def generateBehaviour():
print("Generate Behaviours")
np.random.seed(42)
random.seed(42)
countOfItems: int = 100
countOfRepetitions: int = 5
uBehavStatic08Desc: UserBehaviourDescription = UserBehaviourDescription(
observationalStaticProbabilityFnc, [0.8])
uBehavStatic06Desc: UserBehaviourDescription = UserBehaviourDescription(
observationalStaticProbabilityFnc, [0.6])
uBehavStatic04Desc: UserBehaviourDescription = UserBehaviourDescription(
observationalStaticProbabilityFnc, [0.4])
uBehavStatic02Desc: UserBehaviourDescription = UserBehaviourDescription(
observationalStaticProbabilityFnc, [0.2])
uBehavLinear0109Desc: UserBehaviourDescription = UserBehaviourDescription(
observationalLinearProbabilityFnc, [0.1, 0.9])
uBehavPowerlaw054min048: UserBehaviourDescription = UserBehaviourDescription(
observationalPowerLawFnc, [0.54, -0.48])
# ML
# BehavioursML.generateFileMl1m(countOfItems, countOfRepetitions, BehavioursML.BHVR_STATIC08, uBehavStatic08Desc)
# BehavioursML.generateFileMl1m(countOfItems, countOfRepetitions, BehavioursML.BHVR_STATIC06, uBehavStatic06Desc)
# BehavioursML.generateFileMl1m(countOfItems, countOfRepetitions, BehavioursML.BHVR_STATIC04, uBehavStatic04Desc)
BehavioursML.generateFileMl1m(countOfItems, countOfRepetitions,
BehavioursML.BHVR_STATIC02,
uBehavStatic02Desc)
def test01():
print("Simulation: ML DHontThompsonSampling")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"DHontThompsonSampling" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondtThompsonSampling(selector))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs())
eTool: AEvalTool = EvalToolDHondtBanditVotes({})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: ML TheMostPopular")
rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescTheMostPopular()
pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.THE_MOST_POPULAR.title(),
InputRecomMLDefinition.THE_MOST_POPULAR, rDescr)
batchID:str = "ml1mDiv90Ulinear0109R1"
dataset:DatasetML = DatasetML.readDatasets()
behaviourFile:str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF:DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
# remove old results
#path:str = ".." + os.sep + "results" + os.sep + batchID
#try:
# os.remove(path + os.sep + "computation-theMostPopular.txt")
# os.remove(path + os.sep + "historyOfRecommendation-theMostPopular.txt")
# os.remove(path + os.sep + "portfModelTimeEvolution-theMostPopular.txt")
#except:
# print("An exception occurred")
# simulation of portfolio
simulator:Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: ML FuzzyDHondtDirectOptimize")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FuzzyDHondtDirectOptimize" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSampling(
selector, "DCG"))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
#model:DataFrame = PModelDHont(pDescr.getRecommendersIDs())
model: DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs())
#lrClick:float = 0.03
#lrView:float = lrClick / 500
#eTool:AEvalTool = EvalToolDHondt({EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: lrClick,
# EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: lrView})
eTool: AEvalTool = EvalToolDHondtBanditVotes({})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def exportSimulatorML1M(self, batchID: str,
divisionDatasetPercentualSize: int,
uBehaviourID: str, repetition: int):
argsSimulationDict: dict = {
SimulationML.ARG_WINDOW_SIZE: 5,
SimulationML.ARG_RECOM_REPETITION_COUNT: repetition,
SimulationML.ARG_NUMBER_OF_RECOMM_ITEMS: 100,
SimulationML.ARG_NUMBER_OF_AGGR_ITEMS: self.numberOfAggrItems,
SimulationML.ARG_DIV_DATASET_PERC_SIZE:
divisionDatasetPercentualSize,
SimulationML.ARG_HISTORY_LENGTH: 10
}
# dataset reading
dataset: ADataset = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(uBehaviourID)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML,
argsSimulationDict, dataset,
behavioursDF)
return simulator
def test06():
print("Simulation: ML VMCMF")
rDescr:RecommenderDescription = InputRecomSTDefinition.exportRDescVMContextKNN()
pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomSTDefinition.VMC_KNN.title(),
InputRecomSTDefinition.VMC_KNN, rDescr)
batchID:str = "mlDiv90Ulinear0109R1"
dataset:DatasetML = DatasetML.readDatasets()
behaviourFile:str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF:DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
# simulation of portfolio
simulator:Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test02():
print("Simulation: ML W2V")
rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescW2Vtpositivei50000ws1vs32upsweightedMeanups3()
pDescr:APortfolioDescription = Portfolio1MethDescription("W2vPositiveMax",
"w2vPositiveMax", rDescr)
batchID:str = "ml1mDiv90Ulinear0109R1"
dataset:DatasetML = DatasetML.readDatasets()
behaviourFile:str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF:DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
# simulation of portfolio
simulator:Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: ML ContextDHondtINF")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
#pProbToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002(
# InputSimulatorDefinition.numberOfAggrItems)
pToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
itemsDF: DataFrame = Items.readFromFileMl1m()
usersDF: DataFrame = Users.readFromFileMl1m()
historyDF: AHistory = HistoryDF("test01")
eTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_USERS: usersDF,
EvalToolContext.ARG_ITEMS: itemsDF,
EvalToolContext.ARG_DATASET: "ml",
EvalToolContext.ARG_HISTORY: historyDF
})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextDHondtINF" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDContextHondtINF(
selector, pToolOLin0802HLin1002, eTool))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: ML FuzzyDHondtINF")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
pProbToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FuzzyDHondtINF" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondtINF(selector,
pProbToolOLin0802HLin1002))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
lrClick: float = 0.03
lrView: float = lrClick / 500
eTool: AEvalTool = EvalToolDHondt({
EvalToolDHondt.ARG_LEARNING_RATE_CLICKS:
lrClick,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS:
lrView
})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: ML ContextFuzzyDHondtDirectOptimize")
jobID: str = "Roulette1"
selector: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 1})
itemsDF: DataFrame = Items.readFromFileMl1m()
usersDF: DataFrame = Users.readFromFileMl1m()
historyDF: AHistory = HistoryHierDF("test01")
eTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_USERS: usersDF,
EvalToolContext.ARG_ITEMS: itemsDF,
EvalToolContext.ARG_DATASET: "ml",
EvalToolContext.ARG_HISTORY: historyDF
})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextFuzzyDHondtDirectOptimize" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescContextFuzzyDHondtDirectOptimize(
selector, eTool))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool], [historyDF])
def test04():
print("Simulation: ML CB")
#rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescCBmean()
rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescCosineCBcbdOHEupsmaxups1()
#pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.COS_CB_MEAN.title(),
# InputRecomMLDefinition.COS_CB_MEAN, rDescr)
pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.COS_CB_WINDOW3.title(),
InputRecomMLDefinition.COS_CB_WINDOW3, rDescr)
batchID:str = "ml1mDiv90Ulinear0109R1"
dataset:DatasetML = DatasetML.readDatasets()
behaviourFile:str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF:DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
# simulation of portfolio
simulator:Simulator = Simulator(batchID, SimulationML, argsSimulationDict, dataset, behavioursDF)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def __generateGeneralBehaviourrRR(behavioursDF: DataFrame,
numberOfItems: int,
countOfRepetitions: int,
uBehavDesc: UserBehaviourDescription):
for numberOfRepetitionI in range(countOfRepetitions):
for indexJ, rowJ in behavioursDF.iterrows():
if indexJ % 1000 == 0:
print("Generating repetition " + str(numberOfRepetitionI) +
" " + str(indexJ) + " / " +
str(behavioursDF.shape[0]))
repetitionIJ: int = rowJ[BehavioursRR.COL_REPETITION]
if numberOfRepetitionI != repetitionIJ:
continue
uBehavIJ: List[bool] = uBehavDesc.getBehaviour(numberOfItems)
strBehavIJ: str = BehavioursML.convertToString(uBehavIJ)
behavioursDF.at[indexJ,
BehavioursRR.COL_BEHAVIOUR] = strBehavIJ
def simulateRecommendations(
self, portfolios: List[APortfolio],
portfolioDescs: List[APortfolioDescription],
portFolioModels: List[DataFrame], evaluatonTools: List[AEvalTool],
histories: List[AHistory], evaluations: List[dict],
currentDFIndex: int, counterI: int, counterMax: int, userID: int,
sessionID: int, repetition: int, testRatingsDF: DataFrame,
testBehaviourDict: Dict[int, DataFrame],
windowOfItemIDsI: List[int], currentPageType: object):
COL_BEHAVIOUR: str = self._behaviourClass.getColNameBehaviour()
COL_ITEMID: str = self._ratingClass.getColNameItemID()
currentItemID: int = testRatingsDF.loc[currentDFIndex][COL_ITEMID]
print("userID: " + str(userID))
print("currentDFIndex: " + str(currentDFIndex))
print("currentItemID: " + str(currentItemID))
print("repetition: " + str(repetition))
uObservationStrI: str = testBehaviourDict[repetition].loc[
currentDFIndex][COL_BEHAVIOUR]
uObservation: List[bool] = BehavioursML.convertToListOfBoolean(
uObservationStrI)
#uObservation: List[bool] = [True]*20
print("uObservation: " + str(uObservation))
portfolioI: Portfolio1Aggr
portFolioModelI: pd.DataFrame
historyI: pd.DataFrame
for portfolioI, portfolioDescI, portFolioModelI, evaluatonToolI, historyI, evaluationI in zip(
portfolios, portfolioDescs, portFolioModels, evaluatonTools,
histories, evaluations):
self.simulateRecommendation(portfolioI, portfolioDescI,
portFolioModelI, evaluatonToolI,
historyI, evaluationI, currentDFIndex,
counterI, counterMax, testRatingsDF,
uObservation, userID, sessionID,
windowOfItemIDsI, currentPageType)