Example #1
0
        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)
Example #2
0
File: main.py Project: kivi239/ML
        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)
Example #3
0
File: main.py Project: kivi239/ML
        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)
Example #4
0
        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()