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
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
genre = sys.argv[1]
no_of_components = 4
genome_tags = util.read_genome_tags()
#print genome_tags
tf_idf_matrix = util.get_tf_idf_matrix(genre)
tagid_list = list(tf_idf_matrix.columns.values)
tag_list = genome_tags[genome_tags['tagId'].isin(tagid_list)]['tag'].tolist()
#print tag_list
svd = TruncatedSVD(n_components=no_of_components, n_iter=100, random_state=None)
svd.fit(tf_idf_matrix)
concepts = []
for i in range(no_of_components):
concept = []
for j, component in enumerate(svd.components_[i]):
concept.append((tag_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
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)