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 test01():
print("Test 01")
os.chdir("..")
userID: int = 1
history: AHistory = HistoryHierDF("databse1")
# userID, itemID, position, observation, clicked
history.insertRecommendation(userID, 100, 1, 0.5, False)
history.insertRecommendation(userID, 100, 1, 0.5, True)
history.insertRecommendation(userID, 100, 1, 0.5, True)
history.insertRecommendation(userID, 100, 1, 0.5, True)
recommendationDict: dict = {
100: 0.35,
125: 0.25,
95: 0.15,
45: 0.1,
78: 0.05,
68: 0.05,
32: 0.02,
6: 0.01,
18: 0.01,
47: 0.01
}
recommendationSrs: Series = Series(recommendationDict)
penalty: APenalization = PenalUsingReduceRelevance(penaltyLinear,
[0.8, 0.2, 100],
penaltyLinear,
[1.0, 0.2, 100], 100)
pRecommendationSrs: Series = penalty.runOneMethodPenalization(
userID, recommendationSrs, history)
print(pRecommendationSrs)
def exportAPenaltyToolOLin0802HLin1002(numberOfAggrItems: int):
return PenalUsingReduceRelevance(penaltyLinear,
[0.8, 0.2, numberOfAggrItems],
penaltyLinear, [1.0, 0.2, 100], 100)
def exportAPenaltyToolOStat08HLin1002(numberOfAggrItems: int):
return PenalUsingReduceRelevance(penaltyStatic, [1.0], penaltyLinear,
[1.0, 0.2, 100], 100)
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
)
penalization: APenalization = PenalUsingReduceRelevance(
penaltyLinear, [0.8, 0.2, 3], penaltyLinear, [1.0, 0.2, 3], 3)
aggr: AggrContextFuzzyDHondtINF = AggrContextFuzzyDHondtINF(
historyDF,
{ # empty instance of AHistory is OK for ST dataset
AggrContextFuzzyDHondtINF.ARG_EVAL_TOOL: eToolContext, # eTool
AggrContextFuzzyDHondtINF.ARG_SELECTOR: TheMostVotedItemSelector(
{}), # ? FuzzyDHondt needs this, not contextAggr
AggrContextFuzzyDHondtINF.ARG_PENALTY_TOOL: penalization
})
# 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)
# ...
# ...
# ...
# 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)