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similar_user_index.py
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similar_user_index.py
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# coding:utf-8
import csv
import jieba
import math
import pymysql
import threading
import time
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
class Similar():
def __init__(self, book_id, weight):
self.csv_path = "miaoshu_fenci_chunjing.csv"
self.csv_to_path = "testdata.csv"
self.hz = dict()
self.read = self.read_file()
self.new_abstract_dict = dict()
self.data_dict = dict()
self.book_id = book_id
self.book_class = self.book_ab()[1]
self.data_dict_two = dict()
self.new_dict = {v: k for k, v in self.data_read_to_dict().items()}
self.weight = weight
def SqlCx(self, sql):
connect = pymysql.connect(host='rm-m5e42302277dox30fno.mysql.rds.aliyuncs.com',
port=3306, user='book',
password='Angel_Book', db="book",
cursorclass=pymysql.cursors.DictCursor)
cursor = connect.cursor()
cursor.execute(sql)
data = cursor.fetchall()
# connect.close()
connect.commit()
return data
def book_ab(self):
sql = "select id, book_abstract, book_class from book where id='%s'" % self.book_id
data = self.SqlCx(sql)
for i in data:
book_abs = str([x for x in jieba.cut(str(i['book_abstract']).replace('【', '').replace('】', '') \
.replace(' ', '').replace('，', '') \
.replace('。', '').replace('、', '').replace('...', '').replace('？', '') \
.replace('！', '').replace('&', '').replace('…', '').replace('”',
'') \
.replace('“', '').replace('《', '').replace('》', '').replace('#', '') \
.replace('&', '').replace('；', '').replace('amp', '').replace('�', '') \
.replace('◆', '').replace('-', '').replace('―', '').replace('・', '') \
.replace('：', '').replace('［', '').replace('］', '').replace('）', '') \
.replace('（', '').replace('‘', '').replace('’', ''))]).replace('[',
'').replace(
']', '').replace("'", '') \
.replace(' ', '').replace(',', ' ')
book_class = i['book_class']
return book_abs, book_class
def read_file(self):
csvfile = open(self.csv_path, encoding='UTF-8')
reader = csv.DictReader(csvfile)
id = [row['id'] for row in reader]
csvfile = open(self.csv_path, encoding='UTF-8')
reader = csv.DictReader(csvfile)
abstract = [row['abstract'] for row in reader]
for i in range(len(abstract)):
self.hz[id[i]] = abstract[i]
return self.hz
def data_read_to_dict(self):
# 需要把数据库里的数据转换为字典形式
sql = "select id, book_abstract from book where book_class='" + str(
self.book_class) + "' and id!='%s'" % self.book_id
data = self.SqlCx(sql)
for i in data:
self.data_dict[i['id']] = str(
[x for x in jieba.cut(str(i['book_abstract']).replace('【', '').replace('】', '') \
.replace(' ', '').replace('，', '') \
.replace('。', '').replace('、', '').replace('...', '').replace('？', '') \
.replace('！', '').replace('&', '').replace('…', '').replace('”', '') \
.replace('“', '').replace('《', '').replace('》', '').replace('#', '') \
.replace('&', '').replace('；', '').replace('amp', '').replace('�', '') \
.replace('◆', '').replace('-', '').replace('―', '').replace('・', '') \
.replace('：', '').replace('［', '').replace('］', '').replace('）', '') \
.replace('（', '').replace('‘', '').replace('’', ''))]).replace('[', '').replace(
']', '').replace("'", '') \
.replace(' ', '').replace(',', ' ')
# self.data_dict_two[str([x for x in jieba.cut(str(i['book_abstract']).replace('【', '').replace('】', '')\
# .replace(' ', '').replace('，', '') \
# .replace('。', '').replace('、', '').replace('...', '').replace('？', '') \
# .replace('！', '').replace('&', '').replace('…', '').replace('”', '') \
# .replace('“', '').replace('《', '').replace('》', '').replace('#', '') \
# .replace('&', '').replace('；', '').replace('amp', '').replace('�', '') \
# .replace('◆', '').replace('-', '').replace('―', '').replace('・', '') \
# .replace('：', '').replace('［', '').replace('］', '').replace('）', '') \
# .replace('（', '').replace('‘', '').replace('’', ''))]).replace('[', '').replace(']', '').replace("'", '')\
# .replace(' ', '').replace(',', ' ')] = i['id']
# print(self.data_dict)
# 然后进行清洗和分词处理
# 最后传递到函数里面
return self.data_dict
# @jit(nopython=True)
def tf_idf(self, corpus):
vectorizer = CountVectorizer(analyzer='word', token_pattern="(?u)\\b\\w+\\b")
transformer = TfidfTransformer()
tf_idf = transformer.fit_transform(vectorizer.fit_transform(corpus))
tf_idf_finish = tf_idf.toarray()
return tf_idf_finish
def similarity(self, data_1, data_2):
# print(type(data_1))
return sum(a * b for a, b in zip(data_1, data_2)) / (math.sqrt(sum(a * b for a, b in zip(data_1, data_2)))
* math.sqrt(
sum(a * b for a, b in zip(data_1, data_2))) + .00000000001)
# @jit(nopython=False)
def recomend(self, word_1, abstract_len, word_2):
self.sim_dict = dict()
print('3')
for i in range(abstract_len):
corpus = [word_1, word_2[i]]
# print(corpus)
print('4')
tf_finish = self.tf_idf(corpus)
print('5')
sim_sim = self.similarity(tf_finish[0], tf_finish[1])
print('6')
# print(sim_sim)
self.sim_dict[sim_sim] = word_2[i]
print('7')
sort_sim = sorted(self.sim_dict.keys(), reverse=True)
print('8')
# data_id_1 = list(self.data_read_to_dict().keys())[list(self.data_read_to_dict().values()).index(word_1)]
sim_value = [self.sim_dict[x] for x in sort_sim[0:self.weight]]
# 所有的工作落到了这一行，怎么反求出来id，30秒就执行一句话，我笑了
# data_id_2 = [list(self.data_read_to_dict().keys())[list(self.data_read_to_dict().values()).index(xx)] for xx in sim_value]
# for i in sim_value:
# new_dict_va = new_dict[i]
# print(new_dict_va)
print('9')
data_id_2 = [self.new_dict[i] for i in sim_value]
# sql = "select * from book where id='%s' or id='%s' or id='%s' or id='%s' or id='%s'" \
# % (data_id_2[4], data_id_2[3], data_id_2[2], data_id_2[1], data_id_2[0])
# data = self.SqlCx(sql)
# print(data)
return data_id_2
def run(self):
print("1")
run_time_1 = time.time()
# abstract_list_1 = [ms for ms in self.data_read_to_dict().values()]
# print(abstract_list_1)
abstract_list_2 = [ms for ms in self.data_read_to_dict().values()]
print('2')
t1 = time.time()
data = self.recomend(self.book_ab()[0], len(abstract_list_2), abstract_list_2)
# t2 = time.time()
# print("单个用时：", t2-t1)
run_time_2 = time.time()
print("总用时：", run_time_2 - run_time_1)
return data
class MyThread(threading.Thread):
def __init__(self, func, args=()):
super(MyThread, self).__init__()
self.func = func
self.args = args
def run(self):
self.result = self.func(*self.args)
def get_result(self):
try:
return self.result # 如果子线程不使用join方法，此处可能会报没有self.result的错误
except Exception:
return ''
def sim(book_id, weight):
xx = Similar(book_id, weight)
xx = xx.run()
return xx
if __name__ == '__main__':
# book_id = 1
x = Similar(20520, 2).run()
print(x)
# x = Similar(100, 1).run()
# print(x)
# x = Similar(100, 1).run()
# print(x)
# x = Similar(5, 1).run()
# print(x)
# thread_li = []
# book_id_list = []
# t1 = MyThread(sim, args=(1, 2,))
# thread_li.append(t1)
# t1.start()
# t2 = MyThread(sim, args=(10000, 1,))
# thread_li.append(t2)
# t2.start()
# t3 = MyThread(sim, args=(10500, 1,))
# thread_li.append(t3)
# t3.start()
# t4 = MyThread(sim, args=(12000, 1,))
# thread_li.append(t4)
# t4.start()
# for t in thread_li:
# t.join()
# book_id_list.append(t.get_result())
# print(book_id_list)