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] itemsDF:DataFrame = Items.readFromFileMl1m() usersDF:DataFrame = Users.readFromFileMl1m() historyDF:AHistory = HistoryHierDF("test01") eTool:AEvalTool = EvalToolContext({ EvalToolContext.ARG_USERS: usersDF, EvalToolContext.ARG_ITEMS: itemsDF, EvalToolContext.ARG_DATASET: "ml", EvalToolContext.ARG_HISTORY: historyDF}) rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescDHont:AggregationDescription = InputAggrDefinition.exportADescContextFuzzyDHondtDirectOptimize(selector, eTool) pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M( 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] selector: ADHondtSelector = self.getParameters()[jobID] dataset: ADataset = DatasetST.readDatasets() events = dataset.eventsDF serials = dataset.serialsDF historyDF: 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: historyDF }) # empty instance of AHistory is OK for ST dataset rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescDHont: AggregationDescription = InputAggrDefinition.exportADescContextFuzzyDHondtDirectOptimize( selector, evalTool) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [evalTool], [HistoryHierDF(pDescr.getPortfolioID())])
def test01(): print("Simulation: ML ContextFuzzyDHondtDirectOptimize") jobID: str = "Roulette1" selector: ADHondtSelector = RouletteWheelSelector( {RouletteWheelSelector.ARG_EXPONENT: 1}) itemsDF: DataFrame = Items.readFromFileMl1m() usersDF: DataFrame = Users.readFromFileMl1m() historyDF: AHistory = HistoryHierDF("test01") eTool: AEvalTool = EvalToolContext({ EvalToolContext.ARG_USERS: usersDF, EvalToolContext.ARG_ITEMS: itemsDF, EvalToolContext.ARG_DATASET: "ml", EvalToolContext.ARG_HISTORY: historyDF }) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( "ContextFuzzyDHondtDirectOptimize" + jobID, rIDs, rDescs, InputAggrDefinition.exportADescContextFuzzyDHondtDirectOptimize( 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], [historyDF])