def getBatchName(self): return "SingleBPRMFHT" def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] rDescr: RecommenderDescription = self.getParameters()[jobID] recommenderID: str = "BPRMF" + jobID pDescr: APortfolioDescription = Portfolio1MethDescription( recommenderID.title(), recommenderID, rDescr) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRSingleBPRMFHT().generateAllBatches(InputABatchDefinition())
InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] # eTool:AEvalTool selector, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondt( selector) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) model: DataFrame = PModelDHondtPersonalised( pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTPersonalFuzzyDHondt().generateAllBatches(InputABatchDefinition())
}) aDict[keyI] = rCBI return aDict def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] rDescr: RecommenderDescription = self.getParameters()[jobID] recommenderID: str = jobID pDescr: APortfolioDescription = Portfolio1MethDescription( recommenderID.title(), recommenderID, rDescr) simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLSingleCosineCBHT.generateAllBatches(InputABatchDefinition())
repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] eTool: AEvalTool = EToolDoNothing({}) rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescWeightedFAI: AggregationDescription = InputAggrDefinition.exportADescFAI( ) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( "FAI" + jobID, rIDs, rDescs, aDescWeightedFAI) model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRFAI().generateAllBatches(InputABatchDefinition())
paramsDict: Dict[str, object] = BatchDefMLSingleW2VHT.getParameters() BatchDefMLSingleW2VHT.trainVariants = oldValue return paramsDict def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] rDescr: RecommenderDescription = self.getParameters()[jobID] recommenderID: str = jobID pDescr: APortfolioDescription = Portfolio1MethDescription( recommenderID.title(), recommenderID, rDescr) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTSingleW2VHT.generateAllBatches(InputABatchDefinition())
def run(self, batchID:str, jobID:str): divisionDatasetPercentualSize:int uBehaviour:str repetition:int divisionDatasetPercentualSize, uBehaviour, repetition = \ InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] # eTool:AEvalTool selector, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondt(selector) pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) model:DataFrame = PModelDHondtPersonalisedStat(pDescr.getRecommendersIDs()) simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLPersonalStatFuzzyDHondt().generateAllBatches(InputABatchDefinition())
def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = \ InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] # eTool:AEvalTool selector, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondt(selector) pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition().exportSimulatorRetailRocket( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRFuzzyDHondt().generateAllBatches(InputABatchDefinition())
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.exportADescDContextHondtINF( selector, negativeImplFeedback, 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]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTContextDHondtINF.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] eTool: AEvalTool = EToolDoNothing({}) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs( ) mainMethodID: str = "RecomW2Vtalli100000Ws1Vs32Upsmaxups1" # the best method for ST aDescRandomKfromN: AggregationDescription = InputAggrDefinition.exportADescRandomKfromN( mainMethodID) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescRandomKfromN) model: DataFrame = DataFrame() simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTRandomKfromN.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] eTool: AEvalTool = EToolDoNothing({}) rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs( ) mainMethodID: str = "RecomKnn" # the best method for ML aDescRandomRecsSwitching: AggregationDescription = InputAggrDefinition.exportADescRandomRecsSwitching( mainMethodID) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescRandomRecsSwitching) model: DataFrame = DataFrame() simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLRandomRecsSwitching.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize:int uBehaviour:str repetition:int divisionDatasetPercentualSize, uBehaviour, repetition = \ InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] # eTool:AEvalTool selector, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondt(selector) pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) rIds:List[str] = pDescr.getRecommendersIDs() model:DataFrame = PModelHybrid(PModelDHondt(rIds), PModelDHondtPersonalisedStat(rIds)) simulator:Simulator = InputSimulatorDefinition().exportSimulatorSlantour( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTHybridStat().generateAllBatches(InputABatchDefinition())
repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] #eTool:AEvalTool selector, nImplFeedback, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescNegDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtINF( selector, nImplFeedback) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescNegDHont) model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLFuzzyDHondtINF().generateAllBatches(InputABatchDefinition())
# 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.exportADescContextFuzzyDHondtDirectOptimizeINF(selector, nImplFeedback, 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())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTContextFuzzyDHondtDirectOptimizeINF.generateAllBatches(InputABatchDefinition()) #BatchSTContextFuzzyDHondtDirectOptimizeINF().run("stDiv90Ulinear0109R1", "Fixed")
divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] selector: ADHondtSelector = self.getParameters()[jobID] rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs( ) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, InputAggrDefinition.exportADescBanditTS(selector)) eTool: AEvalTool = EvalToolBanditTS({}) model: DataFrame = PModelBandit(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLBanditTS.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize:int uBehaviour:str repetition:int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] selector, nImplFeedback = self.getParameters()[jobID] eTool:AEvalTool = EvalToolDHondtBanditVotes({}) rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSamplingINF(selector, nImplFeedback) pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID.replace("Uniform",""), rIDs, rDescs, aDescDHont) model:DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs()) simulator:Simulator = InputSimulatorDefinition().exportSimulatorRetailRocket( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRFuzzyDHondtDirectOptimizeThompsonSamplingINF().generateAllBatches(InputABatchDefinition())
def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = \ InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] eTool:AEvalTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescWeightedAVG:AggregationDescription = InputAggrDefinition.exportADescWeightedAVGMMR() pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescWeightedAVG) model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator:Simulator = InputSimulatorDefinition().exportSimulatorSlantour( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTWeightedAVGMMR.generateAllBatches(InputABatchDefinition())
def run(self, batchID:str, jobID:str): divisionDatasetPercentualSize:int uBehaviour:str repetition:int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] kI:str = self.getParameters()[jobID] recommenderID:str = "RecommendervmContextKNN" + "K" + str(kI) rVMCtKNN:RecommenderDescription = RecommenderDescription(RecommenderVMContextKNN, { RecommenderVMContextKNN.ARG_K: kI}) pDescr:APortfolioDescription = Portfolio1MethDescription(recommenderID.title(), recommenderID, rVMCtKNN) simulator:Simulator = InputSimulatorDefinition().exportSimulatorSlantour( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") #print(os.getcwd()) BatchDefSTSingleVMContextKNNHT.generateAllBatches(InputABatchDefinition())
usersDF:DataFrame = Users.readFromFileMl1m() eTool:AEvalTool = EvalToolContext({ EvalToolContext.ARG_USERS: usersDF, EvalToolContext.ARG_ITEMS: itemsDF, EvalToolContext.ARG_DATASET: "ml", EvalToolContext.ARG_HISTORY: history}) rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescContextDHont:AggregationDescription = InputAggrDefinition.exportADescDContextHondt(selector, eTool) pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( portfolioID, rIDs, rDescs, aDescContextDHont) model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [history]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLContextDHondt.generateAllBatches(InputABatchDefinition())
) p1AggrDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( "FDHont" + jobID, rIDs, rDescs, InputAggrDefinition.exportADescDHondt(selector)) recommenderID: str = "TheMostPopular" rDescr: RecommenderDescription = RecommenderDescription( RecommenderTheMostPopular, {}) pDescr: APortfolioDescription = PortfolioDynamicDescription( "Dynamic" + "FDHontPersStat" + jobID, recommenderID, rDescr, "FDHondt", p1AggrDescr) model: DataFrame = PModelDHondtPersonalisedStat( p1AggrDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRDynamic().generateAllBatches(InputABatchDefinition())
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondt( selector) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) rIds: List[str] = pDescr.getRecommendersIDs() model: DataFrame = PModelHybrid( PModelDHondt(rIds), PModelDHondtPersonalisedStat(rIds), { PModelHybrid.ARG_MODE_SKIP: True, PModelHybrid.ARG_SKIP_CLICK_THRESHOLD: 3 }) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRHybrid().generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize:int uBehaviour:str repetition:int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] #eTool:AEvalTool selector, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimize(selector) 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], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTFuzzyDHondtDirectOptimize.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] eTool: AEvalTool selector, nImplFeedback, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescDHontINF: AggregationDescription = InputAggrDefinition.exportADescDHondtINF( selector, nImplFeedback) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHontINF) model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefSTFuzzyDHondtINF.generateAllBatches(InputABatchDefinition())
}) aDict[keyI] = rBPRMFI return aDict def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] rDescr: RecommenderDescription = self.getParameters()[jobID] recommenderID: str = jobID pDescr: APortfolioDescription = Portfolio1MethDescription( recommenderID.title(), recommenderID, rDescr) simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLSingleBPRMFHT.generateAllBatches(InputABatchDefinition())
selector, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondt( selector) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) rIds: List[str] = pDescr.getRecommendersIDs() model: DataFrame = PModelHybrid( PModelDHondt(rIds), PModelDHondtPersonalisedStat(rIds), { PModelHybrid.ARG_MODE_SKIP: True, PModelHybrid.ARG_SKIP_CLICK_THRESHOLD: 3 }) simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLHybridSkip().generateAllBatches(InputABatchDefinition())
selector, discFactor = self.getParameters()[jobID] eTool: AEvalTool = EvalToolDHondtBanditVotes({}) rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs( ) aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSamplingMMR( selector, discFactor) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) model: DataFrame = PModelDHondtBanditsVotes( pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLFuzzyDHondtDirectOptimizeThompsonSamplingMMR.generateAllBatches( InputABatchDefinition())
divisionDatasetPercentualSize:int uBehaviour:str repetition:int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] eTool:AEvalTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs() aDescWeightedAVG:AggregationDescription = InputAggrDefinition.exportADescWeightedAVG() pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( "WAVG" + jobID, rIDs, rDescs, aDescWeightedAVG) model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefMLWeightedAVG.generateAllBatches(InputABatchDefinition())
object] = BatchDefMLSingleCosineCBHT.getParameters() BatchDefMLSingleCosineCBHT.cbDataPaths = oldValue return paramsDict def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] rDescr: RecommenderDescription = self.getParameters()[jobID] recommenderID: str = "CosineCB" + jobID pDescr: APortfolioDescription = Portfolio1MethDescription( recommenderID.title(), recommenderID, rDescr) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRSingleCosineCBHT().generateAllBatches(InputABatchDefinition()) #BatchDefRRSingleCosineCBHT().run('rrDiv90Ulinear0109R1', 'cbdOHEupsmaxups1')
uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = \ InputABatchDefinition().getBatchParameters(self.datasetID)[batchID] selector: ADHondtSelector = self.getParameters()[jobID] rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs( ) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, InputAggrDefinition.exportADescBanditTS(selector)) eTool: AEvalTool = EvalToolBanditTS({}) model: DataFrame = PModelBandit(pDescr.getRecommendersIDs()) simulator: Simulator = InputSimulatorDefinition( ).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())]) if __name__ == "__main__": os.chdir("..") os.chdir("..") print(os.getcwd()) BatchDefRRBanditTS().generateAllBatches(InputABatchDefinition()) #BatchDefRRBanditTS().run('rrDiv90Ulinear0109R1', 'Fixed')