line_f = f.readline()
line_g = g.readline()
continue
train_dislikes = dislike_posts[0:int(cnt_dislikes*0.8)]
test_dislikes = dislike_posts[int(cnt_dislikes * 0.8):]
for dislike in train_dislikes:
dislike_text_posts.write(dislike)
dislike_text_posts.close()
if not os.path.isdir('../OK_recommend/user' + user_id):
mkdir('../OK_recommend/user' + user_id)
build_words_graph.build_graph('like_texts.txt', '../OK_recommend/user' + user_id + '/words_degrees_like.txt')
training.train_small('like_texts.txt', 'like')
build_words_graph.build_graph('dislike_texts.txt', '../OK_recommend/user' + user_id + '/words_degrees_dislike.txt')
training.train_small('dislike_texts.txt', 'dislike')
types = ['like', 'dislike']
W_like = [0] * K
W_dislike = [0] * K
score_like = dict()
score_dislike = dict()
topic_of_words_like = dict()
topic_of_words_dislike = dict()
bad_user = False
for typ in types:
topic_score, word_in_topic, word_score, word_ids, id_words, newK = extract_text_topics.extract_text_topics(typ + '_texts.txt', typ, K, user_id, model)
if len(likes) < 20:
continue
g = open('texts.txt', 'w', encoding='utf-8')
cnt_existing_posts = 0
for like in likes:
Id = like.split(", ")
group_id = Id[0]
post_id = Id[1]
text = read_post(group_id, post_id)
if text == "-1":
continue
cnt_existing_posts += 1
g.write(text)
g.close()
if cnt_existing_posts < 15:
continue
if not os.path.isdir('../OK_results/user' + user_id):
mkdir('../OK_results/user' + user_id)
build_words_graph.build_graph(
'texts.txt', '../OK_results/user' + user_id + '/words_degrees.txt')
training.train_small('texts.txt')
extract_text_topics.extract_text_topics('texts.txt', K, user_id, model)
cnt += 1
print("For %d users interests profile was built", cnt)
likes = likes.split("|")
if len(likes) < 20:
continue
g = open("texts.txt", "w", encoding="utf-8")
cnt_existing_posts = 0
for like in likes:
Id = like.split(", ")
group_id = Id[0]
post_id = Id[1]
text = read_post(group_id, post_id)
if text == "-1":
continue
cnt_existing_posts += 1
g.write(text)
g.close()
if cnt_existing_posts < 15:
continue
if not os.path.isdir("../OK_results/user" + user_id):
mkdir("../OK_results/user" + user_id)
build_words_graph.build_graph("texts.txt", "../OK_results/user" + user_id + "/words_degrees.txt")
training.train_small("texts.txt")
extract_text_topics.extract_text_topics("texts.txt", K, user_id, model)
cnt += 1
print("For %d users interests profile was built", cnt)
line_g = g.readline()
continue
train_dislikes = dislike_posts[0:int(cnt_dislikes * 0.8)]
test_dislikes = dislike_posts[int(cnt_dislikes * 0.8):]
for dislike in train_dislikes:
dislike_text_posts.write(dislike)
dislike_text_posts.close()
if not os.path.isdir('../OK_recommend/user' + user_id):
mkdir('../OK_recommend/user' + user_id)
build_words_graph.build_graph(
'like_texts.txt',
'../OK_recommend/user' + user_id + '/words_degrees_like.txt')
training.train_small('like_texts.txt', 'like')
build_words_graph.build_graph(
'dislike_texts.txt',
'../OK_recommend/user' + user_id + '/words_degrees_dislike.txt')
training.train_small('dislike_texts.txt', 'dislike')
types = ['like', 'dislike']
W_like = [0] * K
W_dislike = [0] * K
score_like = dict()
score_dislike = dict()
topic_of_words_like = dict()
topic_of_words_dislike = dict()