def run(self, batchID: str, jobID: str):
from execute.generateBatches import BatchParameters #class
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = BatchParameters.getBatchParameters(
)[batchID]
#eTool:AEvalTool
selector, nImplFeedback, eTool = self.getParameters()[jobID]
datasetID: str = "ml1m" + "Div" + str(divisionDatasetPercentualSize)
rIDs, rDescs = InputRecomDefinition.exportPairOfRecomIdsAndRecomDescrs(
datasetID)
aDescNegDHont: AggregationDescription = InputAggrDefinition.exportADescDHontINF(
selector, nImplFeedback)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FDHontINF" + jobID, rIDs, rDescs, aDescNegDHont)
model: DataFrame = ModelDefinition.createDHontModel(
pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition.exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], HistoryHierDF)
def run(self, batchID: str, jobID: str):
from execute.generateBatches import BatchParameters #class
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = BatchParameters.getBatchParameters(
)[batchID]
datasetID: str = "ml1m" + "Div" + str(divisionDatasetPercentualSize)
recommenderID: str
nImplFeedback: APenalization
recommenderID, nImplFeedback = self.getParameters()[jobID]
rDescr: RecommenderDescription = InputRecomDefinition.exportInputRecomDefinition(
recommenderID, datasetID)
pDescr: APortfolioDescription = PortfolioNeg1MethDescription(
jobID.title(), recommenderID, rDescr, nImplFeedback)
simulator: Simulator = InputSimulatorDefinition.exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [DataFrame()], [EToolSingleMethod({})],
HistoryHierDF)
def run(self, batchID: str, jobID: str):
from execute.generateBatches import BatchParameters #class
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = BatchParameters.getBatchParameters(
)[batchID]
selector: ADHondtSelector = self.getParameters()[jobID]
datasetID: str = "ml1m" + "Div" + str(divisionDatasetPercentualSize)
rIDs, rDescs = InputRecomDefinition.exportPairOfRecomIdsAndRecomDescrs(
datasetID)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"BanditTS" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescBanditTS(selector))
eTool: AEvalTool = EvalToolBanditTS({})
model: DataFrame = ModelDefinition.createBanditModel(
pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition.exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], HistoryHierDF)
def generateBatches(self):
from execute.generateBatches import BatchParameters #class
batchIDs: List[str] = BatchParameters.getBatchParameters().keys()
jobIDs: List[str] = list(self.getParameters().keys())
for batchIDI in batchIDs:
for jobIDL in jobIDs:
batchesDir: str = ".." + os.sep + "batches" + os.sep + batchIDI
if not os.path.exists(batchesDir):
os.mkdir(batchesDir)
job: str = str(self.__class__.__name__) + jobIDL
text:str = str(self.__class__.__name__) + "().run(" \
+ "'" + str(batchIDI) + "', " \
+ "'" + str(jobIDL) + "'" + ")"
jobFile: str = batchesDir + os.sep + job + ".txt"
self.__writeToFile(jobFile, text)
def run(self, batchID: str, jobID: str):
from execute.generateBatches import BatchParameters #class
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = BatchParameters.getBatchParameters(
)[batchID]
selector, nImplFeedback = self.getParameters()[jobID]
eTool: AEvalTool = EvalToolDHondt({
EvalToolDHondt.ARG_LEARNING_RATE_CLICKS:
0.02,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS:
1000
})
datasetID: str = "ml1m" + "Div" + str(divisionDatasetPercentualSize)
rIDs, rDescs = InputRecomDefinition.exportPairOfRecomIdsAndRecomDescrs(
datasetID)
aDescFuzzyHontDirectOptimizeINF: AggregationDescription = InputAggrDefinition.exportADescDFuzzyHontDirectOptimizeINF(
selector, nImplFeedback)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FuzzyDHondtDirectOptimizeINF" + jobID, rIDs, rDescs,
aDescFuzzyHontDirectOptimizeINF)
model: DataFrame = ModelDefinition.createDHontModel(
pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition.exportSimulatorML1M(
batchID, divisionDatasetPercentualSize, uBehaviour, repetition)
simulator.simulate([pDescr], [model], [eTool], HistoryHierDF)