator == TriumphKey.Operator.FEATURE: from FeatureManager.FeatureStore import FeatureStore fs=FeatureStore() allocationFeature=curRule.operands[0].opTag allocOperandList=operandList[1:] allocOperandTuple=tuple(operandList[1:]) featureType=fs.getFeatureType(allocationFeature) if featureType==TriumphKey.FeatureType.ALLOCATION: if self.mCache.getData(allocationFeature,allocOperandList) is None: result=self.executionContext.fetchAllocationFeatureValues(allocationFeature,allocOperandTuple) result=result[0][0][1] self.mCache.saveData(allocationFeature,allocOperandList,result) else: result=self.mCache.getData(allocationFeature,allocOperandList) if result is not None: Log.d(Allocation.TAG,'Found an allocation feature name'+str(allocationFeature)) #result=result[0] elif curRule.operator == TriumphKey.Operator.LOOP: dataList=operandList[0] executionStack.append(ExecutionStackNode(workflowName,curRule.depth,curRule.nextRuleIndex,ExecutionStackNode.STACKNODETYPE.DATALOOP,dataList)) elif curRule.operator == TriumphKey.Operator.SUBRULE: from AllocationService import AllocationService self.workflowManager=AllocationService.getWorkflowManager() if operandList[0] == 'assignA1' or operandList[0] == 'assignA2' or operandList[0] =='assignA3': executionStack[-1].assigned=True newWorkflow=self.workflowManager.getWorkflow(operandList[0],'tech',0,'sanket') if newWorkflow is not None: result=self.runWorkflow(newWorkflow,inputLeadDataDict,executionStack) else: from Executor.Operation import Operation operandListTuple=tuple(operandList) operation=Operation(curRule.operator,operandListTuple) result=operation.operate() return result
def fetchFeatureValueForLeads(self, leadsData, agentsData):
from FeatureExec import FeatureExec
from Utils.Log import Log
from Utils.TriumphKey import TriumphKey
featureExec = FeatureExec()
self.currentLeadsData.actorDataDict = {}
leadsList = []
if leadsData is not None:
for leadIdKey in leadsData.keys():
leadsList.append(leadIdKey)
self.currentLeadsData.actorDataDict[leadIdKey] = leadsData[
leadIdKey]
self.currentAgentsData.actorDataDict = agentsData
inputProperties = {}
inputProperties[TriumphKey.FeatureFetchProperty.
dataType] = TriumphKey.FeatureType.LEAD
inputProperties[TriumphKey.FeatureFetchProperty.
inputDataField] = TriumphKey.LeadInputDataField.leadId
inputProperties[TriumphKey.FeatureFetchProperty.data] = leadsList
from FeatureManager.FeatureStore import FeatureStore
fs = FeatureStore()
featureFoundOut = []
Log.d(
ExecutionContext.TAG,
'List of all features from workflow to be fetched are ' +
str(self.featureList))
for feature in self.featureList:
featureType = fs.getFeatureType(feature)
if featureType == TriumphKey.FeatureType.AGENT or featureType == TriumphKey.FeatureType.ALLOCATION:
continue
#To avoid fetching same feature multiple times
if feature not in featureFoundOut:
featureFoundOut.append(feature)
else:
#Feature has already been found out for each lead
continue
#Now beginning to fetch feature data
featureValues4AllLeads = featureExec.fetchFeatureValue(
feature, inputProperties)
#In case sql query failed and no data was fetched
if featureValues4AllLeads is None:
continue
#Processing data lead by lead as data has been fetched for multiple leads
for featureValuesPerLead in featureValues4AllLeads:
leadPropDict = {}
#Saving data for all attributes: first argument being attribute name and second being value
for featureValTuple in featureValuesPerLead:
if featureValTuple[0] != 'leadId':
leadPropDict[str(
featureValTuple[0])] = featureValTuple[1]
else:
leadId = featureValTuple[1]
#In case of data being already present for a lead append new attributes to existing dictionaries
#else create a dictionary entry for this lead in currentLeadsData
#Skipping putting an entry for an attribute of a lead if its already present
if self.currentLeadsData.actorDataDict.has_key(leadId) is True:
prevDataTemp = self.currentLeadsData.actorDataDict[leadId]
for key in leadPropDict.keys():
if prevDataTemp.has_key(key) is False:
prevDataTemp[key] = leadPropDict[key]
else:
Log.d(
ExecutionContext.TAG,
'Entry already present in dictionary for this lead so skipping'
+ str(key))
self.currentLeadsData.actorDataDict[leadId] = prevDataTemp
else:
self.currentLeadsData.actorDataDict[leadId] = leadPropDict