def test01():
print("Simulation: RR Dynamic")
lrClick: float = 0.03
#lrView:float = lrClick / 300
lrViewDivisor: float = 250
jobID: str = "Fixed" + "Clk" + str(lrClick).replace(
".", "") + "ViewDivisor" + str(lrViewDivisor).replace(".", "")
selector: ADHondtSelector = TheMostVotedItemSelector({})
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)
batchID: str = "rrDiv90Ulinear0109R1"
dataset: DatasetRetailRocket = DatasetRetailRocket.readDatasetsWithFilter(
minEventCount=50)
behaviourFile: str = BehavioursRR.getFile(BehavioursRR.BHVR_LINEAR0109)
behavioursDF: DataFrame = BehavioursRR.readFromFileRR(behaviourFile)
model: DataFrame = PModelDHondtPersonalisedStat(
p1AggrDescr.getRecommendersIDs())
eTool: AEvalTool = EvalToolDHondtPersonal({
EvalToolDHondt.ARG_LEARNING_RATE_CLICKS:
lrClick,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS:
lrClick / lrViewDivisor
})
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationRR, argsSimulationDict,
dataset, behavioursDF)
simulator.simulate([pDescr], [model], [eTool],
[HistoryHierDF(pDescr.getPortfolioID())])
def getParameters(self):
aDict:Dict[str,object] = {}
for selectorIDI in self.selectorIDs:
for lrClickJ in self.lrClicks:
for lrViewDivisorK in self.lrViewDivisors:
for normOfResponsL in self.normOfRespons:
keyIJ:str = selectorIDI + "Clk" + str(lrClickJ).replace(".", "") + "ViewDivisor" + str(lrViewDivisorK).replace(".", "") + "NR" + str(normOfResponsL)
lrViewIJK:float = lrClickJ / lrViewDivisorK
eToolIJK:AEvalTool = EvalToolDHondtPersonal({
EvalToolDHondtPersonal.ARG_LEARNING_RATE_CLICKS: lrClickJ,
EvalToolDHondtPersonal.ARG_LEARNING_RATE_VIEWS: lrViewIJK,
EvalToolDHondtPersonal.ARG_NORMALIZATION_OF_RESPONSIBILITY: normOfResponsL})
selectorIJK:ADHondtSelector = BatchDefMLFuzzyDHondt().getAllSelectors()[selectorIDI]
aDict[keyIJ] = (selectorIJK, eToolIJK)
return aDict
def getParameters(self):
aDict: Dict[str, object] = {}
for selectorIDI in self.selectorIDs:
for gLrClickJ in self.mGlobalLrClicks:
for gLrViewDivisorK in self.mGlobalLrViewDivisors:
for gNormOfResponsL in self.mGlobalNormOfRespons:
for pLrClickM in self.mPersonLrClicks:
for pLrViewDivisorN in self.mPersonLrViewDivisors:
for pLrViewDivisorO in self.mPersonNormOfRespons:
gjk: str = "Clk" + str(gLrClickJ).replace(
".", "") + "ViewDivisor" + str(
gLrViewDivisorK).replace(
".", "") + "NR" + str(
gNormOfResponsL)
pjk: str = "Clk" + str(pLrClickM).replace(
".", "") + "ViewDivisor" + str(
pLrViewDivisorN).replace(
".", "") + "NR" + str(
pLrViewDivisorO)
keyIJ: str = selectorIDI + gjk + pjk
lrViewJK: float = gLrClickJ / gLrViewDivisorK
lrViewLM: float = pLrClickM / pLrViewDivisorN
evalToolMGlobal: EvalToolDHondt = EvalToolDHondt({
EvalToolDHondt.ARG_LEARNING_RATE_CLICKS:
gLrClickJ,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS:
lrViewJK,
EvalToolDHondt.ARG_NORMALIZATION_OF_RESPONSIBILITY:
gNormOfResponsL
})
evalToolMPerson: EvalToolDHondt = EvalToolDHondtPersonal({
EvalToolDHondtPersonal.ARG_LEARNING_RATE_CLICKS:
pLrClickM,
EvalToolDHondtPersonal.ARG_LEARNING_RATE_VIEWS:
lrViewLM,
EvalToolDHondtPersonal.ARG_NORMALIZATION_OF_RESPONSIBILITY:
pLrViewDivisorO
})
eToolIJK: AEvalTool = EToolHybrid(
evalToolMGlobal, evalToolMPerson, {})
selectorIJK: ADHondtSelector = BatchDefMLFuzzyDHondt(
).getSelectorParameters()[selectorIDI]
aDict[keyIJ] = (selectorIJK, eToolIJK)
return aDict
def test01():
print("Test 01")
#print("Running Two paralel History Databases:")
# 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")
}
rItemIDsWithResponsibility: List = [(7, {
'metoda1': 0,
'metoda2': 24.0,
'metoda3': 18.0
}), (1, {
'metoda1': 30.0,
'metoda2': 8.0,
'metoda3': 0
}), (32, {
'metoda1': 20.0,
'metoda2': 16.0,
'metoda3': 0
}), (8, {
'metoda1': 30.0,
'metoda2': 0,
'metoda3': 0
}), (6, {
'metoda1': 0,
'metoda2': 24.0,
'metoda3': 0
}), (64, {
'metoda1': 0,
'metoda2': 0,
'metoda3': 18.0
}), (2, {
'metoda1': 10.0,
'metoda2': 0,
'metoda3': 6.0
}), (77, {
'metoda1': 0,
'metoda2': 0,
'metoda3': 12.0
}), (4, {
'metoda1': 10.0,
'metoda2': 0,
'metoda3': 0
}), (5, {
'metoda1': 0,
'metoda2': 8.0,
'metoda3': 0
}), (12, {
'metoda1': 0,
'metoda2': 0,
'metoda3': 6.0
})]
# methods parametes
portfolioModelData: List[tuple] = [['metoda1', 100], ['metoda2', 80],
['metoda3', 60]]
portfolioModelDF: DataFrame = pd.DataFrame(
portfolioModelData,
columns=[PModelDHondt.COL_METHOD_ID, PModelDHondt.COL_VOTES])
portfolioModelDF.set_index(PModelDHondt.COL_METHOD_ID, inplace=True)
portfolioModelDF.__class__ = PModelDHondtPersonalised
userID = 1
clickedItemID = 101
tool = EvalToolDHondtPersonal({
EvalToolDHondtPersonal.ARG_LEARNING_RATE_CLICKS:
0.2,
EvalToolDHondtPersonal.ARG_LEARNING_RATE_VIEWS:
1000
})
tool.click(userID, rItemIDsWithResponsibility, clickedItemID,
portfolioModelDF, {})
def test02():
print("Test 02")
#print("Running Two paralel History Databases:")
# 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")
}
rItemIDsWithResponsibility: List = [(7, {
'metoda1': 0,
'metoda2': 24.0,
'metoda3': 18.0
}), (1, {
'metoda1': 30.0,
'metoda2': 8.0,
'metoda3': 0
}), (32, {
'metoda1': 20.0,
'metoda2': 16.0,
'metoda3': 0
}), (8, {
'metoda1': 30.0,
'metoda2': 0,
'metoda3': 0
}), (6, {
'metoda1': 0,
'metoda2': 24.0,
'metoda3': 0
}), (64, {
'metoda1': 0,
'metoda2': 0,
'metoda3': 18.0
}), (2, {
'metoda1': 10.0,
'metoda2': 0,
'metoda3': 6.0
}), (77, {
'metoda1': 0,
'metoda2': 0,
'metoda3': 12.0
}), (4, {
'metoda1': 10.0,
'metoda2': 0,
'metoda3': 0
}), (5, {
'metoda1': 0,
'metoda2': 8.0,
'metoda3': 0
}), (12, {
'metoda1': 0,
'metoda2': 0,
'metoda3': 6.0
})]
userID = 1
# methods parametes
portfolioModelDF: DataFrame = PModelDHondtPersonalised(
['metoda1', 'metoda2', 'metoda3'])
pModelDF: DataFrame = portfolioModelDF.getModel(userID)
print("11111111111111111111111111111")
print(pModelDF)
clickedItemID = 12
tool = EvalToolDHondtPersonal({
EvalToolDHondtPersonal.ARG_LEARNING_RATE_CLICKS:
0.2,
EvalToolDHondtPersonal.ARG_LEARNING_RATE_VIEWS:
1000
})
tool.click(userID, rItemIDsWithResponsibility, clickedItemID,
portfolioModelDF, {})
pModelDF: DataFrame = portfolioModelDF.getModel(userID)
print("22222222222222222222222222222")
print(pModelDF)