def tfUserTag(userId):
usrObj = User(userId)
movies = di.getUserMovies(userId)
for movieId in movies:
movieId = movieId[0]
mv = Movie(
movieId,
0) # Here the actor movie rank is not reqd., setting this to 0
movieTags = di.getMovieTags(movieId)
for movieTag in movieTags:
tagId = movieTag[0]
timeStamp = movieTag[1]
mv.addTag(tagId, timeStamp)
usrObj.addMovie(mv)
tfVector = {}
usrObj.setUnqTags()
unqTags = usrObj.getUnqTags()
#print(unqTags)
for tagId in unqTags:
tfFactorTag = 0
for movie in usrObj.getMovies():
searchTags = movie.getTags()
tfFactor = 0
totalMovieWeight = 0
for tag in searchTags:
if (tag.getId() == tagId):
tfFactor = tfFactor + tag.getTimeWeight()
#print(tfFactor)
totalMovieWeight = totalMovieWeight + 1
if (totalMovieWeight != 0):
tfFactorTag = tfFactorTag + tfFactor / totalMovieWeight
tfVector[tagId] = tfFactorTag
tfVector = utils.sortByValue(tfVector)
return utils.normalizeVector(tfVector)
def tfIdfActorTag(actorId, idfActVector):
tfVector = tfCalc.tfActorTag(actorId)
tfIdfVector = {}
tags = tfVector.keys()
for tag in tags:
tfIdfVector[tag] = tfVector[tag] * idfActVector[tag]
#print('actor',tfIdfVector)
tfIdfVector = utils.sortByValue(tfIdfVector)
return utils.normalizeVector(tfIdfVector)
def tfIdfGenreTag(genre, idfGenVector):
tfVector = tfCalc.tfGenreTag(genre)
tfIdfVector = {}
tags = tfVector.keys()
for tag in tags:
tfIdfVector[tag] = tfVector[tag] * idfGenVector[tag]
#print('genre',tfIdfVector)
tfIdfVector = utils.sortByValue(tfIdfVector)
return utils.normalizeVector(tfIdfVector)
def tfIdfUserTag(userId, idfUserVector):
tfVector = tfCalc.tfUserTag(userId)
tfIdfVector = {}
tags = tfVector.keys()
for tag in tags:
tfIdfVector[tag] = tfVector[tag] * idfUserVector[tag]
#print('user',tfIdfVector)
tfIdfVector = utils.sortByValue(tfIdfVector)
return utils.normalizeVector(tfIdfVector)
#idfVect = idfMovieTag()
#tfIdfVect = tfIdfMovieTag('3189', idfVect)
#print((tfIdfVect))
def tfActorTag(actorId):
movies = di.getActorMovies(actorId)
#print(movies)
actor = Actor(actorId)
for movie in movies:
# Here the first element in the entry is movieId and second is the actor rank
movieId = movie[0]
rank = movie[1]
# Create the Movie obj and add to the Actor
mv = Movie(movieId, rank)
# Get the tags of movie
movieTags = di.getMovieTags(movieId)
#print(movieId)
#print(movieTags)
# Calculate the weight of the tags
for movieTag in movieTags:
tagId = movieTag[0]
timeStamp = movieTag[1]
mv.addTag(tagId, timeStamp)
actor.addMovie(mv)
tfVector = {}
actor.setUnqTags()
unqTags = actor.getUnqTags()
for tagId in unqTags:
tfFactorTag = 0
#print("tagId "+tagId)
for movie in actor.getMovies():
searchTags = movie.getTags()
tfFactor = 0
totalMovieWeight = 0
movRankWeight = movie.getRWeight()
#print(movRankWeight)
for tag in searchTags:
#print(tag.getId())
if (tag.getId() == tagId):
tfFactor = tfFactor + tag.getTimeWeight()
totalMovieWeight = totalMovieWeight + 1
#print(tfFactor)
#print(totalMovieWeight)
if (
totalMovieWeight != 0
): # Check this condition because their are movies with no tags
tfFactorTag = tfFactorTag + (movRankWeight *
tfFactor) / totalMovieWeight
tfVector[tagId] = tfFactorTag
tfVector = utils.sortByValue(tfVector)
return utils.normalizeVector(tfVector)
def tfGenreTag(genre):
genObj = createGenObj(genre)
unqTags = genObj.getUnqTags()
tfVector = {}
#print(unqTags)
for tagId in unqTags:
tfFactorTag = 0
for movie in genObj.getMovies():
searchTags = movie.getTags()
tfFactor = 0
totalMovieWeight = 0
for tag in searchTags:
if (tag.getId() == tagId):
tfFactor = tfFactor + tag.getTimeWeight()
#print(tfFactor)
totalMovieWeight = totalMovieWeight + 1
if (totalMovieWeight != 0):
tfFactorTag = tfFactorTag + tfFactor / totalMovieWeight
tfVector[tagId] = tfFactorTag
tfVector = utils.sortByValue(tfVector)
return utils.normalizeVector(tfVector)
def getVals(cls, tweet):
before = len(cls.uniq_words)
tfMap = {} # stores frequency for a term in a tweet
words = tweet.getTokens()
for w in words:
if w in tfMap:
tfMap[w] += 1
else:
# when the word comes up for the first time, we
tfMap[w] = 1
if w in cls.uniq_words:
cls.uniq_words[w] += 1
else:
cls.uniq_words[w] = 1
cls.pos_map[w] = cls.pos_in_map
cls.pos_in_map += 1
cls.num_prev_tweet += 1
size_increase = len(cls.uniq_words) - before
vec = {}
for k,v in tfMap.iteritems():
idf_score = 0
if k in cls.idfMap:
idf_score = cls.idfMap[k] # get the score
# update the score
cls.idfMap[k] = math.log10((cls.num_prev_tweet) / (cls.uniq_words[k] + 1))
else:
idf_score = math.log10(cls.num_prev_tweet)
cls.idfMap[k] = idf_score
vec[cls.pos_map[k]] = v * idf_score
vec = utils.normalizeVector(vec)
tweet.setVector(vec)
return size_increase