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 getNegativeImplFeedbackParameters_():
pToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition().numberOfAggrItems)
pToolOStat08HLin1002: APenalization = PenalizationToolDefinition.exportPenaltyToolOStat08HLin1002(
InputSimulatorDefinition().numberOfAggrItems)
pProbToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002(
InputSimulatorDefinition().numberOfAggrItems)
pProbToolOStat08HLin1002: APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition().numberOfAggrItems)
pToolFilterBord3Lengt100: APenalization = PenalizationToolDefinition.exportPenaltyToolFiltering(
)
aDict: Dict[str, object] = {}
aDict["OLin0802HLin1002"] = pToolOLin0802HLin1002
aDict["OStat08HLin1002"] = pToolOStat08HLin1002
aDict["ProbOLin0802HLin1002"] = pProbToolOLin0802HLin1002
aDict["ProbOStat08HLin1002"] = pProbToolOStat08HLin1002
aDict["TFilterBord3Lengt100"] = pToolFilterBord3Lengt100
return aDict
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 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 = PModelDHondtPersonalised(
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 = 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, 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 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 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 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]
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 = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs()
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescContextFuzzyDHondtDirectOptimize(selector, eTool)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHont)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M(
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
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 run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = \
InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
eTool: AEvalTool = self.getParameters()[jobID]
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescWeightedAVG: AggregationDescription = InputAggrDefinition.exportADescWeightedAVGMMR(
)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"WAVGMMR" + jobID, rIDs, rDescs, aDescWeightedAVG)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
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]
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 run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
selector: ADHondtSelector
selector, negativeImplFeedback = self.getParameters()[jobID]
portfolioID: str = self.getBatchName() + jobID
history: AHistory = HistoryHierDF(portfolioID)
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(
)
aDescContextDHont: AggregationDescription = InputAggrDefinition.exportADescDContextHondtINF(
selector, negativeImplFeedback, evalTool)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
portfolioID, rIDs, rDescs, aDescContextDHont)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [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: ADHondtSelector
negativeImplFeedback: APenalization
selector, negativeImplFeedback = self.getParameters()[jobID]
portfolioID: str = self.getBatchName() + jobID
history: AHistory = HistoryHierDF(portfolioID)
dataset: ADataset = DatasetML.readDatasets()
usersDF = dataset.usersDF
itemsDF = dataset.itemsDF
# Init evalTool
evalTool: 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.exportADescDContextHondtINF(
selector, negativeImplFeedback, evalTool)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
portfolioID, rIDs, rDescs, aDescContextDHont)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [evalTool], [history])
def run(self, batchID:str, jobID:str):
divisionDatasetPercentualSize:int
uBehaviour:str
repetition:int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
kI:str = self.getParameters()[jobID]
recommenderID:str = "RecommendervmContextKNN" + "K" + str(kI)
rVMCtKNN:RecommenderDescription = RecommenderDescription(RecommenderVMContextKNN, {
RecommenderVMContextKNN.ARG_K: kI})
pDescr:APortfolioDescription = Portfolio1MethDescription(recommenderID.title(), recommenderID, rVMCtKNN)
simulator:Simulator = InputSimulatorDefinition().exportSimulatorSlantour(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
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]
datasetID:str = "ml1m" + "Div" + str(divisionDatasetPercentualSize)
recommenderID:str
nImplFeedback:APenalization
recommenderID, nImplFeedback = self.getParameters()[jobID]
rDescr:RecommenderDescription = InputRecomMLDefinition.exportInputRecomDefinition(recommenderID)
pDescr:APortfolioDescription = PortfolioNeg1MethDescription(jobID.title(), recommenderID, rDescr, nImplFeedback)
simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
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]
selector, nImplFeedback = self.getParameters()[jobID]
eTool:AEvalTool = EvalToolDHondt({EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: 0.02,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: 1000})
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs()
aDescFuzzyHontDirectOptimizeINF:AggregationDescription = InputAggrDefinition.exportADescDFuzzyHondtDirectOptimizeINF(selector, nImplFeedback)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescFuzzyHontDirectOptimizeINF)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M(
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]
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescBanditTS(selector))
eTool: AEvalTool = EvalToolBanditTS({})
model: DataFrame = PModelBandit(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])