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96 lines
3.6 KiB
96 lines
3.6 KiB
# -*- coding = utf-8 -*-
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# @Time: 18:02
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# @Author:ZYP
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# @File：util.py
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# @mail：zypsunshine1@gmail.com
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# @Software: PyCharm
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# =========================================================================================
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# 工具类
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# 用于加载停用词、数据库、word2vec、fasttext模型
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# =========================================================================================
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import os
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import time
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import math
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import json
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import jieba
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import numpy as np
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import requests
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from collections import defaultdict
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from textrank4zh import TextRank4Keyword
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jieba.initialize()
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stop_word_path = '/home/zc-nlp-zyp/work_file/ssd_data/program/check_paper/fasttext_train/data/total_stopwords.txt'
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class Logging:
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def __init__(self):
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pass
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def log(*args, **kwargs):
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format = '%Y/%m/%d-%H:%M:%S'
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format_h = '%Y-%m-%d'
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value = time.localtime(int(time.time()))
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dt = time.strftime(format, value)
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dt_log_file = time.strftime(format_h, value)
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log_file = 'gunicornLogs/access-%s-%s' % (str(os.getpid()), dt_log_file) + ".log"
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if not os.path.exists(log_file):
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with open(os.path.join(log_file), 'w', encoding='utf-8') as f:
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print(dt, *args, file=f, **kwargs)
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else:
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with open(os.path.join(log_file), 'a+', encoding='utf-8') as f:
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print(dt, *args, file=f, **kwargs)
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def load_stopwords(path=stop_word_path):
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"""加载停用词"""
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with open(path, 'r', encoding='utf-8') as f:
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stop_words = {i.strip() for i in f.readlines()}
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return stop_words
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def cut_text(text_str, tokenizer='jieba'):
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"""使用相应的分词算法对文章进行分词，然后统计每个单词的词频，按照降序返回相应的字典"""
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word_dict = defaultdict(int)
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if tokenizer == 'jieba':
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all_word_list = jieba.cut(text_str)
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for word in all_word_list:
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if word not in stop_word:
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word_dict[word] += 1
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# elif tokenizer == 'hanlp':
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# for i in HanLP.segment(text_str):
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# if i.word not in stop_word and i.nature != 'w':
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# word_dict[i.word] += 1
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else:
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print('您输入的 tokenizer 参数有误！')
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return {k: v for k, v in sorted(word_dict.items(), key=lambda x: x[1], reverse=True)}
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def l2_normal(tf_idf_dict):
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"""对计算出来的tf-idf字典进行归一化，归一到（0-1）之间"""
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l2_norm = math.sqrt(sum(map(lambda x: x ** 2, tf_idf_dict.values())))
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tf_idf_dict1 = sorted(tf_idf_dict.items(), key=lambda x: x[1] / l2_norm, reverse=True)
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tf_idf_dict2 = {key: value / l2_norm for key, value in tf_idf_dict1[:15]}
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return tf_idf_dict2
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def get_word_vec(word):
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"""根据相应的词语，使用模型进行提取词语向量，如果不存在词表中返回0，存在词表中返回对应向量"""
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vec = requests.post('http://192.168.31.74:50001/word2vec', data=json.dumps({'word': word}), timeout=100)
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if len(vec.text) < 100:
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vec = requests.post('http://192.168.31.74:50002/fasttext', data=json.dumps({'word': word}), timeout=100)
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if len(vec.text) < 100:
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vec = 0
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return vec
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else:
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json_dict = json.loads(vec.text)
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res_vec = np.array([float(j) for j in json_dict["vec"].split(',')], dtype=np.float64)
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return res_vec
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else:
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json_dict = json.loads(vec.text)
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res_vec = np.array([float(j) for j in json_dict["vec"].split(',')], dtype=np.float64)
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return res_vec
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stop_word = load_stopwords()
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tr4w = TextRank4Keyword(stop_words_file=stop_word_path)
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