def test02():
print("Test 02")
print("Running RecommenderCosineCB RR:")
dataset: DatasetST = DatasetRetailRocket.readDatasets()
args: dict = {
RecommenderCosineCB.ARG_CB_DATA_PATH:
Configuration.cbRRDataFileWithPathOHE,
RecommenderCosineCB.ARG_USER_PROFILE_SIZE: 5,
RecommenderCosineCB.ARG_USER_PROFILE_STRATEGY: "max",
RecommenderCosineCB.ARG_USE_DIVERSITY: False,
} #True
rec: ARecommender = RecommenderCosineCB("test", args)
rec.train(HistoryDF("test"), dataset)
eventsDFDFUpdate: DataFrame = dataset.eventsDF.iloc[5003:5004]
print(eventsDFDFUpdate)
rec.update(eventsDFDFUpdate, args)
# user with very outdated profile - no recent objects
r: Series = rec.recommend(863743, 20, args)
print(type(r))
print(r)
# testing of a non-existent user
r: Series = rec.recommend(10000, 50, args)
print(type(r))
print(r)
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())])