sim/sim/text_sim.py

# -*- coding: utf-8 -*-

from operator import itemgetter

import jieba
import numpy as np
from words_sim import SimCilin

ci_lin = SimCilin()
jieba.load_userdict('cilin_words.txt')


def segmentation(sentence_list):
    result = list()
    for s in sentence_list:
        temp_seg = jieba.cut(s)
        result.append([x for x in temp_seg])

    return result


def get_similarity(s1, s2):
    all_sim_1 = list()
    for w1 in s1:
        if is_contains_chinese(w1):
            sim_list = list()
            for w2 in s2:
                sim_list.append(ci_lin.compute_word_sim(w1, w2))
            sim_list.sort()
            all_sim_1.append(sim_list[-1])

    all_sim_2 = list()
    for w1 in s2:
        if is_contains_chinese(w1):
            sim_list = list()
            for w2 in s1:
                sim_list.append(ci_lin.compute_word_sim(w1, w2))
            sim_list.sort()
            all_sim_2.append(sim_list[-1])

    return (np.mean(all_sim_1) + np.mean(all_sim_2)) / 2


def most_similar_items(src_s, sentences, n=3):
    sentences = segmentation(sentences)
    temp = list()
    for item in sentences:
        sim_value = get_similarity(src_s, item)
        temp.append({
            'key': merge(item),
            'value': sim_value,
        })
    result = sorted(temp, key=itemgetter('value'), reverse=True)
    return result[:n]


def is_contains_chinese(s):
    for _char in s:
        if '\u4e00' <= _char <= '\u9fa5':
            return True
    return False


def merge(word_list):
    s = ''
    for w in word_list:
        s += w.split('/')[0]
    return s


if __name__ == '__main__':
    str1 = '我喜欢吃苹果'
    str2 = '他喜欢肯红薯'
    str1_seg = jieba.cut(str1)
    str2_seg = jieba.cut(str2)
    str1_new = [x for x in str1_seg]
    str2_new = [x for x in str2_seg]
    str_l = ['我喜欢吃梨', '你喜欢吃苹果', '他喜欢吃橙子']
    print(get_similarity(str1_new, str2_new))
    print(most_similar_items(str1, str_l, 5))