def test01(): print("Simulation: ML BanditTS") jobID: str = "BanditTS" selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1}) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( "BanditTS" + jobID, rIDs, rDescs, InputAggrDefinition.exportADescBanditTS(selector)) batchID: str = "ml1mDiv90Ulinear0109R1" dataset: DatasetML = DatasetML.readDatasets() behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109) behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile) model: DataFrame = PModelBandit(pDescr.getRecommendersIDs()) # simulation of portfolio simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [model], [EvalToolBanditTS({})], [HistoryHierDF(pDescr.getPortfolioID())])
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] rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs( ) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, InputAggrDefinition.exportADescBanditTS(selector)) eTool: AEvalTool = EvalToolBanditTS({}) model: DataFrame = PModelBandit(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())])
def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = \ InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] recommenderTheMPopID: str = "TheMostPopular" pRTheMPopDescr: RecommenderDescription = RecommenderDescription( RecommenderTheMostPopular, {}) recommenderRPID: str = "RepeatedPurchase" pRecRPDescr: RecommenderDescription = RecommenderDescription( RecommenderRepeatedPurchase, {}) selector: ADHondtSelector = self.getParameters()[jobID] aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSampling( selector) aDescDHont: AggregationDescription = InputAggrDefinition.exportADescBanditTS( selector) #aDescDHont:AggregationDescription = InputAggrDefinition.exportADescFAI() rIDs: List[str] rDescs: List[AggregationDescription] rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs( ) #rIDs = [recommenderTheMPopID] #rDescs = [pRTheMPopDescr] p1AggrDescrID: str = "p1AggrDescrID" p1AggrDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( p1AggrDescrID, rIDs, rDescs, aDescDHont) #pProbTool:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002( # InputSimulatorDefinition().numberOfAggrItems) pProbTool: APenalization = PenalizationToolDefinition.exportPenaltyToolOStat08HLin1002( InputSimulatorDefinition().numberOfAggrItems) aHierDescr: AggregationDescription = AggregationDescription( AggrD21, {AggrD21.ARG_RATING_THRESHOLD_FOR_NEG: 0.0}) pHierDescr: PortfolioHierDescription = PortfolioHierDescription( "pHierDescr", recommenderRPID, pRecRPDescr, p1AggrDescrID, p1AggrDescr, aHierDescr, pProbTool) eTool: AEvalTool = EvalToolBanditTS({}) #eTool:AEvalTool = EToolDoNothing({}) #model:DataFrame = PModelDHont(p1AggrDescr.getRecommendersIDs()) model: DataFrame = PModelBandit(p1AggrDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pHierDescr], [model], [eTool], [HistoryHierDF(p1AggrDescr.getPortfolioID())])
def getBanditTS(): taskID:str = "Web" + "BanditTS" + "Roulette1" selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1}) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( taskID, rIDs, rDescs, InputAggrDefinition.exportADescBanditTS(selector)) dataset:DatasetST = DatasetST.readDatasets() history:AHistory = HistoryHierDF(taskID) port:APortfolio = pDescr.exportPortfolio(taskID, history) port.train(history, dataset) model:DataFrame = PModelBandit(pDescr.getRecommendersIDs()) evalTool:AEvalTool = EvalToolBanditTS({}) return (taskID, port, model, evalTool, history)