def test01():
print("Simulation: ML 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 = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelBandit(pDescr.getRecommendersIDs())
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [EvalToolBanditTS({})],
[HistoryHierDF(pDescr.getPortfolioID())])
def run(self, batchID: str, jobID: str):
from execute.generateBatches import BatchParameters #class
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = BatchParameters.getBatchParameters(
)[batchID]
selector: ADHondtSelector = self.getParameters()[jobID]
datasetID: str = "ml1m" + "Div" + str(divisionDatasetPercentualSize)
rIDs, rDescs = InputRecomDefinition.exportPairOfRecomIdsAndRecomDescrs(
datasetID)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"BanditTS" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescBanditTS(selector))
eTool: AEvalTool = EvalToolBanditTS({})
model: DataFrame = ModelDefinition.createBanditModel(
pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition.exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], HistoryHierDF)
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]
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescBanditTS(selector))
eTool: AEvalTool = EvalToolBanditTS({})
model: DataFrame = PModelBandit(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]
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 test01():
print("Test 01")
print("Running EvalToolBanditTS:")
rItemIDsWithResponsibility: List = [(1, 'metoda1'), (32, 'metoda1'),
(2, 'metoda1'), (8, 'metoda1'),
(6, 'metoda2'), (4, 'metoda1'),
(7, 'metoda3'), (5, 'metoda2'),
(64, 'metoda3'), (77, 'metoda3'),
(12, 'metoda3')]
# methods parametes
portfolioModelData: List[tuple] = [['metoda1', 5, 10, 1, 1],
['metoda2', 5, 12, 1, 1],
['metoda3', 6, 13, 1, 1]]
portfolioModelDF: DataFrame = pd.DataFrame(
portfolioModelData, columns=["methodID", "r", "n", "alpha0", "beta0"])
portfolioModelDF.set_index("methodID", inplace=True)
print("Definition:")
print(portfolioModelDF)
print()
evaluationDict: dict = {}
print("Clicked:")
EvalToolBanditTS.click(rItemIDsWithResponsibility, 1, portfolioModelDF,
evaluationDict)
print()
print("Clicked:")
EvalToolBanditTS.click(rItemIDsWithResponsibility, 32, portfolioModelDF,
evaluationDict)
print()
print("Clicked:")
EvalToolBanditTS.click(rItemIDsWithResponsibility, 6, portfolioModelDF,
evaluationDict)
print()
print("Displayed:")
rItemIDsWithResponsibility1: List[tuple] = [(1, 'metoda1'),
(32, 'metoda1'),
(2, 'metoda1'), (8, 'metoda1')]
for i in [0, 1, 2]:
EvalToolBanditTS.displayed(rItemIDsWithResponsibility1,
portfolioModelDF, evaluationDict)
print(portfolioModelDF)
def getBanditTS():
taskID:str = "Web" + "BanditTS" + "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, InputAggrDefinition.exportADescBanditTS(selector))
dataset:DatasetST = DatasetST.readDatasets()
history:AHistory = HistoryHierDF(taskID)
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelBandit(pDescr.getRecommendersIDs())
evalTool:AEvalTool = EvalToolBanditTS({})
return (taskID, port, model, evalTool, history)