def getSelectorParameters(self):
selectorRoulette1: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 1})
selectorRoulette2: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 2})
selectorRoulette3: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 3})
selectorRoulette4: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 4})
selectorRoulette5: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 5})
selectorFixed: ADHondtSelector = TheMostVotedItemSelector({})
aDict: Dict[str, object] = {}
aDict[BatchDefMLFuzzyDHondtDirectOptimize.
SLCTR_ROULETTE1] = selectorRoulette1
aDict[BatchDefMLFuzzyDHondtDirectOptimize.
SLCTR_ROULETTE2] = selectorRoulette2
aDict[BatchDefMLFuzzyDHondtDirectOptimize.
SLCTR_ROULETTE3] = selectorRoulette3
aDict[BatchDefMLFuzzyDHondtDirectOptimize.
SLCTR_ROULETTE4] = selectorRoulette4
aDict[BatchDefMLFuzzyDHondtDirectOptimize.
SLCTR_ROULETTE5] = selectorRoulette5
aDict[BatchDefMLFuzzyDHondtDirectOptimize.SLCTR_FIXED] = selectorFixed
aSubDict: Dict[str, object] = {
selIdI: aDict[selIdI]
for selIdI in aDict.keys() if selIdI in self.selectorIds
}
return aSubDict
def getSelectorParameters(self):
selectorRoulette1: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 1})
selectorRoulette3: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 3})
selectorFixed: ADHondtSelector = TheMostVotedItemSelector({})
aDict: dict = {}
aDict[BatchFuzzyDHondt.SLCTR_ROULETTE1] = selectorRoulette1
aDict[BatchFuzzyDHondt.SLCTR_ROULETTE2] = selectorRoulette3
aDict[BatchFuzzyDHondt.SLCTR_FIXED] = selectorFixed
return aDict
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 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 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 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 test01():
print("Test 01")
print("Running AggrBanditTSRun:")
# number of recommended items
N = 120
# method results, items=[1,2,4,5,6,7,8,12,32,64,77]
methodsResultDict:dict = {
"metoda1": pd.Series([0.2, 0.1, 0.3, 0.3, 0.1], [32, 2, 8, 1, 4], name="rating"),
"metoda2": pd.Series([0.1, 0.1, 0.2, 0.3, 0.3], [1, 5, 32, 6, 7], name="rating"),
"metoda3": pd.Series([0.3, 0.1, 0.2, 0.3, 0.1], [7, 2, 77, 64, 12], name="rating")
}
# print(methodsResultDict)
# methods parametes
methodsParamsData:List[tuple] = [['metoda1', 5, 10, 1, 1], ['metoda2', 5, 12, 1, 1], ['metoda3', 6, 13, 1, 1]]
methodsParamsDF:DataFrame = pd.DataFrame(methodsParamsData, columns=["methodID", "r", "n", "alpha0", "beta0"])
methodsParamsDF.set_index("methodID", inplace=True)
# print(methodsParamsDF)
aggr:AggrBanditTS = AggrBanditTS(HistoryDF(""), {AggrBanditTS.ARG_SELECTOR:RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT:1})})
itemIDs:List[tuple] = aggr.runWithResponsibility(methodsResultDict, methodsParamsDF, N)
#itemIDs:List[tuple] = aggr.run(methodsResultDict, methodsParamsDF, N)
print(itemIDs)
def selectorOfRouletteWheelExpRatedItem(votesOfCandidatesDict: dict,
exp: int):
vcDict: dict = dict(
map(lambda mIdJ: (mIdJ, votesOfCandidatesDict[mIdJ]**exp),
votesOfCandidatesDict.keys()))
votesOfCandidatesSer: Series = Series(vcDict, index=vcDict.keys())
return RouletteWheelSelector.run(votesOfCandidatesSer)
def getAllSelectors(self):
selectorRoulette1:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT:1})
selectorRoulette2:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT:2})
selectorRoulette3:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT:3})
selectorRoulette4:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT:4})
selectorRoulette5:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT:5})
selectorFixed:ADHondtSelector = TheMostVotedItemSelector({})
aDict:Dict[str,object] = {}
aDict[BatchDefMLFuzzyDHondt.SLCTR_ROULETTE1] = selectorRoulette1
aDict[BatchDefMLFuzzyDHondt.SLCTR_ROULETTE2] = selectorRoulette2
aDict[BatchDefMLFuzzyDHondt.SLCTR_ROULETTE3] = selectorRoulette3
aDict[BatchDefMLFuzzyDHondt.SLCTR_ROULETTE4] = selectorRoulette4
aDict[BatchDefMLFuzzyDHondt.SLCTR_ROULETTE5] = selectorRoulette5
aDict[BatchDefMLFuzzyDHondt.SLCTR_FIXED] = selectorFixed
return aDict
def test21():
print("Simulation: ST ContextDHondtINF")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
#pProbToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002(
# InputSimulatorDefinition.numberOfAggrItems)
pToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
dataset: ADataset = DatasetST.readDatasets()
events = dataset.eventsDF
serials = dataset.serialsDF
historyDF: AHistory = HistoryDF("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
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextDHondtNIF" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDContextHondtINF(
selector, pToolOLin0802HLin1002, 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 test01():
print("Simulation: ML ContextDHondtINF")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
#pProbToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002(
# InputSimulatorDefinition.numberOfAggrItems)
pToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
itemsDF: DataFrame = Items.readFromFileMl1m()
usersDF: DataFrame = Users.readFromFileMl1m()
historyDF: AHistory = HistoryDF("test01")
eTool: AEvalTool = EvalToolContext({
EvalToolContext.ARG_USERS: usersDF,
EvalToolContext.ARG_ITEMS: itemsDF,
EvalToolContext.ARG_DATASET: "ml",
EvalToolContext.ARG_HISTORY: historyDF
})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextDHondtINF" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDContextHondtINF(
selector, pToolOLin0802HLin1002, 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 test02():
print("Test 02")
# number of recommended items
N = 120
# method results, items=[1,2,3,4,5,6,7,8,9,10]
methodsResultDict = {
"metoda1":
pd.Series([0.2, 0.2, 0.2, 0.2, 0.2], [1, 3, 5, 7, 9], name="rating"),
"metoda2":
pd.Series([0.2, 0.2, 0.2, 0.2, 0.2], [2, 4, 6, 8, 10], name="rating"),
}
print(methodsResultDict)
# methods parametes
#methodsParamsData:List[tuple] = [['metoda1',0], ['metoda2',0]]
methodsParamsData: List[tuple] = [['metoda1', 1], ['metoda2', 1]]
methodsParamsDF: DataFrame = pd.DataFrame(methodsParamsData,
columns=["methodID", "votes"])
methodsParamsDF.set_index("methodID", inplace=True)
#aggr:AggrDHont = AggrDHont(HistoryDF(), {AggrDHont.ARG_SELECTORFNC:(AggrDHont.selectorOfTheMostVotedItem,[])})
#aggr:AggrDHont = AggrDHont(HistoryDF(), {AggrDHont.ARG_SELECTORFNC:(AggrDHont.selectorOfRouletteWheelRatedItem,[])})
#aggr:AggrDHont = AggrDHont(HistoryDF(), {AggrDHont.ARG_SELECTORFNC:(AggrDHont.selectorOfRouletteWheelExpRatedItem,[1])})
pToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
20)
aggr: AggrFuzzyDHondt = AggrFuzzyDHondtINF(
HistoryDF(""), {
AggrFuzzyDHondtINF.ARG_SELECTOR:
RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1}),
AggrFuzzyDHondtINF.ARG_PENALTY_TOOL:
pToolOLin0802HLin1002
})
userID: int = 101
##itemIDs:int = aggr.run(methodsResultDict, methodsParamsDF, userID, N)
itemIDs: int = aggr.run(methodsResultDict, methodsParamsDF, userID, N)
#itemIDs:List[tuple] = aggr.runWithResponsibility(methodsResultDict, methodsParamsDF, userID, N)
print(itemIDs)
def getBanditTS():
taskID:str = "Web" + "BanditTS" + "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, InputAggrDefinition.exportADescBanditTS(selector))
dataset:DatasetST = DatasetST.readDatasets()
history:AHistory = HistoryHierDF(taskID)
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelBandit(pDescr.getRecommendersIDs())
evalTool:AEvalTool = EvalToolBanditTS({})
return (taskID, port, model, evalTool, history)
def test01():
print("Simulation: ML FuzzyDHondtINF")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
pProbToolOLin0802HLin1002: APenalization = PenalizationToolDefinition.exportProbPenaltyToolOStat08HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FuzzyDHondtINF" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondtINF(selector,
pProbToolOLin0802HLin1002))
batchID: str = "ml1mDiv90Ulinear0109R1"
dataset: DatasetML = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(BehavioursML.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
model: DataFrame = PModelDHondt(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
})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def test01():
print("Simulation: ML ContextFuzzyDHondtDirectOptimize")
jobID: str = "Roulette1"
selector: ADHondtSelector = RouletteWheelSelector(
{RouletteWheelSelector.ARG_EXPONENT: 1})
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 = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"ContextFuzzyDHondtDirectOptimize" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescContextFuzzyDHondtDirectOptimize(
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], [historyDF])
def test21():
print("Simulation: ST FuzzyDHondt")
jobID: str = "Roulette1"
selector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
"FuzzyDHondt" + jobID, rIDs, rDescs,
InputAggrDefinition.exportADescDHondt(selector))
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)
lrClick: float = 0.1
lrView: float = lrClick / 300
evalTool: AEvalTool = EvalToolDHondt({
EvalToolDHondt.ARG_LEARNING_RATE_CLICKS:
lrClick,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS:
lrView
})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationST, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [evalTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def selectorOfRouletteWheelRatedItem(votesOfCandidatesDict: dict):
votesOfCandidatesSer: Series = Series(
votesOfCandidatesDict, index=votesOfCandidatesDict.keys())
return RouletteWheelSelector.run(votesOfCandidatesSer)
def selectorOfRouletteWheelRatedItem(resultOfMethod: Series):
return RouletteWheelSelector.run(resultOfMethod)