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 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]
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 = 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())])
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 = 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 getParameters(self):
rIDs, rDescr = InputRecomMLDefinition.exportPairOfRecomIdsAndRecomDescrs()
rID1:str = "Recom" + "CosineCBcbdOHEupsweightedMeanups3" + "MMR05"
rID2:str = "Recom" + "CosineCBcbdOHEupsmaxups1" + "MMR05"
cosineCBMMR1:RecommenderDescription = InputRecomMLDefinition.exportRDescCosineCBcbdOHEupsweightedMeanups3()
cosineCBMMR1.getArguments()[RecommenderCosineCB.ARG_USE_DIVERSITY] = True
cosineCBMMR1.getArguments()[RecommenderCosineCB.ARG_MMR_LAMBDA] = 0.5
cosineCBMMR2:RecommenderDescription = InputRecomMLDefinition.exportRDescCosineCBcbdOHEupsmaxups1()
cosineCBMMR2.getArguments()[RecommenderCosineCB.ARG_USE_DIVERSITY] = True
cosineCBMMR2.getArguments()[RecommenderCosineCB.ARG_MMR_LAMBDA] = 0.5
aDict:Dict[str,object] = dict(zip(rIDs + [rID1, rID2], rDescr + [cosineCBMMR1, cosineCBMMR2]))
return aDict
def exportPairOfRecomIdsAndRecomDescrsRetailRocket(cls):
recom: str = "Recom"
rIDs: List[str] = [
recom + InputRecomMLDefinition.THE_MOST_POPULAR.title()
]
rDescs: List[RecommenderDescription] = [
InputRecomMLDefinition.exportRDescTheMostPopular()
]
return (rIDs, rDescs)
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 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 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 test25():
print("Simulation: ST MF")
rDescr:RecommenderDescription = InputRecomMLDefinition.exportRDescBPRMFIMPLf100i10lr0003r01()
pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomMLDefinition.BPRMFIMPLf100i10lr0003r01.title(),
InputRecomMLDefinition.BPRMFIMPLf100i10lr0003r01, 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 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())])
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 test11():
print("Test 11")
print("Running Recommender BPRMF on ML:")
batchID: str = "batchID"
trainDataset: ADataset
testDataset: ADataset
trainDataset, testDataset = DatasetML.readDatasets().divideDataset(90)
testUserIDs: ndarray = testDataset.ratingsDF[Ratings.COL_USERID].unique()
history: AHistory = HistoryHierDF(["aa"])
numberOfItems: int = 20
rd: RecommenderDescription = InputRecomMLDefinition.exportRDescBPRMF()
#rd:RecommenderDescription = InputRecomMLDefinition.exportRDescBPRMFIMPLf20i20lr0003r01()
#rd:RecommenderDescription = InputRecomMLDefinition.exportRDescTheMostPopular()
#rd:RecommenderDescription = InputRecomMLDefinition.exportRDescKNN()
#rd:RecommenderDescription = InputRecomMLDefinition.exportRDescCosineCBcbdOHEupsweightedMeanups3()
#rd:RecommenderDescription = InputRecomMLDefinition.exportRDescW2Vtpositivei50000ws1vs32upsweightedMeanups3()
#rd:RecommenderDescription = InputRecomMLDefinition.exportRDescW2Vtpositivei50000ws1vs64upsweightedMeanups7()
r: ARecommender = rd.exportRecommender("aaa")
argumentsDict: Dict = rd.getArguments()
r.train(history, trainDataset)
numberOfHit: int = 0
for userIdI in testUserIDs:
recI: Series = r.recommend(int(userIdI), numberOfItems, argumentsDict)
recItemIDsI: List[int] = [i for i in recI.keys()]
windowItemIds: List[int] = testDataset.ratingsDF.loc[
testDataset.ratingsDF[Ratings.COL_USERID] == userIdI][
Ratings.COL_MOVIEID].unique()
itemIdsHitted: List[int] = list(set(recItemIDsI) & set(windowItemIds))
numberOfHit += len(itemIdsHitted)
print("")
print("numberOfHit: " + str(numberOfHit))
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 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 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())])