def task2b():
DataHandler.vectors()
DataHandler.createDictionaries1()
coactor_similarity_df, actorList = DataHandler.coactor_siilarity_matrix()
u, sigma, vt = decompositions.SVDDecomposition(coactor_similarity_df, 3)
semantics = np.matrix(vt).tolist()
actorIdActorsDf = DataHandler.actor_info_df
actorsInDf = list(coactor_similarity_df.index)
print("Top 3 semantics are:")
for semantic in semantics:
prettyPrintActorVector(semantic, actorsInDf, actorIdActorsDf)
print("")
split_group_with_index = formatter.splitGroup(u, 3)
print("The three groupings are:")
groups = tasksBusiness.get_partition_on_ids(split_group_with_index,
actorIdActorsDf['name'])
for x, v in groups.items():
print('Group ' + str(x + 1) + ' : ' + str(v))
print(" ")
print()
def PersnalizedPageRank_top10_SimilarCoActors(seed):
DataHandler.createDictionaries1()
DataHandler.create_actor_actorid_map()
coactcoact, ignoreVariable = DataHandler.coactor_siilarity_matrix()
actor_actorid_map = DataHandler.actor_actorid_map
alpha = constants.ALPHA
act_similarities = ppr.personalizedPageRank(coactcoact,seed,alpha)
actors = list(coactcoact.index)
actorDF = pd.DataFrame(pd.Series(actors),columns = ['Actor'])
actorDF['Actor'] = actorDF['Actor'].map(lambda x:actor_actorid_map.get(x))
Result = pd.concat([act_similarities,actorDF],axis = 1)
sortedResult=Result.sort_values(by=0,ascending=False).head(15)
seedAcotorNames = [actor_actorid_map.get(i) for i in seed]
print('Co Actors similar to the following seed actors: '+str(seedAcotorNames))
for index in sortedResult.index:
if sortedResult.loc[index,'Actor'] not in seedAcotorNames:
print(sortedResult.loc[index,'Actor']+' '+ str(sortedResult.loc[index,0]))
