def test02():
print("Test 02")
print("Running Recommender BPRMF on ML:")
dataset: DatasetML = DatasetML.readDatasets()
# Take only first 500k
trainDataset: DatasetML = DatasetML("test",
dataset.ratingsDF.iloc[0:800000],
dataset.usersDF, dataset.itemsDF)
print(dataset.ratingsDF.iloc[655924:655926])
# train recommender
rec: ARecommender = RecommenderBPRMFImplicit(
"test", {
RecommenderBPRMFImplicit.ARG_FACTORS: 20,
RecommenderBPRMFImplicit.ARG_ITERATIONS: 50,
RecommenderBPRMFImplicit.ARG_LEARNINGRATE: 0.003,
RecommenderBPRMFImplicit.ARG_REGULARIZATION: 0.003
})
rec.train(HistoryDF("test02"), trainDataset)
# get recommendations:
print("Recommendations before update")
r: Series = rec.recommend(23, 50, {})
print(r)
print("================== END OF TEST 02 ======================\n\n\n\n\n")
def exportSimulatorML1M(self, batchID: str,
divisionDatasetPercentualSize: int,
uBehaviourID: str, repetition: int):
argsSimulationDict: dict = {
SimulationML.ARG_WINDOW_SIZE: 5,
SimulationML.ARG_RECOM_REPETITION_COUNT: repetition,
SimulationML.ARG_NUMBER_OF_RECOMM_ITEMS: 100,
SimulationML.ARG_NUMBER_OF_AGGR_ITEMS: self.numberOfAggrItems,
SimulationML.ARG_DIV_DATASET_PERC_SIZE:
divisionDatasetPercentualSize,
SimulationML.ARG_HISTORY_LENGTH: 10
}
# dataset reading
dataset: ADataset = DatasetML.readDatasets()
behaviourFile: str = BehavioursML.getFile(uBehaviourID)
behavioursDF: DataFrame = BehavioursML.readFromFileMl1m(behaviourFile)
# simulation of portfolio
simulator: Simulator = Simulator(batchID, SimulationML,
argsSimulationDict, dataset,
behavioursDF)
return simulator
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 test01():
print("Test 01")
print("Running Recommender BPRMF on ML:")
batchID: str = "batchID"
dataset: ADataset = DatasetML.readDatasets()
history: AHistory = HistoryHierDF(["aa"])
argumentsDict: Dict[str, object] = {
RecommenderBPRMF.ARG_EPOCHS: 2,
RecommenderBPRMF.ARG_FACTORS: 10,
RecommenderBPRMF.ARG_LEARNINGRATE: 0.05,
RecommenderBPRMF.ARG_UREGULARIZATION: 0.0025,
RecommenderBPRMF.ARG_BREGULARIZATION: 0,
RecommenderBPRMF.ARG_PIREGULARIZATION: 0.0025,
RecommenderBPRMF.ARG_NIREGULARIZATION: 0.00025
}
r: ARecommender = RecommenderBPRMF(batchID, argumentsDict)
r.train(history, dataset)
numberOfItems: int = 20
userId: int = 1
res = r.recommend(userId, numberOfItems, argumentsDict)
print(res)
userId: int = 2
res = r.recommend(userId, numberOfItems, argumentsDict)
print(res)
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 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 test01():
print("Test 01")
dataset: ADataset = DatasetML.readDatasets()
#12::Dracula: Dead and Loving It(1995)::Comedy | Horror
itemsDF = dataset.itemsDF[dataset.itemsDF[Items.COL_MOVIEID] == 12]
print(itemsDF)
#458::Geronimo: An American Legend(1993)::Drama | Western
itemsDF = dataset.itemsDF[dataset.itemsDF[Items.COL_MOVIEID] == 458]
print(itemsDF)
def test01():
print("Test 01")
print("Running Recommender BPRMF on ML:")
dataset: DatasetML = DatasetML.readDatasets()
# Take only first 500k
trainDataset: DatasetML = DatasetML("test",
dataset.ratingsDF.iloc[0:499965],
dataset.usersDF, dataset.itemsDF)
# train recommender
rec: ARecommender = RecommenderBPRMFImplicit(
"test", {
RecommenderBPRMFImplicit.ARG_FACTORS: 20,
RecommenderBPRMFImplicit.ARG_ITERATIONS: 50,
RecommenderBPRMFImplicit.ARG_LEARNINGRATE: 0.003,
RecommenderBPRMFImplicit.ARG_REGULARIZATION: 0.003
})
rec.train(HistoryDF("test01"), trainDataset)
# get one rating for update
ratingsDFUpdate: DataFrame = dataset.ratingsDF.iloc[499965:503006]
# get recommendations:
print("Recommendations before update")
print(rec._movieFeaturesMatrixLIL[:, ratingsDFUpdate['userId'].iloc[0]].
getnnz())
r: Series = rec.recommend(ratingsDFUpdate['userId'].iloc[0], 50, {})
print(r)
for i in range(ratingsDFUpdate.shape[0]):
rUp = ratingsDFUpdate.iloc[i:i + 1, :]
rec.update(rUp, {})
print("Recommendations after update")
print(rec._movieFeaturesMatrixLIL[:, ratingsDFUpdate['userId'].iloc[0]].
getnnz())
r: Series = rec.recommend(ratingsDFUpdate['userId'].iloc[0], 50, {})
print(r)
print("Test for non-existent user:"******"================== END OF TEST 01 ======================\n\n\n\n\n")
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 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 test06():
print("Simulation: ML VMCMF")
rDescr:RecommenderDescription = InputRecomSTDefinition.exportRDescVMContextKNN()
pDescr:APortfolioDescription = Portfolio1MethDescription(InputRecomSTDefinition.VMC_KNN.title(),
InputRecomSTDefinition.VMC_KNN, rDescr)
batchID:str = "mlDiv90Ulinear0109R1"
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 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 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 visualizationML():
print("visualizationML")
from datasets.ml.ratings import Ratings # class
# dataset reading
dataset: ADataset = DatasetML.readDatasets()
print(dataset.ratingsDF.head())
userIdsWithDuplicites: List[int] = dataset.ratingsDF[
Ratings.COL_USERID].tolist()
userIds: List[int] = list(set(userIdsWithDuplicites))
plt.hist(userIdsWithDuplicites,
len(userIds),
None,
fc='none',
lw=1.5,
histtype='step')
plt.ticklabel_format(style='plain')
plt.show()
def test01():
print("")
dataset: DatasetML = DatasetML.readDatasets()
toolMMR: ToolMMR = ToolMMR()
toolMMR.init(dataset)
inputRecommendation: Dict[int, float] = {
2390: 0.585729,
1858: 0.513299,
2169: 0.471460,
3125: 0.376679,
3624: 0.369205
}
lambda_: float = 0.5
numberOfItems: int = 20
r = toolMMR.mmr_sorted(lambda_, pd.Series(inputRecommendation),
numberOfItems)
print(r)
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 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 test01():
print("Test 01")
dataset: ADataset = DatasetML.readDatasets()
itemsDF: DataFrame = dataset.itemsDF
from datasets.ml.items import Items #class
#print(Items.getAllGenres())
#print(itemsDF.head())
#print(itemsDF[itemsDF[Items.COL_GENRES] == Items.GENRE_COMEDY].head())
#print(itemsDF[itemsDF[Items.COL_GENRES].str.contains(Items.GENRE_COMEDY)].head())
dataset.getTheMostPopularOfGenre(Items.GENRE_COMEDY)
argsDict: dict = {RecommenderClusterBased.ARG_RECOMMENDER_NUMERIC_ID: 1}
r: ARecommender = RecommenderClusterBased("test", argsDict)
r.train(HistoryDF("test01"), dataset)
userID: int = 1
rItemIds: List[int] = r.recommend(userID, 20, argsDict)
print(rItemIds)
def test01():
print("Test 01")
rDescr: RecommenderDescription = RecommenderDescription(
RecommenderTheMostPopular, {})
recommenderID: str = "TheMostPopular"
pDescr: Portfolio1MethDescription = Portfolio1MethDescription(
recommenderID.title(), recommenderID, rDescr)
dataset: ADataset = DatasetML.readDatasets()
history: AHistory = HistoryDF("test")
p: APortfolio = pDescr.exportPortfolio("jobID", history)
p.train(history, dataset)
# r, rwr = p.recommend(1, DataFrame(), {APortfolio.ARG_NUMBER_OF_AGGR_ITEMS:20})
# rItemID1 = r[0]
# rItemID2 = r[1]
# rItemID3 = r[2]
#
# print(r)
# print("rItemID1: " + str(rItemID1))
# print("rItemID2: " + str(rItemID2))
# print("rItemID3: " + str(rItemID3))
testRatingsDF: DataFrame = DataFrame(columns=[
Ratings.COL_USERID, Ratings.COL_MOVIEID, Ratings.COL_RATING,
Ratings.COL_TIMESTAMP
])
timeStampI: int = 1000
userID1: int = 1
userID2: int = 2
userID3: int = 3
rItemID1: int = 9001
rItemID2: int = 9002
rItemID3: int = 9003
# training part of dataset
for i in [i + 0 for i in range(5 * 8)]:
timeStampI = timeStampI + 1
testRatingsDF.loc[i] = [userID1] + list([9000, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID2] + list(
[rItemID1, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID2] + list(
[rItemID2, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID3] + list(
[rItemID3, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID2] + list(
[rItemID2, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID2] + list(
[rItemID2, 5, timeStampI])
# testing part of dataset
userID11: int = 11
userID12: int = 12
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID11] + list(
[rItemID1, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID11] + list(
[rItemID2, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID11] + list(
[rItemID3, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID12] + list(
[rItemID2, 5, timeStampI])
timeStampI = timeStampI + 1
testRatingsDF.loc[len(testRatingsDF)] = [userID11] + list(
[rItemID2, 5, timeStampI])
print("len(testRatingsDF): " + str(len(testRatingsDF)))
print(testRatingsDF.head(20))
print(testRatingsDF.tail(20))
datasetMy: ADataset = DatasetML("", testRatingsDF, dataset.usersDF,
dataset.itemsDF)
behavioursDF: DataFrame = DataFrame(
columns=[BehavioursML.COL_REPETITION, BehavioursML.COL_BEHAVIOUR])
for ratingIndexI in range(len(testRatingsDF)):
for repetitionI in range(5):
behavioursDF.loc[ratingIndexI * 5 + repetitionI] = list(
[repetitionI, [True] * 20])
print(behavioursDF.head(20))
argsSimulationDict: Dict[str, str] = {
SimulationML.ARG_WINDOW_SIZE: 5,
SimulationML.ARG_RECOM_REPETITION_COUNT: 1,
SimulationML.ARG_NUMBER_OF_RECOMM_ITEMS: 100,
SimulationML.ARG_NUMBER_OF_AGGR_ITEMS:
InputSimulatorDefinition.numberOfAggrItems,
SimulationML.ARG_DIV_DATASET_PERC_SIZE: 90,
SimulationML.ARG_HISTORY_LENGTH: 10
}
# simulation of portfolio
simulator: Simulator = Simulator("test", SimulationML, argsSimulationDict,
datasetMy, behavioursDF)
simulator.simulate([pDescr], [DataFrame()], [EToolDoNothing({})],
HistoryHierDF)