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 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 = PModelDHondtPersonalised(
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
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 getFuzzyDHont():
#taskID:str = "Web" + "FuzzyDHondt" + "Roulette1"
taskID:str = "Web" + "FuzzyDHondt" + "Fixed"
dataset:ADataset = DatasetST.readDatasets()
#selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
selector:ADHondtSelector = TheMostVotedItemSelector({})
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondt(selector)
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 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 test01():
print("Simulation: ML FuzzyDHondt")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FuzzyDHondt" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondt(selector))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelDHondt(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
})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[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())])