def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
selector, nImplFeedback = self.getParameters()[jobID]
eTool: AEvalTool = EvalToolDHondtBanditVotes({})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescNegDHontThompsonSamplingI: AggregationDescription = InputAggrDefinition.exportADescDHondtThompsonSamplingINF(
selector, nImplFeedback)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs,
aDescNegDHontThompsonSamplingI)
model: DataFrame = PModelDHondtBanditsVotes(
pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def getFuzzyDHontThompsonSamplingINF():
taskID:str = "Web" + "FuzzyDHondtThompsonSamplingINF" + "Fixed" + "OLin0802HLin1002"
selector:ADHondtSelector = TheMostVotedItemSelector({})
penalization:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002(20)
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtThompsonSamplingINF(selector, penalization)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
history:AHistory = HistoryHierDF(taskID)
dataset:ADataset = DatasetST.readDatasets()
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs())
evalTool:AEvalTool = EvalToolDHondtBanditVotes({})
return (taskID, port, model, evalTool, history)