def getBatchName(self):
return "SingleBPRMFHT"
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 = "BPRMF" + jobID
pDescr: APortfolioDescription = Portfolio1MethDescription(
recommenderID.title(), recommenderID, rDescr)
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRSingleBPRMFHT().generateAllBatches(InputABatchDefinition())
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTPersonalFuzzyDHondt().generateAllBatches(InputABatchDefinition())
})
aDict[keyI] = rCBI
return aDict
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 = jobID
pDescr: APortfolioDescription = Portfolio1MethDescription(
recommenderID.title(), recommenderID, rDescr)
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLSingleCosineCBHT.generateAllBatches(InputABatchDefinition())
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRFAI().generateAllBatches(InputABatchDefinition())
paramsDict: Dict[str, object] = BatchDefMLSingleW2VHT.getParameters()
BatchDefMLSingleW2VHT.trainVariants = oldValue
return paramsDict
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 = jobID
pDescr: APortfolioDescription = Portfolio1MethDescription(
recommenderID.title(), recommenderID, rDescr)
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTSingleW2VHT.generateAllBatches(InputABatchDefinition())
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)
model:DataFrame = PModelDHondtPersonalisedStat(pDescr.getRecommendersIDs())
simulator:Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLPersonalStatFuzzyDHondt().generateAllBatches(InputABatchDefinition())
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()
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondt(selector)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHont)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition().exportSimulatorRetailRocket(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRFuzzyDHondt().generateAllBatches(InputABatchDefinition())
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])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTContextDHondtINF.generateAllBatches(InputABatchDefinition())
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTRandomKfromN.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
eTool: AEvalTool = EToolDoNothing({})
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
mainMethodID: str = "RecomKnn" # the best method for ML
aDescRandomRecsSwitching: AggregationDescription = InputAggrDefinition.exportADescRandomRecsSwitching(
mainMethodID)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs,
aDescRandomRecsSwitching)
model: DataFrame = DataFrame()
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLRandomRecsSwitching.generateAllBatches(InputABatchDefinition())
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()
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))
simulator:Simulator = InputSimulatorDefinition().exportSimulatorSlantour(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTHybridStat().generateAllBatches(InputABatchDefinition())
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
#eTool:AEvalTool
selector, nImplFeedback, eTool = self.getParameters()[jobID]
rIDs, rDescs = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescNegDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtINF(
selector, nImplFeedback)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescNegDHont)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLFuzzyDHondtINF().generateAllBatches(InputABatchDefinition())
# 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.exportADescContextFuzzyDHondtDirectOptimizeINF(selector, nImplFeedback, 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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTContextFuzzyDHondtDirectOptimizeINF.generateAllBatches(InputABatchDefinition())
#BatchSTContextFuzzyDHondtDirectOptimizeINF().run("stDiv90Ulinear0109R1", "Fixed")
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLBanditTS.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize:int
uBehaviour:str
repetition:int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
selector, nImplFeedback = self.getParameters()[jobID]
eTool:AEvalTool = EvalToolDHondtBanditVotes({})
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs()
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSamplingINF(selector, nImplFeedback)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID.replace("Uniform",""), rIDs, rDescs, aDescDHont)
model:DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs())
simulator:Simulator = InputSimulatorDefinition().exportSimulatorRetailRocket(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRFuzzyDHondtDirectOptimizeThompsonSamplingINF().generateAllBatches(InputABatchDefinition())
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 = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
aDescWeightedAVG:AggregationDescription = InputAggrDefinition.exportADescWeightedAVGMMR()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescWeightedAVG)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator:Simulator = InputSimulatorDefinition().exportSimulatorSlantour(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], [HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTWeightedAVGMMR.generateAllBatches(InputABatchDefinition())
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
#print(os.getcwd())
BatchDefSTSingleVMContextKNNHT.generateAllBatches(InputABatchDefinition())
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])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLContextDHondt.generateAllBatches(InputABatchDefinition())
)
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRDynamic().generateAllBatches(InputABatchDefinition())
rIDs, rDescs = InputRecomRRDefinition.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(
).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRHybrid().generateAllBatches(InputABatchDefinition())
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.exportADescDHondtDirectOptimize(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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTFuzzyDHondtDirectOptimize.generateAllBatches(InputABatchDefinition())
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
eTool: AEvalTool
selector, nImplFeedback, eTool = self.getParameters()[jobID]
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescDHontINF: AggregationDescription = InputAggrDefinition.exportADescDHondtINF(
selector, nImplFeedback)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs, aDescDHontINF)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefSTFuzzyDHondtINF.generateAllBatches(InputABatchDefinition())
})
aDict[keyI] = rBPRMFI
return aDict
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 = jobID
pDescr: APortfolioDescription = Portfolio1MethDescription(
recommenderID.title(), recommenderID, rDescr)
simulator: Simulator = InputSimulatorDefinition().exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLSingleBPRMFHT.generateAllBatches(InputABatchDefinition())
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLHybridSkip().generateAllBatches(InputABatchDefinition())
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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLFuzzyDHondtDirectOptimizeThompsonSamplingMMR.generateAllBatches(
InputABatchDefinition())
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.exportADescWeightedAVG()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
"WAVG" + 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())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefMLWeightedAVG.generateAllBatches(InputABatchDefinition())
object] = BatchDefMLSingleCosineCBHT.getParameters()
BatchDefMLSingleCosineCBHT.cbDataPaths = oldValue
return paramsDict
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 = "CosineCB" + jobID
pDescr: APortfolioDescription = Portfolio1MethDescription(
recommenderID.title(), recommenderID, rDescr)
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRSingleCosineCBHT().generateAllBatches(InputABatchDefinition())
#BatchDefRRSingleCosineCBHT().run('rrDiv90Ulinear0109R1', 'cbdOHEupsmaxups1')
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = \
InputABatchDefinition().getBatchParameters(self.datasetID)[batchID]
selector: ADHondtSelector = self.getParameters()[jobID]
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
self.getBatchName() + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescBanditTS(selector))
eTool: AEvalTool = EvalToolBanditTS({})
model: DataFrame = PModelBandit(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorRetailRocket(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
if __name__ == "__main__":
os.chdir("..")
os.chdir("..")
print(os.getcwd())
BatchDefRRBanditTS().generateAllBatches(InputABatchDefinition())
#BatchDefRRBanditTS().run('rrDiv90Ulinear0109R1', 'Fixed')
