def test01():
print("Test 01")
# number of recommended items
N: int = 5
methodsResultDict: dict = {
"method1": pd.Series([0.9, 0.5, 0.4, 0.2], [1, 2, 3, 4],
name="rating"),
"method2": pd.Series([0.2, 0.9, 0.01], [3, 4, 5], name="rating"),
"method3": pd.Series([0.01, 0.9, 0.9], [5, 6, 7], name="rating")
}
# methods parametes
methodsParamsData: List[tuple] = [['method1', 5, 10, 1, 1],
['method2', 5, 12, 1, 1],
['method3', 6, 13, 1, 1]]
modelDF: DataFrame = pd.DataFrame(
methodsParamsData, columns=["methodID", "r", "n", "alpha0", "beta0"])
modelDF.set_index("methodID", inplace=True)
#modelDF:DataFrame = DataFrame({"votes": [0.5, 0.4, 0.1]}, index=["method1", "method2", "method3"])
#print(modelDF)
userID: int = 0
aggr: AggrDHondtDirectOptimizeThompsonSampling = AggrDHondtDirectOptimizeThompsonSampling(
HistoryDF(""), {
AggrDHondtDirectOptimizeThompsonSampling.ARG_SELECTOR:
TheMostVotedItemSelector({}),
AggrDHondtDirectOptimizeThompsonSampling.ARG_DISCOUNT_FACTOR:
"uniform"
})
itemIDs: List[tuple] = aggr.run(methodsResultDict, modelDF, userID, N)
print("itemIDs:" + str(itemIDs))
aggr: AggrDHondtDirectOptimizeThompsonSampling = AggrDHondtDirectOptimizeThompsonSampling(
HistoryDF(""), {
AggrDHondtDirectOptimizeThompsonSampling.ARG_SELECTOR:
TheMostVotedItemSelector({}),
AggrDHondtDirectOptimizeThompsonSampling.ARG_DISCOUNT_FACTOR:
"DCG"
})
itemIDs: List[tuple] = aggr.run(methodsResultDict, modelDF, userID, N)
print("itemIDs:" + str(itemIDs))
aggr: AggrDHondtDirectOptimizeThompsonSampling = AggrDHondtDirectOptimizeThompsonSampling(
HistoryDF(""), {
AggrDHondtDirectOptimizeThompsonSampling.ARG_SELECTOR:
TheMostVotedItemSelector({}),
AggrDHondtDirectOptimizeThompsonSampling.ARG_DISCOUNT_FACTOR:
"PowerLaw"
})
itemIDs: List[tuple] = aggr.run(methodsResultDict, modelDF, userID, N)
print("itemIDs:" + str(itemIDs))
def getContextFuzzyDHondtDirectOptimizeINF():
# taskID:str = "Web" + "ContextFuzzyDHondtDirectOptimizeINF" + "Roulette1"
taskID:str = "Web" + "ContextFuzzyDHondtDirectOptimizeINF" + "Fixed"
dataset:ADataset = DatasetST.readDatasets()
# selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
selector:ADHondtSelector = TheMostVotedItemSelector({})
pToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
history:AHistory = HistoryHierDF(taskID)
# Init eTool
evalTool:AEvalTool = EvalToolContext({
EvalToolContext.ARG_ITEMS: dataset.serialsDF, # ITEMS
EvalToolContext.ARG_EVENTS: dataset.eventsDF, # EVENTS (FOR CALCULATING HISTORY OF USER)
EvalToolContext.ARG_DATASET: "st", # WHAT DATASET ARE WE IN
EvalToolContext.ARG_HISTORY: history}) # empty instance of AHistory is OK for ST dataset
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDContextHondtDirectOptimizeINF(selector, pToolOLin0802HLin1002, evalTool)
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
return (taskID, port, model, evalTool, history)
def getFuzzyDHontINF():
#taskID:str = "Web" + "FuzzyDHondtINF" + "Roulette1"
taskID:str = "Web" + "FuzzyDHondt" + "Fixed"
dataset:ADataset = DatasetST.readDatasets()
#selector:ADHondtSelector = RouletteWheelSelector({RouletteWheelSelector.ARG_EXPONENT: 1})
selector:ADHondtSelector = TheMostVotedItemSelector({})
pToolOLin0802HLin1002:APenalization = PenalizationToolDefinition.exportPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtINF(selector, pToolOLin0802HLin1002)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
history:AHistory = HistoryHierDF(taskID)
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
evalTool:AEvalTool = EvalToolDHondt({EvalToolDHondt.ARG_LEARNING_RATE_CLICKS: 0.03,
EvalToolDHondt.ARG_LEARNING_RATE_VIEWS: 0.03 / 500})
return (taskID, port, model, evalTool, history)
def getFuzzyDHontThompsonSamplingINF():
taskID:str = "Web" + "FuzzyDHondtThompsonSamplingINF" + "Fixed" + "OLin0802HLin1002"
selector:ADHondtSelector = TheMostVotedItemSelector({})
penalization:APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002(20)
aDescDHont:AggregationDescription = InputAggrDefinition.exportADescDHondtThompsonSamplingINF(selector, penalization)
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs()
pDescr:Portfolio1AggrDescription = Portfolio1AggrDescription(
taskID, rIDs, rDescs, aDescDHont)
history:AHistory = HistoryHierDF(taskID)
dataset:ADataset = DatasetST.readDatasets()
port:APortfolio = pDescr.exportPortfolio(taskID, history)
port.train(history, dataset)
model:DataFrame = PModelDHondtBanditsVotes(pDescr.getRecommendersIDs())
evalTool:AEvalTool = EvalToolDHondtBanditVotes({})
return (taskID, port, model, evalTool, history)
def test01():
print("Test 01")
# number of recommended items
N:int = 5
methodsResultDict:dict = {
"method1":pd.Series([0.5, 0.5, 0.4, 0.2],[1,2,3,4],name="rating"),
"method2":pd.Series([0.9, 0.5, 0.1],[5,1,6],name="rating"),
"method3":pd.Series([0.7, 0.3, 0.9], [3, 4, 6], name="rating")
}
# methods parametes
modelDF:DataFrame = DataFrame({"votes": [0.5, 0.4, 0.1]}, index=["method1", "method2", "method3"])
#print(modelDF)
userID:int = 0
aggr:AggrFuzzyDHondtDirectOptimize = AggrFuzzyDHondtDirectOptimize({
AggrFuzzyDHondtDirectOptimize.ARG_SELECTOR:TheMostVotedItemSelector({})
})
#itemIDs:List[tuple] = aggr.runWithResponsibility(methodsResultDict, modelDF, userID, N)
# [1, 3, 5, 2, 4]
itemIDs:List[tuple] = aggr.run(methodsResultDict, modelDF, userID, N)
print("itemIDs:" + str(itemIDs))
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 test01():
print("Test 01")
# number of recommended items
N = 120
#a = observationalLinearProbabilityFnc(0.1, 0.9, 5)
#print(a)
uBehaviourDesc: UserBehaviourDescription = UserBehaviourDescription(
observationalLinearProbabilityFnc, [0.1, 0.9])
# method results, items=[1,2,4,5,6,7,8,12,32,64,77]
methodsResultDict: dict[str, pd.Series] = {
"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', 100], ['metoda2', 80],
['metoda3', 60]]
methodsParamsDF: DataFrame = pd.DataFrame(methodsParamsData,
columns=["methodID", "votes"])
methodsParamsDF.set_index("methodID", inplace=True)
#print(methodsParamsDF)
userID: int = 0
itemID: int = 7
historyDF: AHistory = HistoryDF("test01")
historyDF.insertRecommendation(userID, itemID, 1, True, None)
historyDF.insertRecommendation(userID, itemID, 1, True, None)
historyDF.insertRecommendation(userID, itemID, 1, True, None)
historyDF.print()
#ignoringValue:float = historyDF.getIgnoringValue(userID, itemID, limit=3)
#print("IgnoringValue: " + str(ignoringValue))
penalization: APenalization = PenalUsingReduceRelevance(
penaltyLinear, [0.8, 0.2, 3], penaltyLinear, [1.0, 0.2, 3], 3)
aggr: AggrFuzzyDHondt = AggrFuzzyDHondtINF(
historyDF, {
AggrFuzzyDHondtINF.ARG_SELECTOR: TheMostVotedItemSelector({}),
AggrFuzzyDHondtINF.ARG_PENALTY_TOOL: penalization
})
itemIDs: List[tuple] = aggr.runWithResponsibility(methodsResultDict,
methodsParamsDF, userID,
N)
print(itemIDs)
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: 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 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 test01():
print("Test 01")
# number of recommended items
N = 120
# method results, items=[1,2,4,5,6,7,8,12,32,64,77]
methodsResultDict: dict[str, pd.Series] = {
"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
portfolioModelData: List[tuple] = [['metoda1', 100], ['metoda2', 80],
['metoda3', 60]]
portfolioModel: DataFrame = pd.DataFrame(portfolioModelData,
columns=["methodID", "votes"])
portfolioModel.set_index("methodID", inplace=True)
sumMethodsVotes: float = portfolioModel["votes"].sum()
for methodIdI in portfolioModel.index:
portfolioModel.loc[
methodIdI,
"votes"] = portfolioModel.loc[methodIdI, "votes"] / sumMethodsVotes
aggr: AggrFuzzyDHondt = AggrFuzzyDHondt(
HistoryDF(""),
{AggrFuzzyDHondt.ARG_SELECTOR: TheMostVotedItemSelector({})})
#itemIDs:int = aggr.run(methodsResultDict, methodsParamsDF, N)
#print(itemIDs)
userID: int = 101
itemIDs: List[tuple] = aggr.runWithResponsibility(methodsResultDict,
portfolioModel, userID,
N)
print(itemIDs)
def test03():
# First get Dataset Data
dataset: ADataset = DatasetST.readDatasets()
events = dataset.eventsDF
serials = dataset.serialsDF
# I created some dummy data, but each key,value pair should be result list from a recommender
# (=what recommender recommended)
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")
}
# init votes for each recommender
portfolioModelData = [['metoda1', 0.6], ['metoda2', 0.4]]
portfolioModelDF: DataFrame = pd.DataFrame(portfolioModelData,
columns=["methodID", "votes"])
portfolioModelDF.set_index("methodID", inplace=True)
userID = 1
itemID = 20
historyDF: AHistory = HistoryDF("test01")
# WHAT EVALUATIOR NEEDS into dictionary!
evaluationDict: dict = {
EvalToolContext.ARG_USER_ID: userID,
EvalToolContext.ARG_ITEM_ID:
itemID, # ITEMID (not mandatory if EvalToolContext.ARG_PAGE_TYPE != "zobrazit")
EvalToolContext.ARG_SENIORITY: 5, # SENIORITY OF USER
EvalToolContext.ARG_PAGE_TYPE:
"zobrazit", # TYPE OF PAGE ("zobrazit", "index" or "katalog)
EvalToolContext.ARG_ITEMS_SHOWN: 10 # HOW MANY ITEMS ARE SHOWN TO USER
}
# Init eTool
eToolContext = 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
)
aggr: AggrContextFuzzyDHondt = AggrContextFuzzyDHondt(
historyDF,
{ # empty instance of AHistory is OK for ST dataset
AggrContextFuzzyDHondt.ARG_EVAL_TOOL: eToolContext, # eTool
AggrContextFuzzyDHondt.ARG_SELECTOR: TheMostVotedItemSelector(
{}) # ? FuzzyDHondt needs this, not contextAggr
})
# Get data from aggregator
rItemsWithResponsibility = aggr.runWithResponsibility(
methodsResultDict,
portfolioModelDF,
userID,
numberOfItems=5,
argumentsDict=evaluationDict)
# call click & displayed methods
l1 = eToolContext.displayed(rItemsWithResponsibility, portfolioModelDF,
evaluationDict)
# rItemsWithResponsibility[0][0] is clicked item
print(rItemsWithResponsibility)
l1 = eToolContext.click(rItemsWithResponsibility,
rItemsWithResponsibility[0][0], portfolioModelDF,
evaluationDict)
# ...
# ...
# ...
# user is now on "index" page type, so we have to change page type in evaluationDict (!)
evaluationDict[EvalToolContext.ARG_PAGE_TYPE] = "index"
# same as before
# Get data from aggregator
rItemsWithResponsibility = aggr.runWithResponsibility(
methodsResultDict,
portfolioModelDF,
userID,
numberOfItems=5,
argumentsDict=evaluationDict)
# call click & displayed methods
l1 = eToolContext.displayed(rItemsWithResponsibility, portfolioModelDF,
evaluationDict)
# rItemsWithResponsibility[0][0] is clicked item
l1 = eToolContext.click(rItemsWithResponsibility,
rItemsWithResponsibility[0][0], portfolioModelDF,
evaluationDict)
def test02(repetitions=1):
N = 100
# get dataset
itemsDF: DataFrame = Items.readFromFileMl1m()
usersDF: DataFrame = Users.readFromFileMl1m()
ratingsDF: DataFrame = Ratings.readFromFileMl1m()
ratingsDFTrain = ratingsDF[:50000]
ratingsDFUpdate: DataFrame = ratingsDF.iloc[50001:50100]
trainDataset: ADataset = DatasetML("ml", ratingsDFTrain, usersDF, itemsDF)
historyDF: AHistory = HistoryDF("test01")
# train KNN
rec1: ARecommender = RecommenderItemBasedKNN("run", {})
rec1.train(HistoryDF("test01"), trainDataset)
# train Most Popular
rec2: ARecommender = RecommenderTheMostPopular("run", {})
rec2.train(historyDF, trainDataset)
# methods parametes
methodsParamsData: List[tuple] = [['ItembasedKNN', 0.4],
['MostPopular', 0.6]]
methodsParamsDF: DataFrame = pd.DataFrame(methodsParamsData,
columns=["methodID", "votes"])
methodsParamsDF.set_index("methodID", inplace=True)
userID = 352
ratingsDFuserID = ratingsDF[ratingsDF['userId'] == userID]
itemID = ratingsDFuserID.iloc[0]['movieId']
historyDF: AHistory = HistoryDF("test01")
historyDF.insertRecommendation(userID, itemID, 1, True, 10)
r1: Series = rec1.recommend(userID, N, {})
r2: Series = rec2.recommend(userID, N, {})
methodsResultDict: dict = {"ItembasedKNN": r1, "MostPopular": r2}
evaluationDict: dict = {
EvalToolContext.ARG_USER_ID: userID,
EvalToolContext.ARG_RELEVANCE: methodsResultDict
}
evalToolDHondt = EvalToolContext({
EvalToolContext.ARG_USERS: usersDF,
EvalToolContext.ARG_ITEMS: itemsDF,
EvalToolContext.ARG_DATASET: "ml",
EvalToolContext.ARG_HISTORY: historyDF
})
aggr: AggrContextFuzzyDHondt = AggrContextFuzzyDHondt(
historyDF, {
AggrContextFuzzyDHondt.ARG_EVAL_TOOL: evalToolDHondt,
AggrContextFuzzyDHondt.ARG_SELECTOR: TheMostVotedItemSelector({})
})
aggrInit: AggrFuzzyDHondt = AggrFuzzyDHondt(
historyDF,
{AggrFuzzyDHondt.ARG_SELECTOR: TheMostVotedItemSelector({})})
l1 = aggrInit.runWithResponsibility(methodsResultDict, methodsParamsDF,
userID, N)
import random
print("l1:" + str(l1))
evalToolDHondt.displayed(l1, methodsParamsDF, evaluationDict)
evalToolDHondt.click(l1,
random.choice(l1)[0], methodsParamsDF, evaluationDict)
timestamp = 10
counter = 0
r1c = 0
r2c = 0
for _ in range(repetitions):
for index, row in ratingsDFuserID.iterrows():
r1: Series = rec1.recommend(userID, N, {})
r2: Series = rec2.recommend(userID, N, {})
methodsResultDict: dict = {"ItembasedKNN": r1, "MostPopular": r2}
evalDict = {"a": 1}
historyDF.insertRecommendation(userID, row['movieId'], 1, True,
timestamp)
timestamp += 1
l1 = aggr.runWithResponsibility(methodsResultDict,
methodsParamsDF,
userID,
argumentsDict=evalDict,
numberOfItems=N)
import random
randomItem = random.choice(l1)[0]
if randomItem in r1.index:
r1c += 1
if randomItem in r2.index:
r2c += 1
evaluationDict: dict = {
EvalToolContext.ARG_USER_ID: userID,
EvalToolContext.ARG_RELEVANCE: methodsResultDict
}
print("votes Items: ", r1c)
print("votes mostPopular ", r2c)
evalToolDHondt.displayed(l1, methodsParamsDF, evaluationDict)
evalToolDHondt.click(l1, randomItem, methodsParamsDF,
evaluationDict)
rec1.update(ratingsDFuserID.loc[[index]], {})
# rec2.update(ratingsDFuserID.loc[index]) Not implemented
#print("Counter = ", counter, "; All = ", len(ratingsDFuserID.iloc[800:]), "; Index: ", index)
print(methodsParamsDF)
counter += 1
def test01():
print("Test 01")
recommenderID: str = "TheMostPopular"
pRDescr: RecommenderDescription = RecommenderDescription(
RecommenderTheMostPopular, {})
selectorFixed: ADHondtSelector = TheMostVotedItemSelector({})
aDescDHont: AggregationDescription = InputAggrDefinition.exportADescDHondtDirectOptimizeThompsonSampling(
selectorFixed)
rIDs: List[str]
rDescs: List[AggregationDescription]
rIDs, rDescs = InputRecomRRDefinition.exportPairOfRecomIdsAndRecomDescrs()
rIDs = [recommenderID]
rDescs = [pRDescr]
p1AggrDescrID: str = "p1AggrDescrID"
p1AggrDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
p1AggrDescrID, rIDs, rDescs, aDescDHont)
pProbTool: APenalization = PenalizationToolDefinition.exportProbPenaltyToolOLin0802HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
pProbTool: APenalization = PenalizationToolDefinition.exportPenaltyToolOStat08HLin1002(
InputSimulatorDefinition.numberOfAggrItems)
aHierDescr: AggregationDescription = AggregationDescription(
AggrD21, {AggrD21.ARG_RATING_THRESHOLD_FOR_NEG: 2.0})
pHierDescr: PortfolioHierDescription = PortfolioHierDescription(
"pHierDescr", recommenderID, pRDescr, p1AggrDescrID, p1AggrDescr,
aHierDescr, pProbTool)
userID: int = 1
dataset: ADataset = DatasetRetailRocket.readDatasetsWithFilter(
minEventCount=50)
history: AHistory = HistoryDF("test")
history.insertRecommendation(userID, 45, 1, False)
history.insertRecommendation(userID, 45, 2, False)
history.insertRecommendation(userID, 78, 3, False)
p: APortfolio = pHierDescr.exportPortfolio("test", history)
portFolioModel: DataFrame = PModelDHondtBanditsVotes(
p1AggrDescr.getRecommendersIDs())
p.train(history, dataset)
#df:DataFrame = DataFrame([[1, 555]], columns=[Events.COL_USER_ID, Events.COL_OBJECT_ID])
#p.update(ARecommender.UPDT_CLICK, df)
args = {
APortfolio.ARG_NUMBER_OF_AGGR_ITEMS: 20,
APortfolio.ARG_ITEM_ID: 1,
APortfolio.ARG_NUMBER_OF_RECOMM_ITEMS: 100,
AggrD21.ARG_RATING_THRESHOLD_FOR_NEG: 0.5
}
r, rp = p.recommend(userID, portFolioModel, args)
print(r)
