def task1c_pca(actor_id):
DataHandler.vectors()
actorTagDataframe = DataHandler.actor_tag_df()
actorTagMatrix = np.matrix(DataHandler.actor_tag_df().as_matrix())
actorIndexList = list(actorTagDataframe.index)
if (actor_id not in actorIndexList):
print("Invalid actor id or no tags present for actor. Returning")
return
components = decompositions.PCADecomposition(actorTagDataframe, 5)
#using transpose since according to page 158, p inverse = p transpose
pMatrix = np.matrix(components).transpose()
actorsInSemantics = (actorTagMatrix * pMatrix).tolist()
simAndActor = []
concernedActorInSemantics = actorsInSemantics[actorIndexList.index(actor_id)]
DataHandler.create_actor_actorid_map()
for index in range(0, len(actorsInSemantics)):
comparisonActorId = actorIndexList[index]
if (actor_id == comparisonActorId):
continue
comparisonActorSemantics = actorsInSemantics[index]
comparisonActorName = DataHandler.actor_actorid_map.get(comparisonActorId)
simAndActor.append((metrics.l2Norm(concernedActorInSemantics, comparisonActorSemantics), comparisonActorName))
result = sorted(simAndActor, key=operator.itemgetter(0), reverse=False)
top10Actors = result[0:10]
print("Top 10 actors similar to " + str(DataHandler.actor_actorid_map.get(actor_id)) + " are: ")
for tup in top10Actors:
print(tup[1] + " : " + str(tup[0]))
return
def task1c_tfidf(actor_id):
DataHandler.vectors()
actorTagDataframe = DataHandler.actor_tag_df()
actorsTags = np.matrix(actorTagDataframe.as_matrix()).tolist()
actorIndexList = list(actorTagDataframe.index)
if (actor_id not in actorIndexList):
print("Invalid actor id or no tags present for actor. Returning")
return
simAndActor = []
concernedActor = actorsTags[actorIndexList.index(actor_id)]
totalActors = len(actorIndexList)
DataHandler.create_actor_actorid_map()
for index in range(0, totalActors):
comparisonActorId = actorIndexList[index]
if(actor_id == comparisonActorId):
continue
comparisonActorName = DataHandler.actor_actorid_map.get(comparisonActorId)
comparisonActor = actorsTags[index]
comparisonScore = metrics.l2Norm(concernedActor, comparisonActor)
simAndActor.append((comparisonScore, comparisonActorName))
result = sorted(simAndActor, key=operator.itemgetter(0), reverse=False)
top10Actors = result[0:10]
print("Top 10 actors similar to " + str(DataHandler.actor_actorid_map.get(actor_id)) + " are: ")
for tup in top10Actors:
print(tup[1] + " : " + str(tup[0]))
return
def actor_task1c_SVD(actor_id):
DataHandler.vectors()
acdf = DataHandler.actor_tag_df()
indexList=list(acdf.index)
if (actor_id not in indexList):
print("Invalid actor id or no tags present for actor. Returning")
return
U, Sigma, VT = decompositions.SVDDecomposition(acdf, 5)
simAndActor = []
actorInSemantics = U[indexList.index(actor_id)]
DataHandler.create_actor_actorid_map()
for index in range(0, len(U)):
comparisonActorId = indexList[index]
if (comparisonActorId == actor_id):
continue
actorName = DataHandler.actor_actorid_map.get(comparisonActorId)
similarityScore = metrics.l2Norm(actorInSemantics, U[index])
simAndActor.append((similarityScore, actorName))
result = sorted(simAndActor, key=operator.itemgetter(0), reverse=False)
print("Top 10 Actors similar to " + str(DataHandler.actor_actorid_map.get(actor_id)) + " are:")
top10Actors = result[0:10]
for tup in top10Actors:
print(tup[1] + " : " + str(tup[0]))
return
def task1dImplementation_SVD(movie_id):
DataHandler.vectors()
DataHandler.createDictionaries1()
movieid_name_map = DataHandler.movieid_name_map
actor_tag_df = DataHandler.actor_tag_df()
movie_tag_df = DataHandler.load_movie_tag_df()
moviesIndexList = list(movie_tag_df.index)
actorsIndexList = list(actor_tag_df.index)
actorsSize = len(actorsIndexList)
if (movie_id not in moviesIndexList):
print("Movie " + movieid_name_map.get(movie_id) +
" not present in mltags data. Quitting")
return
actorU, actorSigma, actorV = decompositions.SVDDecomposition(
actor_tag_df, 5)
tagsToActorSemantics = (np.matrix(actorV)).transpose()
movieTagMatrix = np.matrix(movie_tag_df.as_matrix())
movieInTags = movieTagMatrix[moviesIndexList.index(movie_id)]
movieInActorSemantics = (movieInTags * tagsToActorSemantics).tolist()[0]
actorsInSemantics = np.matrix(actorU)
actorsWithScores = []
DataHandler.create_actor_actorid_map()
actorsForMovie = DataHandler.movie_actor_map.get(movie_id)
for index in range(0, actorsSize):
actor_id = actorsIndexList[index]
if actor_id in actorsForMovie:
continue
actorMatrix = actorsInSemantics[index]
actor = (actorMatrix.tolist())[0]
actorName = DataHandler.actor_actorid_map.get(actor_id)
similarityScore = metrics.l2Norm(actor, movieInActorSemantics)
actorsWithScores.append((similarityScore, actorName))
resultActors = sorted(actorsWithScores,
key=operator.itemgetter(0),
reverse=False)
top10Actors = resultActors[0:10]
print("10 Actors similar to movie " + str(movieid_name_map.get(movie_id)) +
" are: ")
for tup in top10Actors:
print(tup[1] + " : " + str(tup[0]))
return
def task1d_pca(movie_id):
DataHandler.vectors()
DataHandler.createDictionaries1()
movieid_name_map = DataHandler.movieid_name_map
actor_tag_df = DataHandler.actor_tag_df()
movie_tag_df = DataHandler.load_movie_tag_df()
actorTagMatrix = np.matrix(actor_tag_df.as_matrix())
movieTagMatrix = np.matrix(movie_tag_df.as_matrix())
actorIndexList = list(actor_tag_df.index)
movieIndexList = list(movie_tag_df.index)
if (movie_id not in movieIndexList):
print("Movie " + movieid_name_map.get(movie_id) +
" not present in mltags data. Quitting")
return
actorSemantics = decompositions.PCADecomposition(actor_tag_df, 5)
actorP = np.matrix(actorSemantics).transpose()
movieInTags = movieTagMatrix[movieIndexList.index(movie_id)]
movieInActorSemantics = (movieInTags * actorP).tolist()[0]
actorsInActorSemantics = (actorTagMatrix * actorP).tolist()
DataHandler.create_actor_actorid_map()
actorsForMovie = DataHandler.movie_actor_map.get(movie_id)
DataHandler.create_actor_actorid_map()
actorsSize = len(actorsInActorSemantics)
simAndActor = []
for index in range(0, actorsSize):
actorId = actorIndexList[index]
if (actorId in actorsForMovie):
continue
actorInSemantics = actorsInActorSemantics[index]
actorName = DataHandler.actor_actorid_map.get(actorId)
score = metrics.l2Norm(actorInSemantics, movieInActorSemantics)
simAndActor.append((score, actorName))
result = sorted(simAndActor, key=operator.itemgetter(0), reverse=False)
print("Top 10 actors similar to movie: " +
str(movieid_name_map.get(movie_id)) + " are: ")
top10Actors = result[0:10]
for tup in top10Actors:
print(tup[1] + " : " + str(tup[0]))
return
def task1d_tfidf(movie_id):
DataHandler.vectors()
DataHandler.createDictionaries1()
actorTagDataframe = DataHandler.actor_tag_df()
movie_tag_df = DataHandler.load_movie_tag_df()
movieid_name_map = DataHandler.movieid_name_map
actorsTags = np.matrix(actorTagDataframe.as_matrix()).tolist()
actorIndexList = list(actorTagDataframe.index)
movieIndexList = list(movie_tag_df.index)
movieTagMatrix = np.matrix(movie_tag_df.as_matrix())
if (movie_id not in movieIndexList):
print("Movie " + movieid_name_map.get(movie_id) +
" not present in mltags data. Quitting")
return
actorsForMovie = DataHandler.movie_actor_map.get(movie_id)
simAndActor = []
movieInTags = movieTagMatrix[movieIndexList.index(movie_id)].tolist()[0]
totalActors = len(actorIndexList)
DataHandler.create_actor_actorid_map()
for index in range(0, totalActors):
actorId = actorIndexList[index]
if (actorId in actorsForMovie):
continue
actorName = DataHandler.actor_actorid_map.get(actorId)
actorinTags = actorsTags[index]
comparisonScore = metrics.l2Norm(movieInTags, actorinTags)
simAndActor.append((comparisonScore, actorName))
result = sorted(simAndActor, key=operator.itemgetter(0), reverse=False)
top10Actors = result[0:10]
print("Top 10 actors similar to " + str(movieid_name_map.get(movie_id)) +
" are: ")
for tup in top10Actors:
print(tup[1] + " : " + str(tup[0]))
return
def similarMovieActor_LDA(givenMovie):
DataHandler.vectors()
DataHandler.createDictionaries1()
DataHandler.create_actor_actorid_map()
givenActor_similarity = defaultdict(float)
actor_tag_dff = DataHandler.actor_tag_df()
movie_tag_dff = DataHandler.load_movie_tag_df()
actorTagMatrix = np.matrix(actor_tag_dff.as_matrix())
movieTagMatrix = np.matrix(movie_tag_dff.as_matrix())
movieid_name_map = DataHandler.movieid_name_map
actorIndexList = list(actor_tag_dff.index)
movieIndexList = list(movie_tag_dff.index)
if (givenMovie not in movieIndexList):
print("Movie " + movieid_name_map.get(givenMovie) +
" not present in mltags data. Quitting")
return
movieInTags = movieTagMatrix[movieIndexList.index(givenMovie)]
actorsForMovie = DataHandler.movie_actor_map.get(givenMovie)
ldaModel, doc_term_matrix, id_Term_map = decompositions.LDADecomposition(
actor_tag_dff, 5, constants.actorTagsSpacePasses)
for otherActor in actorIndexList:
mo1 = DataHandler.representDocInLDATopics(movie_tag_dff, givenMovie,
ldaModel)
if otherActor not in actorsForMovie:
ac2 = DataHandler.representDocInLDATopics(actor_tag_dff,
otherActor, ldaModel)
givenActor_similarity[otherActor] = (
metrics.simlarity_kullback_leibler(mo1, ac2))
#print(sorted(givenActor_similarity.items(),key = itemgetter(1),reverse=True))
top10 = sorted(givenActor_similarity.items(),
key=itemgetter(1),
reverse=False)[0:11]
for actors in top10:
print(DataHandler.actor_actorid_map.get(actors[0]), actors[1])
return
