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 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 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 run(self, batchID: str, jobID: str):
divisionDatasetPercentualSize: int
uBehaviour: str
repetition: int
divisionDatasetPercentualSize, uBehaviour, repetition = InputABatchDefinition(
).getBatchParameters(self.datasetID)[batchID]
selector: ADHondtSelector
selector, negativeImplFeedback = self.getParameters()[jobID]
portfolioID: str = self.getBatchName() + jobID
history: AHistory = HistoryHierDF(portfolioID)
dataset: ADataset = DatasetST.readDatasets()
events = dataset.eventsDF
serials = dataset.serialsDF
historyDF: AHistory = HistoryHierDF("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
rIDs, rDescs = InputRecomSTDefinition.exportPairOfRecomIdsAndRecomDescrs(
)
aDescContextDHont: AggregationDescription = InputAggrDefinition.exportADescDContextHondtINF(
selector, negativeImplFeedback, evalTool)
pDescr: Portfolio1AggrDescription = Portfolio1AggrDescription(
portfolioID, rIDs, rDescs, aDescContextDHont)
model: DataFrame = PModelDHondt(pDescr.getRecommendersIDs())
simulator: Simulator = InputSimulatorDefinition(
).exportSimulatorSlantour(batchID, divisionDatasetPercentualSize,
uBehaviour, repetition)
simulator.simulate([pDescr], [model], [evalTool], [history])
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 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 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 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")
#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 = [['metoda1', 100], ['metoda2', 80], ['metoda3', 60]]
portfolioModelDF: DataFrame = pd.DataFrame(portfolioModelData,
columns=["methodID", "votes"])
portfolioModelDF.set_index("methodID", inplace=True)
itemsDF: DataFrame = Items.readFromFileMl1m()
usersDF: DataFrame = Users.readFromFileMl1m()
print("Definition:")
print(portfolioModelDF)
print(sumMethods(portfolioModelDF))
print()
userID = 1
itemID = 2
historyDF: AHistory = HistoryDF("test01")
historyDF.insertRecommendation(userID, itemID, 1, True, 10)
historyDF.insertRecommendation(userID, itemID, 1, True, 20)
historyDF.insertRecommendation(userID, itemID, 1, True, 30)
historyDF.insertRecommendation(userID, itemID, 1, False, 40)
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
})
print("Clicked:")
#print("rItemIDsWithResponsibility: " + str(rItemIDsWithResponsibility))
evalToolDHondt.click(rItemIDsWithResponsibility, 7, portfolioModelDF,
evaluationDict)
evalToolDHondt.click(rItemIDsWithResponsibility, 1, portfolioModelDF,
evaluationDict)
evalToolDHondt.click(rItemIDsWithResponsibility, 7, portfolioModelDF,
evaluationDict)
print(portfolioModelDF)
print(sumMethods(portfolioModelDF))
print()
print("Displayed - start:")
for i in range(100):
print("rItemIDsWithResponsibility: " + str(rItemIDsWithResponsibility))
evalToolDHondt.displayed(rItemIDsWithResponsibility, portfolioModelDF,
evaluationDict)
print(portfolioModelDF)
print(sumMethods(portfolioModelDF))
print()
print(portfolioModelDF)
print(sumMethods(portfolioModelDF))
print("Displayed - end:")
print()
print("Clicked:")
evalToolDHondt.click(rItemIDsWithResponsibility, 4, portfolioModelDF,
evaluationDict)
print(portfolioModelDF)
print(sumMethods(portfolioModelDF))
print()