def main():
command = sys.argv[1]
no = int(sys.argv[2])
if command == "CP":
if no == 1:
tensor = MovieTensor(1)
tensor.getTensor()
elif no == 2:
tensor = MovieTensor(2)
tensor.getTensor()
elif command == "SVD":
allactormoviesdata = pandas.read_csv("movie-actor.csv")
alltagsdata = pandas.read_csv("mltags.csv")
allactormoviesdata['max_actor_rank'] = allactormoviesdata.groupby(
['movieid'])['actor_movie_rank'].transform(max)
allactormoviesdata['min_actor_rank'] = allactormoviesdata.groupby(
['movieid'])['actor_movie_rank'].transform(min)
allactormoviesdata['actor_rank_weightage'] = allactormoviesdata.apply(
compute_actor_weightage, axis=1)
#
# print(allactormoviesdata)
min_timestamp = pandas.to_datetime(min(alltagsdata['timestamp']))
max_timestamp = pandas.to_datetime(max(alltagsdata['timestamp']))
alltagsdata['timestamp_weightage'] = alltagsdata.apply(
CalculateTimestampWeights,
axis=1,
args=(min_timestamp, max_timestamp))
mergeddata = allactormoviesdata[[
'actorid', 'movieid', 'actor_rank_weightage'
]].merge(alltagsdata[['movieid', 'tagid', 'timestamp_weightage']],
on='movieid')
#print(mergeddata[mergeddata['actorid'].isin([878356,1860883,316365,128645])])
mergeddata['total_weightage'] = mergeddata.apply(
aggregate_tf_weightages, axis=1)
mergeddata['tag_weightage'] = mergeddata.groupby(
['actorid', 'tagid'])['total_weightage'].transform('sum')
tfdata = mergeddata[['actorid', 'tagid', 'tag_weightage'
]].drop_duplicates(subset=['tagid', 'actorid'])
tfdata['total_weightage_actor'] = tfdata.groupby(
['actorid'])['tag_weightage'].transform('sum')
tfdata['tf'] = tfdata.apply(ComputeTF, axis=1)
taglist = tfdata['tagid'].tolist()
alltagsdata = pandas.read_csv("mltags.csv")
alltagsdata = alltagsdata[alltagsdata['tagid'].isin(taglist)]
#print(alltagsdata)
allactormoviesdata = pandas.read_csv("movie-actor.csv")
requiredtagsdata = alltagsdata.merge(allactormoviesdata, on='movieid')
requiredtagsdata.drop_duplicates(subset=['tagid', 'actorid'],
inplace=True)
requiredtagsdata['actor_count'] = requiredtagsdata.groupby(
'tagid')['actorid'].transform('count')
requiredtagsdata.drop_duplicates(subset=['tagid'], inplace=True)
actordata = pandas.read_csv("imdb-actor-info.csv")
total_actors = actordata.shape[0]
requiredtagsdata['idf'] = requiredtagsdata.apply(
ComputeIDF, axis=1, total_actors=total_actors)
#
# print(total_actors)
# print(requiredtagsdata)
tfidfdata = ProcessTFandIDFtoTFIDF(tfdata,
requiredtagsdata[['tagid', 'idf']])
# print(tfdata)
#tfidfdata = tfidfdata[tfidfdata['actorid'].isin([878356,1860883,316365,128645])]
#print(tfidfdata)
actor_tag_matrix = tfidfdata.pivot_table(index='actorid',
columns='tagid',
values='tfidf',
fill_value=0)
print "Actor Tag Matrix"
print actor_tag_matrix
tf = TFIDF("", 1, "_actor_")
tf.calcMoviesVector()
