def test02():
print("Test 02")
rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescTheMostPopular(
)
recommenderID: str = InputRecomRRDefinition.THE_MOST_POPULAR
rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescKNN()
recommenderID: str = InputRecomRRDefinition.KNN
rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescBPRMFIMPL(
)
recommenderID: str = InputRecomRRDefinition.BPRMFIMPL
rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescVMContextKNN(
)
recommenderID: str = InputRecomRRDefinition.VMC_KNN
rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescCosineCB(
)
recommenderID: str = InputRecomRRDefinition.COSINECB
rDescr: RecommenderDescription = InputRecomRRDefinition.exportRDescW2V()
recommenderID: str = InputRecomRRDefinition.W2V
pDescr: Portfolio1MethDescription = Portfolio1MethDescription(
recommenderID.title(), recommenderID, rDescr)
dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter(
minEventCount=50)
behavioursDF: DataFrame = BehavioursRR.readFromFileRR(
BehavioursRR.getFile("static08"))
history: AHistory = HistoryDF("test")
p: APortfolio = pDescr.exportPortfolio("jobID", history)
p.train(history, dataset)
argsSimulationDict: Dict[str, str] = {
SimulationRR.ARG_WINDOW_SIZE: 50,
SimulationRR.ARG_RECOM_REPETITION_COUNT: 1,
SimulationRR.ARG_NUMBER_OF_RECOMM_ITEMS: 100,
SimulationRR.ARG_NUMBER_OF_AGGR_ITEMS:
InputSimulatorDefinition.numberOfAggrItems,
SimulationRR.ARG_DIV_DATASET_PERC_SIZE: 90,
SimulationRR.ARG_HISTORY_LENGTH: 10
}
# simulation of portfolio
simulator: Simulator = Simulator("test", SimulationRR, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})],
[HistoryHierDF("a")])
def test12():
print("Test 12")
print("Running Recommender BPRMF on RR:")
from datasets.retailrocket.events import Events # class
batchID: str = "batchID"
trainDataset: ADataset
testDataset: ADataset
trainDataset, testDataset = DatasetRetailRocket.readDatasetsWithFilter(
50).divideDataset(90)
testUserIDs: ndarray = testDataset.eventsDF[Events.COL_VISITOR_ID].unique()
history: AHistory = HistoryHierDF(["aa"])
numberOfItems: int = 20
rd: RecommenderDescription = InputRecomRRDefinition.exportRDescBPRMFIMPL()
#rd:RecommenderDescription = InputRecomRRDefinition.exportRDescBPRMF()
#rd:RecommenderDescription = InputRecomRRDefinition.exportRDescTheMostPopular()
rd: RecommenderDescription = InputRecomRRDefinition.exportRDescKNN()
#rd:RecommenderDescription = InputRecomRRDefinition.exportRDescCosineCBcbdOHEupsweightedMeanups3()
#rd:RecommenderDescription = InputRecomRRDefinition.exportRDescW2Vtpositivei50000ws1vs32upsweightedMeanups3()
#rd:RecommenderDescription = InputRecomRRDefinition.exportRDescW2Vtpositivei50000ws1vs64upsweightedMeanups7()
r: ARecommender = rd.exportRecommender("aaa")
argumentsDict: Dict = rd.getArguments()
r.train(history, trainDataset)
numberOfHit: int = 0
for userIdI in testUserIDs[0:500]:
recI: Series = r.recommend(int(userIdI), numberOfItems, argumentsDict)
recItemIDsI: List[int] = [i for i in recI.keys()]
windowItemIds: List[int] = testDataset.eventsDF.loc[
testDataset.eventsDF[Events.COL_VISITOR_ID] == userIdI][
Events.COL_ITEM_ID].unique()
itemIdsHitted: List[int] = list(set(recItemIDsI) & set(windowItemIds))
numberOfHit += len(itemIdsHitted)
print("")
print("numberOfHit: " + str(numberOfHit))
counter += len(intersectionI)
print(" counter: " + str(counter))
if __name__ == "__main__":
os.chdir("..")
dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter(
minEventCount=50)
rDscrTheMostPopular: RecommenderDescription = InputRecomRRDefinition.exportRDescTheMostPopular(
)
rDscrItemBasedKNN: RecommenderDescription = InputRecomRRDefinition.exportRDescKNN(
)
rDscrBPRMF: RecommenderDescription = InputRecomRRDefinition.exportRDescBPRMFIMPL(
)
rDscrVMContextKNN: RecommenderDescription = InputRecomRRDefinition.exportRDescVMContextKNN(
)
rDscrCosineCB: RecommenderDescription = InputRecomRRDefinition.exportRDescCosineCB(
)
rDscrW2V: RecommenderDescription = InputRecomRRDefinition.exportRDescW2V()
rDscrs: List[object] = [
rDscrTheMostPopular, rDscrItemBasedKNN, rDscrBPRMF, rDscrVMContextKNN,
rDscrCosineCB, rDscrW2V
]
rDscrs: List[object] = [rDscrW2V]
for rDscrI in rDscrs:
se: StaticEvaluation = StaticEvaluation(dataset)
se.evaluate(rDscrI)