def visualisationOfContourLines():
#modelConf:LinPrefModelConfiguration
modelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
aggrLevel = 0.90
# fileNameUsers:String
fileNameUsers = "../datasets/usersRefractedModel.csv"
# users:User[]
users = readUsers(fileNameUsers);
pointsDataCube = [Point(0.73, 0.755), Point(0.125, 0.45), Point(0.624, 0.15)]
labels = ["A", "B", "C"]
for indexI in range(len(users)):
user = users[indexI]
user.printUser()
userProfileModel1 = user.exportUserProfileModel();
#userProfileModel1 = user.exportUserProfileRefractedModel();
# title:String
title = 'Lin. pref. model'
# painting:Painting
painting = Painting(modelConf, title)
painting.paintOnlyModel(userProfileModel1, aggrLevel, 'g')
painting.paintPoints(pointsDataCube, [], labelsDataCube=[], labelsPrefCube=[], color="b", size=1)
painting.paint(indexI);
#painting.save(indexI);
break;
def paintPreferenceCube():
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(2.0, 1.25, 1.0, 1.0)
#points:Point[]
points = [
Point(0.1, 0.1),
Point(0.2, 0.2),
Point(0.3, 0.4),
Point(0.9, 0.5)
]
g = PaintingPreferenceCubeModel(linPrefModelConf, "Data Cube")
g.paintPreferencerCube(points)
g.paint(1)
class InputSimpleShow:
# linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
# userProfileModelStructured:UserProfileModelStructured
userProfileModelStructured = UserProfileModelStructured(
PrefFncRefractedModel(0.5), PrefFncRefractedModel(0.5),
AggrFnc([0.5, 0.5]))
# aggrLevel:Float
aggrLevel = 0.8
# pointsVisitedDC:list<PointWithID>
pointsWithIDVisitedDC = [
PointWithID(Point(0.70, 0.75), "A"),
PointWithID(Point(0.15, 0.45), "B"),
PointWithID(Point(0.60, 0.15), "C"),
PointWithID(Point(0.40, 0.85), "D"),
PointWithID(Point(0.5, 0.5), "E")
]
# pointsNoVisitedDC:list<PointWithID>
pointsWithIDNoVisitedDC = [
PointWithID(Point(0.25, 0.15), "M"),
PointWithID(Point(0.1, 0.65), "N"),
PointWithID(Point(0.35, 0.85), "O"),
PointWithID(Point(0.9, 0.7), "P"),
PointWithID(Point(0.65, 0.45), "Q")
]
def generatePointsWithIDVisitedDC(count):
points = []
for i in range(count):
x = random.uniform(0, 1)
y = random.uniform(0, 1)
pI = PointWithID(Point(x, y), i)
points.append(pI)
return points
def generatePointsWithIDNoVisitedDC(count):
points = []
for i in range(count):
x = random.uniform(0, 1)
y = random.uniform(0, 1)
pI = PointWithID(Point(x, y), 100 + i)
points.append(pI)
return points
def show(self, width, height):
# linPrefModelConf:LinPrefModelConfiguration
self.linPrefModelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
# userID:int
self.userID = 1
# dimensionX:int
self.dimensionX = 0
self.dimensionY = 1
self.wx = 0.5
# aggrLevel:Float
self.aggrLevel = 0.8
self.form();
self.model1()
def __generateRatings(self, dmDescription, upModelsDef, pointNDWithIDs):
# upModel:UserProfileModelND
upModel = upModelsDef.exportUserProfileModelND()
# points:list<PointND>
points = [pointI.point for pointI in pointNDWithIDs]
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
# ratingValues:List<float>
ratingValues = upModel.preferenceOfPointsInDataCube(
points, linPrefModelConf)
# ratings:List<Rating>
ratings = [
Rating(upModelsDef.userID, pointNDWithIDs[i].pointID,
ratingValues[i]) for i in range(len(ratingValues))
]
return ratings
def __init__(self):
super().__init__()
self.setWindowTitle('Linear Monotone Preference Model')
self.resize(550, 550)
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
self.layout = QtWidgets.QVBoxLayout(self._main)
self.model = ModelOfPoints()
self.c = CanvasDimensions(550, 550)
#widget = CanvasWidget(self.c, self.model)
#self.layout.addWidget(widget)
linPrefModelConf = LinPrefModelConfiguration(1, 1, 1, 1)
model = GraphicalModel(linPrefModelConf)
model.initFigure("ahoj")
self.layout.addWidget(model.fig1)
class InputTwoUsers:
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(2.0, 1.25, 1.0, 1.0)
#user2D1:User2D
user2D1 = User2D(1, 1.5, 0.3, 0.66)
#user2D2:User2D
user2D2 = User2D(2, 0.5, 0.9, 0.33)
# aggrLevel1:Float
aggrLevel1 = 0.667
# aggrLevel2:Float
aggrLevel2 = 0.667
#pointsWithIdVisitedDC:Point[]
#pointsDataCube = [Point(0.73, 0.755), Point(1.125, 0.45), Point(1.624, 0.15)]
pointsWithIdVisitedDC = [
PointWithID(Point(0.5, 0.5), "A"),
PointWithID(Point(0.75, 0.5), "B"),
PointWithID(Point(0.5, 0.75), "C"),
PointWithID(Point(0.75, 0.75), "D")
]
def paintData():
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
#userProfileModel1:UserProfileModel
userProfileModel1 = getUserProfileModel01()
#userProfileModel2:UserProfileModel
userProfileModel2 = getUserProfileModel02()
# fileNameItems:String
fileNameItems = "../datasets/itemsRefractedModel.csv"
# items:Item[]
items = readItems(fileNameItems);
#pointsDataCube:Point[]
pointsDataCube = [Point(i.x, i.y) for i in items]
pointsPrefCube1 = userProfileModel1.pointsDataCubeToPointsPrefCube(pointsDataCube);
pointsPrefCube2 = userProfileModel2.pointsDataCubeToPointsPrefCube(pointsDataCube);
# preferences:float[]
preferences1 = userProfileModel1.preferenceOfPointsInPC(pointsPrefCube1, linPrefModelConf);
preferences2 = userProfileModel2.preferenceOfPointsInPC(pointsPrefCube2, linPrefModelConf);
rmseVal = rmse(preferences1, preferences2)
print("rmse: ", rmseVal);
#title:String
title = 'Lin. pref. model';
#painting:Painting
painting = Painting(linPrefModelConf, title)
painting.paintModelOnlyPoints(userProfileModel1, pointsDataCube, pointsPrefCube1, "r");
painting.paintModelOnlyPoints(userProfileModel2, pointsDataCube, pointsPrefCube1, "g");
painting.paint("");
class InputThreshold:
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(2.0, 1.25, 1.0, 1.0)
#title:String
title = "Preferenc Cube"
#points:Point[]
points = [
Point(0.1, 0.1),
Point(0.2, 0.2),
Point(0.3, 0.4),
Point(0.9, 0.5)
]
# pointsWithIDs:PointWithID[]
pointsWithIDs = [
PointWithID(Point(0.1, 0.9), 'A'),
PointWithID(Point(0.2, 0.8), 'B'),
PointWithID(Point(0.3, 0.7), 'C'),
PointWithID(Point(0.4, 0.6), 'D'),
PointWithID(Point(0.5, 0.5), 'E')
]
def convertRefractedModelToTrianularModel():
#modelConf:LinPrefModelConfiguration
modelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
# usersAll:User[]
usersAll = readUsers()
#print("usersAll: ", len(usersAll))
# fileNameRatings:String
fileNameRatings = ".." + os.sep + "datasets" + os.sep + "ratingsRefractedModel.csv"
# ratingsAll:Rating[]
ratingsAll = readRatings(fileNameRatings)
#print("ratingsAll: ", len(ratingsAll))
# itemsAll:Item[]
itemsAll = readItems()
#print("itemsAll: ", len(itemsAll))
# ratingsNew:Rating[]
ratingsNew = []
# userIds:int[]
userIds = [u.uid for u in usersAll]
for userIdI in userIds:
#print(userIdI)
# user:User
user = [u for u in usersAll if u.uid == userIdI][0]
#user.printUser();
#userProfileModel = user.exportUserProfileTriangularModel()
userProfileModel = user.exportUserProfileRefractedModel()
# ratings:Rating[]
ratings = [r for r in ratingsAll if r.uid == userIdI]
#print("ratings: ", len(ratings))
# items:Item[]
items = [i for i in itemsAll if i.iid in [r.iid for r in ratings]]
#print("items: ", len(items))
# pointsDataCube:Point[]
#pointsDataCube = [i.exportAsPoint() for i in items]
# preferences:Float[]
#preferences = userProfileModel.preferenceOfPointsInDataCube(pointsInDataCube, modelConf);
# itemIndexI:int
for itemIndexI in range(0, len(items)):
# itemI:Item
itemI = items[itemIndexI]
# ratingI:Rating
ratingI = ratings[itemIndexI]
# pointInDataCubeI:Point
pointInDataCubeI = itemI.exportAsPoint()
# preferencesI:Float[]
preference2I = userProfileModel.preferenceOfPointInDataCube(
pointInDataCubeI, modelConf)
# ratingI:Rating[]
ratingI = Rating(userIdI, ratingI.iid, preference2I, 0,
ratingI.typ)
ratingsNew.append(ratingI)
fileNameRatings2 = ".." + os.sep + "datasets" + os.sep + "ratingsRectangledModel.csv"
writeRatings(ratingsNew, fileNameRatings2)
# test reading computed dataset
# ratingsAll3:Rating[]
ratingsAll3 = readRatings(fileNameRatings2)
def testDatasetRMSE():
aggrLevel = 0.95
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
# fileNameUsers:String
fileNameUsers = "../datasets/usersRefractedModel.csv"
# usersAll:User[]
usersAll = readUsers(fileNameUsers)
print("usersAll: ", len(usersAll))
# fileNameRatings:String
fileNameRatings = "../datasets/ratingsRefractedModel.csv"
# ratingsAll:Rating[]
ratingsAll = readRatings(fileNameRatings)
#print("ratingsAll: ", len(ratingsAll))
# fileNameItems:String
fileNameItems = "../datasets/itemsRefractedModel.csv"
# itemsAll:Item[]
itemsAll = readItems(fileNameItems)
#print("itemsAll: ", len(itemsAll))
userIds = [u.uid for u in usersAll]
for userIdI in userIds:
# user:User
user = [u for u in usersAll if u.uid == userIdI][0]
user.printUser()
user = user.exportUser2D()
user.printUser()
# ratings:Rating[]
ratings = [r for r in ratingsAll if r.uid == userIdI]
#print("ratings: ", len(ratings))
# ratingsTrain:Rating[]
ratingsTrain = [r for r in ratings if r.typ == 1]
# ratingsTest:Rating[]
ratingsTest = [r for r in ratings if r.typ == 2]
# itemsTrain:Item[]
itemsTrain = [
i for i in itemsAll if i.iid in [r.iid for r in ratingsTrain]
]
# itemsTest:Item[]
itemsTest = [
i for i in itemsAll if i.iid in [r.iid for r in ratingsTest]
]
# pointsTrain:Point[]
pointsTrain = [i.exportAsPoint() for i in itemsTrain]
# pointsTest:Point[]
pointsTest = [i.exportAsPoint() for i in itemsTest]
# prefsTrain:float[]
prefsTrain = [r.p2 for r in ratingsTrain]
# prefsTest:float[]
prefsTest = [r.p2 for r in ratingsTest]
#userProfileModel1 = user.exportUserProfileTriangularModel(linPrefModelConf)
userProfileModel1 = user.exportUserProfileRefractedModel(
linPrefModelConf)
# pointsPrefCubeTrain:Point[]
pointsPrefCubeTrain = userProfileModel1.pointsDataCubeToPointsPrefCube(
pointsTrain)
pointsPrefCubeTest = userProfileModel1.pointsDataCubeToPointsPrefCube(
pointsTest)
# prefsPointsPrefCubeTrain:Point[]
prefsPointsPrefCubeTrain = userProfileModel1.preferenceOfPointsInPC(
pointsPrefCubeTrain, linPrefModelConf)
prefsPointsPrefCubeTest = userProfileModel1.preferenceOfPointsInPC(
pointsPrefCubeTest, linPrefModelConf)
rmseTrainVal = rmse(prefsPointsPrefCubeTrain, prefsTrain)
print("rmseTrain: ", rmseTrainVal)
rmseTestVal = rmse(prefsPointsPrefCubeTest, prefsTest)
print("rmseTest: ", rmseTestVal)
# title:String
title = 'Lin. pref. model'
# painting:Painting
painting = Painting(linPrefModelConf, title)
painting.paintOnlyModel(userProfileModel1, aggrLevel, 'r')
painting.paintPoints(pointsTrain,
pointsPrefCubeTrain,
labelsDataCube=[],
labelsPrefCube=[],
color="b",
size=1)
painting.paintPoints(pointsTest,
pointsPrefCubeTest,
labelsDataCube=[],
labelsPrefCube=[],
color="r",
size=1)
#painting.paint(userIdI);
painting.save(userIdI)
painting.close()
def search():
aggrLevel = 0.95
#modelConf:LinPrefModelConfiguration
modelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
# fileNameUsers:String
fileNameUsers = "../datasets/usersRefractedModel.csv"
# usersAll:User[]
usersAll = readUsers(fileNameUsers)
#print("usersAll: ", len(usersAll))
# fileNameRatings:String
fileNameRatings = "../datasets/ratingsRefractedModel.csv"
# ratingsAll:Rating[]
ratingsAll = readRatings(fileNameRatings)
#print("ratingsAll: ", len(ratingsAll))
# fileNameItems:String
fileNameItems = "../datasets/itemsRefractedModel.csv"
# itemsAll:Item[]
itemsAll = readItems(fileNameItems)
#print("itemsAll: ", len(itemsAll))
userIds = [u.uid for u in usersAll]
userId = userIds[0]
print("Input:")
# user:User
user = [u for u in usersAll if u.uid == userId][0]
user.printUser()
# ratings:Rating[]
ratings = [r for r in ratingsAll if r.uid == userId]
#print("ratings: ", len(ratings))
# ratingsTrain:Rating[]
ratingsTrain = [r for r in ratings if r.typ == 1]
# ratingsTest:Rating[]
ratingsTest = [r for r in ratings if r.typ == 2]
# itemsTrain:Item[]
itemsTrain = [
i for i in itemsAll if i.iid in [r.iid for r in ratingsTrain]
]
# itemsTest:Item[]
itemsTest = [i for i in itemsAll if i.iid in [r.iid for r in ratingsTest]]
# pointsTrain:Point[]
pointsTrain = [i.exportAsPoint() for i in itemsTrain]
# pointsTest:Point[]
pointsTest = [i.exportAsPoint() for i in itemsTest]
# prefsTrain:float[]
prefsTrain = [r.p2 for r in ratingsTrain]
# prefsTest:float[]
prefsTest = [r.p2 for r in ratingsTest]
ix = user.ix
iy = user.iy
wx = user.exportNormalizedWX()
wy = user.exportNormalizedWY()
print("User ix: " + str(ix) + ", iy: " + str(iy) + ", wx: " + str(wx))
## searchAlg:RandomSearch
#searchAlg = RandomSearch()
## theBestIndivEval:IndividualEvaluated = modelConf:LinPrefModelConfiguration, pointsTrain:Point[], prefsTrain:Point[], fitnessFnc:Function, generateFnc:Function
#theBestIndivEval = searchAlg.search(modelConf, pointsTrain, prefsTrain, numberOfGenerations=10, fitnessFnc=fitnessRMSE, generateFnc=operatorGenerate)
# searchAlg:HillClimbing
searchAlg = HillClimbing()
# theBestIndivEval:IndividualEvaluated = modelConf:LinPrefModelConfiguration, pointsTrain:Point[], prefsTrain:Point[], fitnessFnc:Function, generateFnc:Function, neighborFnc:Function
theBestIndivEval = searchAlg.search(
modelConf,
pointsTrain,
prefsTrain,
numberOfGenerations=10000,
fitnessFnc=fitnessRMSE_,
generateFnc=operatorGenerate,
neighborFnc=operatorRandomMoveTriangularModel)
theBestIndiv = theBestIndivEval.individual
# userProfileModel:UserProfileModel
userProfileModel = theBestIndiv.exportUserProfileRefractedModel()
print("")
print("Result:")
#userProfileModel.print()
theBestIndiv.printIndividualUser()
# pointsPrefCubeTrain:Point[]
pointsPrefCubeTrain = userProfileModel.pointsDataCubeToPointsPrefCube(
pointsTrain)
pointsPrefCubeTest = userProfileModel.pointsDataCubeToPointsPrefCube(
pointsTest)
# prefsPointsPrefCubeTrain:Point[]
prefsPointsPrefCubeTrain = userProfileModel.preferenceOfPointsInPC(
pointsPrefCubeTrain, modelConf)
prefsPointsPrefCubeTest = userProfileModel.preferenceOfPointsInPC(
pointsPrefCubeTest, modelConf)
fitnessRMSETrain = rmse(prefsPointsPrefCubeTrain, prefsTrain)
print("fitnessRMSETrain: ", fitnessRMSETrain)
fitnessRMSETest = rmse(prefsPointsPrefCubeTest, prefsTest)
print("fitnessRMSETest: ", fitnessRMSETest)
def paintContourLinesExample():
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(2.0, 1.25, 1.0, 1.0)
#userProfileModel1:UserProfileModel
userProfileModel1 = getUserProfileModelExample01()
#userProfileModel2:UserProfileModel
userProfileModel2 = getUserProfileModelExample02()
aggrLevel = 0.667
#polygon1:Point[]
polygon1 = userProfileModel1.getMorphismOfAggregationFncToDataCubePolygon(
aggrLevel, linPrefModelConf)
# surface:float
surface1 = countPolygonSurfaceArea(polygon1)
print("Surface1: ", surface1)
#polygon1:Point[]
polygon2 = userProfileModel2.getMorphismOfAggregationFncToDataCubePolygon(
aggrLevel, linPrefModelConf)
# surface:float
surface2 = countPolygonSurfaceArea(polygon2)
print("Surface2: ", surface2)
surfaceOfOverlap = countIntersection(polygon1, polygon2)
print("Overlap: ", surfaceOfOverlap)
#pointsDataCube:Point[]
pointsDataCube = [
Point(0.73, 0.755),
Point(1.125, 0.45),
Point(1.624, 0.15)
]
labels = ["A", "B", "C"]
pointsPrefCube = userProfileModel1.pointsDataCubeToPointsPrefCube(
pointsDataCube)
pointsPrefCube2 = userProfileModel2.pointsDataCubeToPointsPrefCube(
pointsDataCube)
# title:String
title = 'Lin. pref. model'
# painting:Painting
painting = Painting(linPrefModelConf, title)
painting.paintOnlyModel(userProfileModel1, aggrLevel, 'r')
painting.paintOnlyModel(userProfileModel2, aggrLevel, 'g')
painting.paintPoints(pointsDataCube,
pointsPrefCube,
labelsDataCube=labels,
labelsPrefCube=labels,
color="r",
size=1)
painting.paintPoints(pointsDataCube,
pointsPrefCube2,
labelsDataCube=labels,
labelsPrefCube=labels,
color="b",
size=1)
painting.paint("")
def cv03():
# numberOfUsers:int
numberOfUsers = 10
#linPrefModelConf:LinPrefModelConfiguration
linPrefModelConf = LinPrefModelConfiguration(1.0, 1.0, 1.0, 1.0)
# fileNameUsers:String
fileNameUsers = "../datasets/usersRefractedModel.csv"
# fileNameRatings:String
fileNameRatings = "../datasets/ratingsRefractedModel.csv"
# fileNameItems:String
fileNameItems = "../datasets/itemsRefractedModel.csv"
# estimateUserFnc:function, testID:str
estimateUserFnc = estimateUserByTheBestRating; testID='theBestRating'
#estimateUserFnc = estimateUserByMiddleOf23; testID='middleOf23'
#estimateUserFnc = estimateUserByCenterOfMassOf23; testID='centerOfMassOf23'
#estimateUserFnc = estimateUserBy10Random; testID='10Random'
print(estimateUserFnc)
# usersAll:User[]
usersAll = readUsers(fileNameUsers);
#print("usersAll: ", len(usersAll))
# ratingsAll:Rating[]
ratingsAll = readRatings(fileNameRatings);
#print("ratingsAll: ", len(ratingsAll))
# itemsAll:Item[]
itemsAll = readItems(fileNameItems);
#print("itemsAll: ", len(itemsAll))
# errors:float[]
errors = []
# user:User
for user in usersAll:
# user2DI:User2D
user2DI = user.exportUser2D()
user2DI.printUser();
# ratings:Rating[]
ratings = [r for r in ratingsAll if r.uid == user2DI.uid]
#print("ratings: ", len(ratings))
# ratingsTrain:Rating[]
ratingsTrain = [r for r in ratings if r.typ == 1]
# ratingsTest:Rating[]
ratingsTest = [r for r in ratings if r.typ == 2]
# itemsTrain:Item[]
itemsTrain = [i for i in itemsAll if i.iid in [r.iid for r in ratingsTrain]]
# itemsTest:Item[]
itemsTest = [i for i in itemsAll if i.iid in [r.iid for r in ratingsTest]]
# pointsTrain:PointWithRating[]
pointsWithRatingTrain = [PointWithRating(itemsTrain[i].exportAsPoint(), ratingsTrain[i].p2) for i in range(len(ratingsTrain))]
# pointsTest:PointWithRating[]
pointsWithRatingTest = [PointWithRating(itemsTest[i].exportAsPoint(), ratingsTest[i].p2) for i in range(len(ratingsTest))]
# user2D:User2D, error:float
user2DNewI, errorI = process(user2DI, estimateUserFnc, pointsWithRatingTrain, pointsWithRatingTest, linPrefModelConf, testID=testID)
errors.append(errorI);
if len(errors) == numberOfUsers:
break;
print("Mean: ", mean(errors))