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())])
def getParameters(self):
rIDs, rDescr = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs()
rID1:str = "Recom" + "CosineCBcbdOHEupsweightedMeanups3" + "MMR05"
rID2:str = "Recom" + "CosineCBcbdOHEupsmaxups1" + "MMR05"
cosineCBMMR1:RecommenderDescription = InputRecomMLDefinition.exportRDescCosineCBcbdOHEupsweightedMeanups3()
cosineCBMMR1.getArguments()[RecommenderCosineCB.ARG_USE_DIVERSITY] = True
cosineCBMMR1.getArguments()[RecommenderCosineCB.ARG_MMR_LAMBDA] = 0.5
cosineCBMMR2:RecommenderDescription = InputRecomMLDefinition.exportRDescCosineCBcbdOHEupsmaxups1()
cosineCBMMR2.getArguments()[RecommenderCosineCB.ARG_USE_DIVERSITY] = True
cosineCBMMR2.getArguments()[RecommenderCosineCB.ARG_MMR_LAMBDA] = 0.5
aDict:Dict[str,object] = dict(zip(rIDs + [rID1, rID2], rDescr + [cosineCBMMR1, cosineCBMMR2]))
return aDict