def partition_components(decomposed):
imdb_actor_info = util.read_imdb_actor_info()
actor_list = imdb_actor_info.id.unique()
imdb_actor_info = util.read_imdb_actor_info()
actor_list = imdb_actor_info[imdb_actor_info['id'].isin(
actor_list)]['name'].tolist()
mlmovies = util.read_mlmovies()
movies_list = mlmovies.movieid.unique()
movies_list = mlmovies[mlmovies['movieid'].isin(
movies_list)]['moviename'].tolist()
year_list = mlmovies.year.unique()
partitions = {
1: {
'actor': [],
'movie': [],
'year': []
},
2: {
'actor': [],
'movie': [],
'year': []
},
3: {
'actor': [],
'movie': [],
'year': []
},
4: {
'actor': [],
'movie': [],
'year': []
},
5: {
'actor': [],
'movie': [],
'year': []
}
}
for j, actor_vec in enumerate(decomposed[0]):
partition_num = np.argmax(actor_vec) + 1
partitions[partition_num]['actor'].append(actor_list[j])
for j, movie_vec in enumerate(decomposed[1]):
partition_num = np.argmax(movie_vec) + 1
partitions[partition_num]['movie'].append(movies_list[j])
for j, year_vec in enumerate(decomposed[2]):
partition_num = np.argmax(year_vec) + 1
partitions[partition_num]['year'].append(year_list[j])
util.print_partition(partitions)
util.write_partition_output_file(partitions, output_file)
def main():
if len(sys.argv) < 2:
print('Expected arguments are not provided.')
return
genre = sys.argv[1]
no_of_components = 4
imdb_actor_info = util.read_imdb_actor_info()
#print imdb_actor_info
tf_idf_matrix = util.get_tf_idf_matrix(genre)
actor_list = list(tf_idf_matrix.columns.values)
actor_list = imdb_actor_info[imdb_actor_info['id'].isin(actor_list)]['name'].tolist()
#print actor_list
pca = PCA(n_components=no_of_components)
pca.fit(tf_idf_matrix)
concepts = []
for i in range(no_of_components):
concept = []
for j, component in enumerate(pca.components_[i]):
concept.append((actor_list[j], component))
concept.sort(key=lambda tup: abs(tup[1]), reverse=True)
concepts.append(concept)
util.print_output(genre, concepts)
util.write_output_file(genre, concepts, output_file)
def main():
if len(sys.argv) < 2:
print('Expected arguments are not provided.')
return
movieid = int(sys.argv[1])
mlmovies = util.read_mlmovies()
movie_actors = util.read_movie_actor()
imdb_actor_info = util.read_imdb_actor_info()
input_movie = mlmovies[mlmovies['movieid'] == movieid]['moviename'].values[0]
actors_of_movie = movie_actors.where(movie_actors['movieid']==movieid).dropna().loc[:,'actorid'].unique()
#print (actors_of_movie)
movie_matrix = util.get_movie_tf_idf_matrix()
actor_matrix = util.get_actor_tf_idf_matrix()
#print(actor_matrix.shape)
input_movie_vector = pd.DataFrame(movie_matrix.loc[movieid])#.transpose()
#print(input_movie_vector.shape)
similarity_matrix = actor_matrix.dot(input_movie_vector)
similarity_matrix = similarity_matrix[~similarity_matrix.index.isin(actors_of_movie)]
#print(similarity_matrix)
actors = []
for index, row in similarity_matrix.iterrows():
actor_name = imdb_actor_info[imdb_actor_info['id'] == index]['name'].values[0]
actors.append((index, actor_name, similarity_matrix.loc[index][movieid]))
actors.sort(key=lambda tup: tup[2], reverse=True)
#print (actors)
util.print_output(movieid, input_movie, actors[:no_of_actors])
util.write_output_file(movieid, input_movie, actors[:no_of_actors], output_file)
def main():
if len(sys.argv) < 2:
print('Expected arguments are not provided.')
return
actorid = int(sys.argv[1])
imdb_actor_info = util.read_imdb_actor_info()
input_actor = imdb_actor_info[imdb_actor_info['id'] ==
actorid]['name'].values[0]
tf_idf_matrix = util.get_tf_idf_matrix()
#print (tf_idf_matrix)
input_actor_tf_idf = tf_idf_matrix.loc[actorid]
#print (input_actor_tf_idf)
actors = []
for index, row in tf_idf_matrix.iterrows():
actor_name = imdb_actor_info[imdb_actor_info['id'] ==
index]['name'].values[0]
actors.append((index, actor_name, 1 - cosine(row, input_actor_tf_idf)))
other_actors = list(filter(lambda tup: tup[0] != actorid, actors))
other_actors.sort(key=lambda tup: tup[2], reverse=True)
util.print_output(actorid, input_actor, other_actors[:no_of_actors])
util.write_output_file(actorid, input_actor, other_actors[:no_of_actors],
output_file)
def main():
if len(sys.argv) < 2:
print('Expected arguments are not provided.')
return
actorid = int(sys.argv[1])
imdb_actor_info = util.read_imdb_actor_info()
input_actor_name = imdb_actor_info[imdb_actor_info['id'] == actorid]['name'].values[0]
tf_idf_matrix = util.get_tf_idf_matrix()
#print(tf_idf_matrix)
actor_tf_idf = tf_idf_matrix.loc[actorid]
#print(actor_tf_idf)
svd = SVD(n_components=no_of_components)
svd.fit(tf_idf_matrix)
svd_df = pd.DataFrame(svd.transform(tf_idf_matrix), index=tf_idf_matrix.index)
input_actor_row = svd_df.loc[actorid]
actors = []
for index, row in svd_df.iterrows():
name = imdb_actor_info[imdb_actor_info['id'] == index]['name'].values[0]
actors.append((index, name, 1 - cosine(row, input_actor_row)))
other_actors = list(filter(lambda tup: tup[0] != actorid, actors))
other_actors.sort(key=lambda tup: tup[2], reverse=True)
util.print_output(actorid, input_actor_name, other_actors[:no_of_actors])
util.write_output_file(actorid, input_actor_name, other_actors[:no_of_actors], output_file)
def main(): mlmovies = util.read_mlmovies() imdb_actor_info = util.read_imdb_actor_info() movie_actor = util.read_movie_actor() movies_list = mlmovies.movieid.unique() year_list = mlmovies.year.unique() actor_list = imdb_actor_info.id.unique() movie_year_matrix = [] actor_movie_year_grouped = pd.merge(movie_actor, mlmovies, on=['movieid','movieid'], how='inner') actor_movie_year_tensor = [] count=0 for actor in actor_list: movie_year_matrix = [] for movie in movies_list: movie_year_list = [] for year in year_list: if actor_movie_year_grouped[(actor_movie_year_grouped.actorid == actor) & (actor_movie_year_grouped.movieid == movie) & (actor_movie_year_grouped.year == year)].empty: movie_year_list.append(0.0) else: movie_year_list.append(1.0) movie_year_matrix.append(movie_year_list) actor_movie_year_tensor.append(movie_year_matrix) cPickle.dump( actor_movie_year_tensor, open( "actor_movie_year_tensor.pkl", "wb" ) )
def latent_actor_semantics(actor_matrix):
imdb_actor_info = util.read_imdb_actor_info()
actor_list = imdb_actor_info.id.unique()
actor_list = imdb_actor_info[imdb_actor_info['id'].isin(
actor_list)]['name'].tolist()
concepts = []
for i in range(no_of_components):
concept = []
for j, component in enumerate(np.transpose(actor_matrix)[i]):
concept.append((actor_list[j], component))
concept.sort(key=lambda tup: abs(tup[1]), reverse=True)
concepts.append(concept)
util.print_output(concepts, 'Actor')
util.write_output_file(concepts, output_file, 'Actor')