def exportADescDHontINF(selector: ADHondtSelector,
nImplFeedback: APenalization):
aDescNegDHontFixed: AggregationDescription = AggregationDescription(
AggrFuzzyDHondtINF, {
AggrFuzzyDHondtINF.ARG_SELECTOR: selector,
AggrFuzzyDHondtINF.ARG_PENALTY_TOOL: nImplFeedback
})
return aDescNegDHontFixed
def exportADescDHontThompsonSamplingINF(selector: ADHondtSelector,
nImplFeedback: APenalization):
aDescDHontBanditVotesRoulette: AggregationDescription = AggregationDescription(
AggrDHondtThompsonSamplingINF, {
AggrDHondtThompsonSampling.ARG_SELECTOR: selector,
AggrFuzzyDHondtINF.ARG_PENALTY_TOOL: nImplFeedback
})
return aDescDHontBanditVotesRoulette
def exportADescDFuzzyHontDirectOptimizeINF(selector: ADHondtSelector,
nImplFeedback: APenalization):
aDescFuzzyDHondtDirectOptimizeINF: AggregationDescription = AggregationDescription(
AggrFuzzyDHondtDirectOptimizeINF, {
AggrFuzzyDHondtDirectOptimizeINF.ARG_SELECTOR: selector,
AggrFuzzyDHondtDirectOptimizeINF.ARG_PENALTY_TOOL:
nImplFeedback
})
return aDescFuzzyDHondtDirectOptimizeINF
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 exportADescDHontBanditVotesRoulette3():
aDescDHontBanditVotesRoulette3:AggregationDescription = AggregationDescription(AggrDHondtBanditsVotes,
{AggrDHondtBanditsVotes.ARG_SELECTORFNC:(AggrDHondtBanditsVotes.selectorOfRouletteWheelExpRatedItem,[3])})
return aDescDHontBanditVotesRoulette3
def exportADescNegDHontFixed(aPenalization:APenalization):
aDescNegDHontFixed:AggregationDescription = AggregationDescription(AggrDHontNegativeImplFeedback, {
AggrDHontNegativeImplFeedback.ARG_SELECTORFNC:(AggrDHont.selectorOfTheMostVotedItem,[]),
AggrDHontNegativeImplFeedback.ARG_PENALTY_TOOL:aPenalization})
return aDescNegDHontFixed
def exportADescRandomKfromN(mainMethodID:str):
return AggregationDescription(AggrRandomKfromN, {
AggrRandomKfromN.ARG_MAIN_METHOD: mainMethodID,
AggrRandomKfromN.ARG_MAX_REC_SIZE: 100})
def exportADescDHontRoulette3():
aDescDHontRoulette3:AggregationDescription = AggregationDescription(AggrDHont,
{AggrDHont.ARG_SELECTORFNC:(AggrDHont.selectorOfRouletteWheelExpRatedItem,[3])})
return aDescDHontRoulette3
def exportADescDHontThompsonSampling(nImplFeedback: APenalization):
aDescDHontBanditVotesRoulette: AggregationDescription = AggregationDescription(
AggrDHondtThompsonSampling,
{AggrDHondtThompsonSampling.ARG_SELECTOR: nImplFeedback})
return aDescDHontBanditVotesRoulette
def exportADescDHontDirectOptimize(selector: ADHondtSelector):
aDescFuzzyDHondtDirectOptimize: AggregationDescription = AggregationDescription(
AggrFuzzyDHondtDirectOptimize,
{AggrFuzzyDHondtDirectOptimize.ARG_SELECTOR: selector})
return aDescFuzzyDHondtDirectOptimize
def exportADescDHondtDirectOptimize(selector:ADHondtSelector,
discountFactor=AggrDHondtDirectOptimizeThompsonSampling.DISCFACTOR_UNIFORM):
return AggregationDescription(AggrFuzzyDHondtDirectOptimize, {
AggrFuzzyDHondtDirectOptimize.ARG_SELECTOR:selector,
AggrDHondtDirectOptimizeThompsonSampling.ARG_DISCOUNT_FACTOR:discountFactor})
def exportADescDHont(selector: ADHondtSelector):
aDescDHontFixed: AggregationDescription = AggregationDescription(
AggrFuzzyDHondt, {AggrFuzzyDHondt.ARG_SELECTOR: selector})
return aDescDHontFixed
def exportADescDHondt(selector:ADHondtSelector):
return AggregationDescription(AggrFuzzyDHondt, {
AggrFuzzyDHondt.ARG_SELECTOR:selector})
def exportADescDHondtThompsonSampling(nImplFeedback:APenalization):
return AggregationDescription(AggrDHondtThompsonSampling, {
AggrDHondtThompsonSampling.ARG_SELECTOR: nImplFeedback})
def exportADescFAI():
return AggregationDescription(AggrFAI, {
})
def exportADescWeightedAVGMMR():
return AggregationDescription(AggrMMRWeightedAVG, {
})
def exportADescRandomRecsSwitching(mainMethodID:str):
return AggregationDescription(AggrRandomRecsSwitching, {
AggrRandomRecsSwitching.ARG_MAIN_METHOD: mainMethodID})
def exportADescNegDHontRoulette(aPenalization:APenalization):
aDescNegDHontRoulette:AggregationDescription = AggregationDescription(AggrDHontNegativeImplFeedback, {
AggrDHontNegativeImplFeedback.ARG_SELECTORFNC:(AggrDHont.selectorOfRouletteWheelRatedItem,[]),
AggrDHontNegativeImplFeedback.ARG_PENALTY_TOOL:aPenalization})
return aDescNegDHontRoulette
def exportADescDFuzzyHondtDirectOptimizeINF(selector:ADHondtSelector, nImplFeedback:APenalization,
discountFactor=AggrDHondtDirectOptimizeThompsonSampling.DISCFACTOR_UNIFORM):
return AggregationDescription(AggrFuzzyDHondtDirectOptimizeINF, {
AggrFuzzyDHondtDirectOptimizeINF.ARG_SELECTOR:selector,
AggrFuzzyDHondtDirectOptimizeINF.ARG_PENALTY_TOOL: nImplFeedback,
AggrDHondtDirectOptimizeThompsonSampling.ARG_DISCOUNT_FACTOR:discountFactor})
def exportADescBanditTS(selector: ADHondtSelector):
aDescBanditTS: AggregationDescription = AggregationDescription(
AggrBanditTS, {AggrBanditTS.ARG_SELECTOR: selector})
return aDescBanditTS
def exportADescDHondtDirectOptimizeThompsonSamplingMMR(selector:ADHondtSelector,
discountFactor=AggrDHondtDirectOptimizeThompsonSampling.DISCFACTOR_UNIFORM):
return AggregationDescription(AggrMMRDHondtDirectOptimizeThompsonSampling, {
AggrDHondtDirectOptimizeThompsonSampling.ARG_SELECTOR:selector,
AggrDHondtDirectOptimizeThompsonSampling.ARG_DISCOUNT_FACTOR:discountFactor,
AggrMMRDHondtDirectOptimizeThompsonSampling.ARG_MMR_LAMBDA:0.5})
def exportADescContextFuzzyDHondtDirectOptimize(selector:ADHondtSelector, eTool:AEvalTool):
return AggregationDescription(AggrContextFuzzyDHondtDirectOptimize, {
AggrContextFuzzyDHondtDirectOptimize.ARG_SELECTOR:selector,
AggrContextFuzzyDHondtDirectOptimize.ARG_EVAL_TOOL:eTool})
def exportADescBanditTS():
aDescBanditTS:AggregationDescription = AggregationDescription(AggrBanditTS,
{AggrBanditTS.ARG_SELECTORFNC:(AggrBanditTS.selectorOfRouletteWheelRatedItem,[])})
return aDescBanditTS
def exportADescDContextHondt(selector:ADHondtSelector, eTool:AEvalTool):
return AggregationDescription(AggrContextFuzzyDHondt, {
AggrContextFuzzyDHondt.ARG_SELECTOR:selector,
AggrContextFuzzyDHondt.ARG_EVAL_TOOL:eTool})
def exportADescDHontFixed():
aDescDHontFixed:AggregationDescription = AggregationDescription(AggrDHont,
{AggrDHont.ARG_SELECTORFNC:(AggrDHont.selectorOfTheMostVotedItem,[])})
return aDescDHontFixed
def test01():
print("Test 01")
recommenderID: str = "TheMostPopular"
pRDescr: RecommenderDescription = RecommenderDescription(
RecommenderTheMostPopular, {})
selectorFixed: ADHondtSelector = TheMostVotedItemSelector({})
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSampling(
selectorFixed)
rIDs: List[str]
rDescs: List[AggregationDescription]
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs()
rIDs = [recommenderID]
rDescs = [pRDescr]
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: 2.0})
pHierDescr: PortfolioHierDescription = PortfolioHierDescription(
"pHierDescr", recommenderID, pRDescr, p1AggrDescrID, p1AggrDescr,
aHierDescr, pProbTool)
userID: int = 1
dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter(
minEventCount=50)
history: AHistory = HistoryDF("test")
history.insertRecommendation(userID, 45, 1, False)
history.insertRecommendation(userID, 45, 2, False)
history.insertRecommendation(userID, 78, 3, False)
p: APortfolio = pHierDescr.exportPortfolio("test", history)
portFolioModel: DataFrame = PModelDHondtBanditsVotes(
p1AggrDescr.getRecommendersIDs())
p.train(history, dataset)
#df:DataFrame = DataFrame([[1, 555]], columns=[Events.COL_USER_ID, Events.COL_OBJECT_ID])
#p.update(ARecommender.UPDT_CLICK, df)
args = {
APortfolio.ARG_NUMBER_OF_AGGR_ITEMS: 20,
APortfolio.ARG_ITEM_ID: 1,
APortfolio.ARG_NUMBER_OF_RECOMM_ITEMS: 100,
AggrD21.ARG_RATING_THRESHOLD_FOR_NEG: 0.5
}
r, rp = p.recommend(userID, portFolioModel, args)
print(r)
def exportADescContextFuzzyDHondtDirectOptimizeINF(selector:ADHondtSelector, nImplFeedback:APenalization, eTool:AEvalTool):
return AggregationDescription(AggrContextFuzzyDHondtDirectOptimizeINF, {
AggrContextFuzzyDHondtINF.ARG_SELECTOR:selector,
AggrContextFuzzyDHondtINF.ARG_EVAL_TOOL:eTool,
AggrFuzzyDHondtDirectOptimizeINF.ARG_PENALTY_TOOL: nImplFeedback})
