def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
selector: ADHondtSelector = self.getParameters()[jobID]
portfolioID: str = self.getBatchName() + jobID
history: AHistory = HistoryHierDF(portfolioID)
dataset: ADataset = DatasetST.readDatasets()
events = dataset.eventsDF
serials = dataset.serialsDF
historyDF: AHistory = HistoryDF("test01")
# Init evalTool
evalTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_ITEMS:
serials, # ITEMS
EvalToolContext.ARG_EVENTS:
events, # EVENTS (FOR CALCULATING HISTORY OF USER)
EvalToolContext.ARG_DATASET:
"st", # WHAT DATASET ARE WE IN
EvalToolContext.ARG_HISTORY:
historyDF
}) # empty instance of AHistory is OK for ST dataset
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescContextDHont: AggregationDescription = InputAggrDefinition.exportADescDContextHondt(
selector, evalTool)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
portfolioID, rIDs, rDescs, aDescContextDHont)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [evalTool], [history])
def test21():
print("Simulation: ST ContextDHondt")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
dataset: ADataset = DatasetST.readDatasets()
events = dataset.eventsDF
serials = dataset.serialsDF
history: AHistory = HistoryHierDF("test01")
# Init evalTool
evalTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_ITEMS: serials, # ITEMS
EvalToolContext.ARG_EVENTS:
events, # EVENTS (FOR CALCULATING HISTORY OF USER)
EvalToolContext.ARG_DATASET: "st", # WHAT DATASET ARE WE IN
EvalToolContext.ARG_HISTORY: history
}) # empty instance of AHistory is OK for ST dataset
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextDHondt" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDContextHondt(selector, evalTool))
batchID: str = "stDiv90Ulinear0109R1"
dataset: DatasetST = DatasetST.readDatasets()
behaviourFile: str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursST.readFromFileST(behaviourFile)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
#print(model)
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationST, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [evalTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: ML ContextDHondt")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
itemsDF: DataFrame = Items.readFromFileMl1m()
usersDF: DataFrame = Users.readFromFileMl1m()
history: AHistory = HistoryHierDF("test01")
eTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_USERS: usersDF,
EvalToolContext.ARG_ITEMS: itemsDF,
EvalToolContext.ARG_DATASET: "ml",
EvalToolContext.ARG_HISTORY: history
})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextDHondt" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDContextHondt(selector, eTool))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def getContextFuzzyDHondt():
#taskID:str = "Web" + "ContextFuzzyDHondt" + "Roulette1"
taskID:str = "Web" + "ContextFuzzyDHondt" + "Fixed"
dataset:ADataset = DatasetST.readDatasets()
#selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
selector:ADHondtSelector = TheMostVotedItemSelector({})
#pToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
# InputSimulatorDefinition.numberOfAggrItems)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
history:AHistory = HistoryHierDF(taskID)
# Init eTool
evalTool:AEvalTool = EvalToolContext({
EvalToolContext.ARG_ITEMS: dataset.serialsDF, # ITEMS
EvalToolContext.ARG_EVENTS: dataset.eventsDF, # EVENTS (FOR CALCULATING HISTORY OF USER)
EvalToolContext.ARG_DATASET: "st", # WHAT DATASET ARE WE IN
EvalToolContext.ARG_HISTORY: history}) # empty instance of AHistory is OK for ST dataset
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDContextHondt(selector, evalTool)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
return (taskID, port, model, evalTool, history)