def getFuzzyDHontThompsonSamplingINF():
taskID:str = "Web" + "FuzzyDHondtThompsonSamplingINF" + "Fixed" + "OLin0802HLin1002"
selector:ADHondtSelector = TheMostVotedItemSelector({})
penalization:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002(20)
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtThompsonSamplingINF(selector, penalization)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
history:AHistory = HistoryHierDF(taskID)
dataset:ADataset = DatasetST.readDatasets()
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs())
evalTool:AEvalTool = EvalToolDHondtBanditVotes({})
return (taskID, port, model, evalTool, history)
def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
selector, discFactor = self.getParameters()[jobID]
eTool: AEvalTool = EvalToolDHondtBanditVotes({})
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSamplingMMR(
selector, discFactor)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHont)
model: DataFrame = PModelDHondtBanditsVotes(
pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
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 run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = \
InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
# eTool:AEvalTool
selector, eTool = self.getParameters()[jobID]
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
p1AggrDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FDHont" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondt(selector))
recommenderID: str = "TheMostPopular"
rDescr: RecommenderDescription = RecommenderDescription(
RecommenderTheMostPopular, {})
pDescr: APortfolioDescription = PortfolioDynamicDescription(
"Dynamic" + "FDHontPersStat" + jobID, recommenderID, rDescr,
"FDHondt", p1AggrDescr)
model: DataFrame = PModelDHondtPersonalisedStat(
p1AggrDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def getFuzzyDHontINF():
#taskID:str = "Web" + "FuzzyDHondtINF" + "Roulette1"
taskID:str = "Web" + "FuzzyDHondt" + "Fixed"
dataset:ADataset = DatasetST.readDatasets()
#selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
selector:ADHondtSelector = TheMostVotedItemSelector({})
pToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtINF(selector, pToolOLin0802HLin1002)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
history:AHistory = HistoryHierDF(taskID)
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
evalTool:AEvalTool = EvalToolDHondt({EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: 0.03,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: 0.03 / 500})
return (taskID, port, model, evalTool, history)
def test03():
print("Test 03")
history1: AHistory = HistoryHierDF("databse1")
for i in range(100):
# userID, itemID, position, observation, clicked
history1.insertRecommendation(1, i, 1, 0.5, False)
history1.insertRecommendation(2, i, 1, 0.5, True)
history1.insertRecommendation(3, i, 1, 0.5, True)
count1: int = history1.getInteractionCount(1, 1000)
print("count1: " + str(count1))
history1.delete(50)
count2: int = history1.getInteractionCount(1, 1000)
print("count2: " + str(count2))
history1.deletePreviousRecomOfUser(1, 10)
count3: int = history1.getInteractionCount(1, 1000)
print("count3: " + str(count3))
history1.print()
def test21():
print("Simulation: ST BanditTS")
jobID: str = "BanditTS"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"BanditTS" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescBanditTS(selector))
batchID: str = "stDiv90Ulinear0109R1"
dataset: DatasetST = DatasetST.readDatasets()
behaviourFile: str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursST.readFromFileST(behaviourFile)
model: DataFrame = PModelBandit(pDescr.getRecommendersIDs())
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationST, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [EvalToolBanditTS({})],
[HistoryHierDF(pDescr.getPortfolioID())])
def getContextFuzzyDHondtDirectOptimizeINF():
# taskID:str = "Web" + "ContextFuzzyDHondtDirectOptimizeINF" + "Roulette1"
taskID:str = "Web" + "ContextFuzzyDHondtDirectOptimizeINF" + "Fixed"
dataset:ADataset = DatasetST.readDatasets()
# selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
selector:ADHondtSelector = TheMostVotedItemSelector({})
pToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
history:AHistory = HistoryHierDF(taskID)
# Init eTool
evalTool:AEvalTool = EvalToolContext({
EvalToolContext.ARG_ITEMS: dataset.serialsDF, # ITEMS
EvalToolContext.ARG_EVENTS: dataset.eventsDF, # EVENTS (FOR CALCULATING HISTORY OF USER)
EvalToolContext.ARG_DATASET: "st", # WHAT DATASET ARE WE IN
EvalToolContext.ARG_HISTORY: history}) # empty instance of AHistory is OK for ST dataset
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDContextHondtDirectOptimizeINF(selector, pToolOLin0802HLin1002, evalTool)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
return (taskID, port, model, evalTool, history)
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 run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
#eTool:AEvalTool
selector, eTool = self.getParameters()[jobID]
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondt(
selector)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHont)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
eTool: AEvalTool = EToolDoNothing({})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
mainMethodID: str = "RecomW2Vtalli100000Ws1Vs32Upsmaxups1" # the best method for ST
aDescRandomKfromN: AggregationDescription = InputAggrDefinition.exportADescRandomKfromN(
mainMethodID)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescRandomKfromN)
model: DataFrame = DataFrame()
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def test21():
print("Simulation: ST FuzzyDHondtDirectOptimize")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 3})
pProbToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FuzzyDHondtDirectOptimize" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSamplingINF(
selector, pProbToolOLin0802HLin1002, "DCG"))
batchID: str = "stDiv90Ulinear0109R1"
dataset: DatasetST = DatasetST.readDatasets()
behaviourFile: str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursST.readFromFileST(behaviourFile)
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, SimulationST, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
portfolioID: str = self.getBatchName() + jobID
history: AHistory = HistoryHierDF(portfolioID)
#eTool:AEvalTool
selector, eTool = self.getParameters()[jobID]
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrsCluster(
)
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondt(
selector)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHont)
print(pDescr.getRecommendersIDs())
model: DataFrame = PModelDHondtPersonalised(
pDescr.getRecommendersIDs())
inputSimulatorDefinition = InputSimulatorDefinition()
inputSimulatorDefinition.numberOfAggrItems = 100
simulator: Simulator = inputSimulatorDefinition.exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], [history])
def test03():
print("Test 03")
print("Running Recommender BPRMF on ST:")
batchID: str = "batchID"
dataset: ADataset = DatasetST.readDatasets()
history: AHistory = HistoryHierDF(["aa"])
argumentsDict: Dict[str, object] = {
RecommenderBPRMF.ARG_EPOCHS: 2,
RecommenderBPRMF.ARG_FACTORS: 10,
RecommenderBPRMF.ARG_LEARNINGRATE: 0.05,
RecommenderBPRMF.ARG_UREGULARIZATION: 0.0025,
RecommenderBPRMF.ARG_BREGULARIZATION: 0,
RecommenderBPRMF.ARG_PIREGULARIZATION: 0.0025,
RecommenderBPRMF.ARG_NIREGULARIZATION: 0.00025
}
r: ARecommender = RecommenderBPRMF(batchID, argumentsDict)
r.train(history, dataset)
numberOfItems: int = 20
userId: int = 62302
res = r.recommend(userId, numberOfItems, argumentsDict)
print(res)
userId: int = 3462303
res = r.recommend(userId, numberOfItems, argumentsDict)
print(res)
def test02():
print("Test 02")
print("Running Recommender BPRMF on RR:")
batchID: str = "batchID"
dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter(50)
history: AHistory = HistoryHierDF(["aa"])
argumentsDict: Dict[str, object] = {
RecommenderBPRMF.ARG_EPOCHS: 2,
RecommenderBPRMF.ARG_FACTORS: 10,
RecommenderBPRMF.ARG_LEARNINGRATE: 0.05,
RecommenderBPRMF.ARG_UREGULARIZATION: 0.0025,
RecommenderBPRMF.ARG_BREGULARIZATION: 0,
RecommenderBPRMF.ARG_PIREGULARIZATION: 0.0025,
RecommenderBPRMF.ARG_NIREGULARIZATION: 0.00025
}
r: ARecommender = RecommenderBPRMF(batchID, argumentsDict)
r.train(history, dataset)
numberOfItems: int = 20
userId: int = 1118731
res = r.recommend(userId, numberOfItems, argumentsDict)
print(res)
userId: int = 85734
res = r.recommend(userId, numberOfItems, argumentsDict)
print(res)
def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = \
InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
# eTool:AEvalTool
selector, eTool = self.getParameters()[jobID]
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondt(
selector)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHont)
rIds: List[str] = pDescr.getRecommendersIDs()
model: DataFrame = PModelHybrid(
PModelDHondt(rIds), PModelDHondtPersonalisedStat(rIds), {
PModelHybrid.ARG_MODE_SKIP: True,
PModelHybrid.ARG_SKIP_CLICK_THRESHOLD: 3
})
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
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 run(self, batchID:str, jobID:str):
divisionDatasetPercentualSize:int
uBehaviour:str
repetition:int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
selector:ADHondtSelector = self.getParameters()[jobID]
portfolioID:str = self.getBatchName() + jobID
history:AHistory = HistoryHierDF(portfolioID)
itemsDF:DataFrame = Items.readFromFileMl1m()
usersDF:DataFrame = Users.readFromFileMl1m()
eTool:AEvalTool = EvalToolContext({
EvalToolContext.ARG_USERS: usersDF,
EvalToolContext.ARG_ITEMS: itemsDF,
EvalToolContext.ARG_DATASET: "ml",
EvalToolContext.ARG_HISTORY: history})
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs()
aDescContextDHont:AggregationDescription = InputAggrDefinition.exportADescDContextHondt(selector, eTool)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
portfolioID, rIDs, rDescs, aDescContextDHont)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], [history])
def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
eTool: AEvalTool = EToolDoNothing({})
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescWeightedFAI: AggregationDescription = InputAggrDefinition.exportADescFAI(
)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FAI" + jobID, rIDs, rDescs, aDescWeightedFAI)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
selector: ADHondtSelector = self.getParameters()[jobID]
dataset: ADataset = DatasetST.readDatasets()
events = dataset.eventsDF
serials = dataset.serialsDF
historyDF: AHistory = HistoryHierDF("test01")
# Init evalTool
evalTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_ITEMS:
serials, # ITEMS
EvalToolContext.ARG_EVENTS:
events, # EVENTS (FOR CALCULATING HISTORY OF USER)
EvalToolContext.ARG_DATASET:
"st", # WHAT DATASET ARE WE IN
EvalToolContext.ARG_HISTORY:
historyDF
}) # empty instance of AHistory is OK for ST dataset
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescContextFuzzyDHondtDirectOptimize(
selector, evalTool)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHont)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [evalTool],
[HistoryHierDF(pDescr.getPortfolioID())])
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 test21():
print("Simulation: ST ContextDHondt")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
dataset: ADataset = DatasetST.readDatasets()
events = dataset.eventsDF
serials = dataset.serialsDF
history: AHistory = HistoryHierDF("test01")
# Init evalTool
evalTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_ITEMS: serials, # ITEMS
EvalToolContext.ARG_EVENTS:
events, # EVENTS (FOR CALCULATING HISTORY OF USER)
EvalToolContext.ARG_DATASET: "st", # WHAT DATASET ARE WE IN
EvalToolContext.ARG_HISTORY: history
}) # empty instance of AHistory is OK for ST dataset
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextDHondt" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDContextHondt(selector, evalTool))
batchID: str = "stDiv90Ulinear0109R1"
dataset: DatasetST = DatasetST.readDatasets()
behaviourFile: str = BehavioursST.getFile(BehavioursST.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursST.readFromFileST(behaviourFile)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
#print(model)
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationST, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [evalTool],
[HistoryHierDF(pDescr.getPortfolioID())])
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 test01():
print("Simulation: ML ContextDHondt")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
itemsDF: DataFrame = Items.readFromFileMl1m()
usersDF: DataFrame = Users.readFromFileMl1m()
history: AHistory = HistoryHierDF("test01")
eTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_USERS: usersDF,
EvalToolContext.ARG_ITEMS: itemsDF,
EvalToolContext.ARG_DATASET: "ml",
EvalToolContext.ARG_HISTORY: history
})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextDHondt" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDContextHondt(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],
[HistoryHierDF(pDescr.getPortfolioID())])
def run(self, batchID:str, jobID:str):
divisionDatasetPercentualSize:int
uBehaviour:str
repetition:int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
rDescr:RecommenderDescription = self.getParameters()[jobID]
recommenderID:str = "W2V" + jobID
pDescr:APortfolioDescription = Portfolio1MethDescription(recommenderID.title(), recommenderID, rDescr)
simulator:Simulator = InputSimulatorDefinition().exportSimulatorRetailRocket(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})], [HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: RR Dynamic")
lrClick: float = 0.03
#lrView:float = lrClick / 300
lrViewDivisor: float = 250
jobID: str = "Fixed" + "Clk" + str(lrClick).replace(
".", "") + "ViewDivisor" + str(lrViewDivisor).replace(".", "")
selector: ADHondtSelector = TheMostVotedItemSelector({})
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs()
p1AggrDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FDHont" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondt(selector))
recommenderID: str = "TheMostPopular"
rDescr: RecommenderDescription = RecommenderDescription(
RecommenderTheMostPopular, {})
pDescr: APortfolioDescription = PortfolioDynamicDescription(
"Dynamic" + "FDHontPersStat" + jobID, recommenderID, rDescr, "FDHondt",
p1AggrDescr)
batchID: str = "rrDiv90Ulinear0109R1"
dataset: DatasetRetailRocket = DatasetRetailRocket.readDatasetsWithFilter(
minEventCount=50)
behaviourFile: str = BehavioursRR.getFile(BehavioursRR.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursRR.readFromFileRR(behaviourFile)
model: DataFrame = PModelDHondtPersonalisedStat(
p1AggrDescr.getRecommendersIDs())
eTool: AEvalTool = EvalToolDHondtPersonal({
EvalToolDHondt.ARG_LEARNING_RATE_CLICKS:
lrClick,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS:
lrClick / lrViewDivisor
})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationRR, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
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())])
def test26():
print("Simulation: ST VMCMF")
rDescr:RecommenderDescription = InputRecomSTDefinition.exportRDescVMContextKNN()
pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomSTDefinition.VMC_KNN.title(),
InputRecomSTDefinition.VMC_KNN, 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 test03():
print("Simulation: ML KNN")
rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescKNN()
pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.KNN.title(),
InputRecomMLDefinition.KNN, 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 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())])
