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)
def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str repetition: int divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition( ).getBatchParameters(self.datasetID)[batchID] 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())])
def test01(): print("Simulation: ML TheMostPopular") rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescTheMostPopular() pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.THE_MOST_POPULAR.title(), InputRecomMLDefinition.THE_MOST_POPULAR, rDescr) batchID:str = "ml1mDiv90Ulinear0109R1" dataset:DatasetML = DatasetML.readDatasets() behaviourFile:str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109) behavioursDF:DataFrame = BehavioursML.readFromFileMl1m(behaviourFile) # remove old results #path:str = ".." + os.sep + "results" + os.sep + batchID #try: # os.remove(path + os.sep + "computation-theMostPopular.txt") # os.remove(path + os.sep + "historyOfRecommendation-theMostPopular.txt") # os.remove(path + os.sep + "portfModelTimeEvolution-theMostPopular.txt") #except: # print("An exception occurred") # simulation of portfolio simulator:Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
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( ) 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())])
def getFuzzyDHontINF(): #taskID:str = "Web" + "FuzzyDHondtINF" + "Roulette1" taskID:str = "Web" + "FuzzyDHondt" + "Fixed" dataset:ADataset = DatasetST.readDatasets() #selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1}) selector:ADHondtSelector = TheMostVotedItemSelector({}) pToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002( InputSimulatorDefinition.numberOfAggrItems) aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtINF(selector, pToolOLin0802HLin1002) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription( taskID, rIDs, rDescs, aDescDHont) history:AHistory = HistoryHierDF(taskID) port:APortfolio = pDescr.exportPortfolio(taskID, history) port.train(history, dataset) model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs()) evalTool:AEvalTool = EvalToolDHondt({EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: 0.03, EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: 0.03 / 500}) return (taskID, port, model, evalTool, history)
def test03(): print("Test 03") history1: AHistory = HistoryHierDF("databse1") for i in range(100): # userID, itemID, position, observation, clicked history1.insertRecommendation(1, i, 1, 0.5, False) history1.insertRecommendation(2, i, 1, 0.5, True) history1.insertRecommendation(3, i, 1, 0.5, True) count1: int = history1.getInteractionCount(1, 1000) print("count1: " + str(count1)) history1.delete(50) count2: int = history1.getInteractionCount(1, 1000) print("count2: " + str(count2)) history1.deletePreviousRecomOfUser(1, 10) count3: int = history1.getInteractionCount(1, 1000) print("count3: " + str(count3)) history1.print()
def test21(): print("Simulation: ST 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 = "stDiv90Ulinear0109R1" dataset: DatasetST = DatasetST.readDatasets() behaviourFile: str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109) behavioursDF: DataFrame = BehavioursST.readFromFileST(behaviourFile) model: DataFrame = PModelBandit(pDescr.getRecommendersIDs()) # simulation of portfolio simulator: Simulator = Simulator(batchID, SimulationST, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [model], [EvalToolBanditTS({})], [HistoryHierDF(pDescr.getPortfolioID())])
def getContextFuzzyDHondtDirectOptimizeINF(): # taskID:str = "Web" + "ContextFuzzyDHondtDirectOptimizeINF" + "Roulette1" taskID:str = "Web" + "ContextFuzzyDHondtDirectOptimizeINF" + "Fixed" dataset:ADataset = DatasetST.readDatasets() # selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1}) selector:ADHondtSelector = TheMostVotedItemSelector({}) pToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002( 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.exportADescDContextHondtDirectOptimizeINF(selector, pToolOLin0802HLin1002, 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)
def test01(): print("Simulation: ML FuzzyDHondtDirectOptimize") jobID: str = "Roulette1" selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1}) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( "FuzzyDHondtDirectOptimize" + jobID, rIDs, rDescs, InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSampling( selector, "DCG")) batchID: str = "ml1mDiv90Ulinear0109R1" dataset: DatasetML = DatasetML.readDatasets() behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109) behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile) #model:DataFrame = PModelDHont(pDescr.getRecommendersIDs()) model: DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs()) #lrClick:float = 0.03 #lrView:float = lrClick / 500 #eTool:AEvalTool = EvalToolDHondt({EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: lrClick, # EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: lrView}) eTool: AEvalTool = EvalToolDHondtBanditVotes({}) # simulation of portfolio simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF) 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] #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 = PModelDHondt(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] 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())])
def test21(): print("Simulation: ST FuzzyDHondtDirectOptimize") jobID: str = "Roulette1" selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 3}) pProbToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002( InputSimulatorDefinition.numberOfAggrItems) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( "FuzzyDHondtDirectOptimize" + jobID, rIDs, rDescs, InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSamplingINF( selector, pProbToolOLin0802HLin1002, "DCG")) batchID: str = "stDiv90Ulinear0109R1" dataset: DatasetST = DatasetST.readDatasets() behaviourFile: str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109) behavioursDF: DataFrame = BehavioursST.readFromFileST(behaviourFile) model: DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs()) #lrClick:float = 0.03 #lrView:float = lrClick / 500 #eTool:AEvalTool = EvalToolDHondt({EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: lrClick, # EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: lrView}) eTool: AEvalTool = EvalToolDHondtBanditVotes({}) # simulation of portfolio simulator: Simulator = Simulator(batchID, SimulationST, argsSimulationDict, dataset, behavioursDF) 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] portfolioID: str = self.getBatchName() + jobID history: AHistory = HistoryHierDF(portfolioID) #eTool:AEvalTool selector, eTool = self.getParameters()[jobID] rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrsCluster( ) aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondt( selector) pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( self.getBatchName() + jobID, rIDs, rDescs, aDescDHont) print(pDescr.getRecommendersIDs()) model: DataFrame = PModelDHondtPersonalised( pDescr.getRecommendersIDs()) inputSimulatorDefinition = InputSimulatorDefinition() inputSimulatorDefinition.numberOfAggrItems = 100 simulator: Simulator = inputSimulatorDefinition.exportSimulatorML1M( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [model], [eTool], [history])
def test03(): print("Test 03") print("Running Recommender BPRMF on ST:") batchID: str = "batchID" dataset: ADataset = DatasetST.readDatasets() history: AHistory = HistoryHierDF(["aa"]) argumentsDict: Dict[str, object] = { RecommenderBPRMF.ARG_EPOCHS: 2, RecommenderBPRMF.ARG_FACTORS: 10, RecommenderBPRMF.ARG_LEARNINGRATE: 0.05, RecommenderBPRMF.ARG_UREGULARIZATION: 0.0025, RecommenderBPRMF.ARG_BREGULARIZATION: 0, RecommenderBPRMF.ARG_PIREGULARIZATION: 0.0025, RecommenderBPRMF.ARG_NIREGULARIZATION: 0.00025 } r: ARecommender = RecommenderBPRMF(batchID, argumentsDict) r.train(history, dataset) numberOfItems: int = 20 userId: int = 62302 res = r.recommend(userId, numberOfItems, argumentsDict) print(res) userId: int = 3462303 res = r.recommend(userId, numberOfItems, argumentsDict) print(res)
def test02(): print("Test 02") print("Running Recommender BPRMF on RR:") batchID: str = "batchID" dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter(50) history: AHistory = HistoryHierDF(["aa"]) argumentsDict: Dict[str, object] = { RecommenderBPRMF.ARG_EPOCHS: 2, RecommenderBPRMF.ARG_FACTORS: 10, RecommenderBPRMF.ARG_LEARNINGRATE: 0.05, RecommenderBPRMF.ARG_UREGULARIZATION: 0.0025, RecommenderBPRMF.ARG_BREGULARIZATION: 0, RecommenderBPRMF.ARG_PIREGULARIZATION: 0.0025, RecommenderBPRMF.ARG_NIREGULARIZATION: 0.00025 } r: ARecommender = RecommenderBPRMF(batchID, argumentsDict) r.train(history, dataset) numberOfItems: int = 20 userId: int = 1118731 res = r.recommend(userId, numberOfItems, argumentsDict) print(res) userId: int = 85734 res = r.recommend(userId, numberOfItems, argumentsDict) print(res)
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) 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())])
def test01(): print("Simulation: ML DHontThompsonSampling") jobID: str = "Roulette1" selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1}) rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs() pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription( "DHontThompsonSampling" + jobID, rIDs, rDescs, InputAggrDefinition.exportADescDHondtThompsonSampling(selector)) batchID: str = "ml1mDiv90Ulinear0109R1" dataset: DatasetML = DatasetML.readDatasets() behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109) behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile) model: DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs()) eTool: AEvalTool = EvalToolDHondtBanditVotes({}) # simulation of portfolio simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF) 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] portfolioID:str = self.getBatchName() + jobID history:AHistory = HistoryHierDF(portfolioID) itemsDF:DataFrame = Items.readFromFileMl1m() 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])
def run(self, batchID: str, jobID: str): divisionDatasetPercentualSize: int uBehaviour: str 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())])
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 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 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 test02(): print("Test 02") rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescTheMostPopular( ) recommenderID: str = InputRecomRRDefinition.THE_MOST_POPULAR rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescKNN() recommenderID: str = InputRecomRRDefinition.KNN rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescBPRMFIMPL( ) recommenderID: str = InputRecomRRDefinition.BPRMFIMPL rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescVMContextKNN( ) recommenderID: str = InputRecomRRDefinition.VMC_KNN rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescCosineCB( ) recommenderID: str = InputRecomRRDefinition.COSINECB rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescW2V() recommenderID: str = InputRecomRRDefinition.W2V pDescr: Portfolio1MethDescription = Portfolio1MethDescription( recommenderID.title(), recommenderID, rDescr) dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter( minEventCount=50) behavioursDF: DataFrame = BehavioursRR.readFromFileRR( BehavioursRR.getFile("static08")) history: AHistory = HistoryDF("test") p: APortfolio = pDescr.exportPortfolio("jobID", history) p.train(history, dataset) argsSimulationDict: Dict[str, str] = { SimulationRR.ARG_WINDOW_SIZE: 50, SimulationRR.ARG_RECOM_REPETITION_COUNT: 1, SimulationRR.ARG_NUMBER_OF_RECOMM_ITEMS: 100, SimulationRR.ARG_NUMBER_OF_AGGR_ITEMS: InputSimulatorDefinition.numberOfAggrItems, SimulationRR.ARG_DIV_DATASET_PERC_SIZE: 90, SimulationRR.ARG_HISTORY_LENGTH: 10 } # simulation of portfolio simulator: Simulator = Simulator("test", SimulationRR, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF("a")])
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 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 = "W2V" + jobID pDescr:APortfolioDescription = Portfolio1MethDescription(recommenderID.title(), recommenderID, rDescr) simulator:Simulator = InputSimulatorDefinition().exportSimulatorRetailRocket( batchID, divisionDatasetPercentualSize, uBehaviour, repetition) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test01(): print("Simulation: RR Dynamic") lrClick: float = 0.03 #lrView:float = lrClick / 300 lrViewDivisor: float = 250 jobID: str = "Fixed" + "Clk" + str(lrClick).replace( ".", "") + "ViewDivisor" + str(lrViewDivisor).replace(".", "") selector: ADHondtSelector = TheMostVotedItemSelector({}) rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs() 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) batchID: str = "rrDiv90Ulinear0109R1" dataset: DatasetRetailRocket = DatasetRetailRocket.readDatasetsWithFilter( minEventCount=50) behaviourFile: str = BehavioursRR.getFile(BehavioursRR.BHVR_LINEAR0109) behavioursDF: DataFrame = BehavioursRR.readFromFileRR(behaviourFile) model: DataFrame = PModelDHondtPersonalisedStat( p1AggrDescr.getRecommendersIDs()) eTool: AEvalTool = EvalToolDHondtPersonal({ EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: lrClick, EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: lrClick / lrViewDivisor }) # simulation of portfolio simulator: Simulator = Simulator(batchID, SimulationRR, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())])
def test11(): print("Simulation: RR TheMostPopular") rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescTheMostPopular() pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.THE_MOST_POPULAR.title(), InputRecomMLDefinition.THE_MOST_POPULAR, rDescr) batchID:str = "retailrocketDiv90Ulinear0109R1" dataset:DatasetRetailRocket = DatasetRetailRocket.readDatasets() behaviourFile:str = BehavioursRR.getFile(BehavioursRR.BHVR_LINEAR0109) behavioursDF:DataFrame = BehavioursRR.readFromFileRR(behaviourFile) # simulation of portfolio simulator:Simulator = Simulator(batchID, SimulationRR, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test26(): print("Simulation: ST VMCMF") rDescr:RecommenderDescription = InputRecomSTDefinition.exportRDescVMContextKNN() pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomSTDefinition.VMC_KNN.title(), InputRecomSTDefinition.VMC_KNN, rDescr) batchID:str = "slantourDiv90Ulinear0109R1" dataset:DatasetST = DatasetST.readDatasets() behaviourFile:str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109) behavioursDF:DataFrame = BehavioursST.readFromFileST(behaviourFile) # simulation of portfolio simulator:Simulator = Simulator(batchID, SimulationST, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test03(): print("Simulation: ML KNN") rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescKNN() pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.KNN.title(), InputRecomMLDefinition.KNN, rDescr) batchID:str = "ml1mDiv90Ulinear0109R1" dataset:DatasetML = DatasetML.readDatasets() behaviourFile:str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109) behavioursDF:DataFrame = BehavioursML.readFromFileMl1m(behaviourFile) # simulation of portfolio simulator:Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test24(): print("Simulation: ST CB") rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescCosineCBcbdOHEupsweightedMeanups3() pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.COS_CB_MEAN.title(), InputRecomMLDefinition.COS_CB_MEAN, rDescr) batchID:str = "slantourDiv90Ulinear0109R1" dataset:DatasetST = DatasetST.readDatasets() behaviourFile:str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109) behavioursDF:DataFrame = BehavioursST.readFromFileST(behaviourFile) # simulation of portfolio simulator:Simulator = Simulator(batchID, SimulationST, argsSimulationDict, dataset, behavioursDF) simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])