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)