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 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 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 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 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 test01():
print("Test 01")
recommenderID: str = "TheMostPopular"
pRDescr: RecommenderDescription = RecommenderDescription(
RecommenderTheMostPopular, {})
selectorFixed: ADHondtSelector = TheMostVotedItemSelector({})
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSampling(
selectorFixed)
rIDs: List[str]
rDescs: List[AggregationDescription]
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs()
rIDs = [recommenderID]
rDescs = [pRDescr]
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: 2.0})
pHierDescr: PortfolioHierDescription = PortfolioHierDescription(
"pHierDescr", recommenderID, pRDescr, p1AggrDescrID, p1AggrDescr,
aHierDescr, pProbTool)
userID: int = 1
dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter(
minEventCount=50)
history: AHistory = HistoryDF("test")
history.insertRecommendation(userID, 45, 1, False)
history.insertRecommendation(userID, 45, 2, False)
history.insertRecommendation(userID, 78, 3, False)
p: APortfolio = pHierDescr.exportPortfolio("test", history)
portFolioModel: DataFrame = PModelDHondtBanditsVotes(
p1AggrDescr.getRecommendersIDs())
p.train(history, dataset)
#df:DataFrame = DataFrame([[1, 555]], columns=[Events.COL_USER_ID, Events.COL_OBJECT_ID])
#p.update(ARecommender.UPDT_CLICK, df)
args = {
APortfolio.ARG_NUMBER_OF_AGGR_ITEMS: 20,
APortfolio.ARG_ITEM_ID: 1,
APortfolio.ARG_NUMBER_OF_RECOMM_ITEMS: 100,
AggrD21.ARG_RATING_THRESHOLD_FOR_NEG: 0.5
}
r, rp = p.recommend(userID, portFolioModel, args)
print(r)
