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recommendation_module.py
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recommendation_module.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Dec 23 14:06:10 2015
@author: bitjoy.net
"""
from os import listdir
import xml.etree.ElementTree as ET
import jieba
import jieba.analyse
import sqlite3
import configparser
from datetime import *
import math
import pandas as pd
import numpy as np
from sklearn.metrics import pairwise_distances
class RecommendationModule:
stop_words = set()
k_nearest = []
config_path = ''
config_encoding = ''
doc_dir_path = ''
doc_encoding = ''
stop_words_path = ''
stop_words_encoding = ''
idf_path = ''
db_path = ''
def __init__(self, config_path, config_encoding):
self.config_path = config_path
self.config_encoding = config_encoding
config = configparser.ConfigParser()
config.read(config_path, config_encoding)
self.doc_dir_path = config['DEFAULT']['doc_dir_path']
self.doc_encoding = config['DEFAULT']['doc_encoding']
self.stop_words_path = config['DEFAULT']['stop_words_path']
self.stop_words_encoding = config['DEFAULT']['stop_words_encoding']
self.idf_path = config['DEFAULT']['idf_path']
self.db_path = config['DEFAULT']['db_path']
f = open(self.stop_words_path, encoding = self.stop_words_encoding)
words = f.read()
self.stop_words = set(words.split('\n'))
def write_k_nearest_matrix_to_db(self):
conn = sqlite3.connect(self.db_path)
c = conn.cursor()
c.execute('''DROP TABLE IF EXISTS knearest''')
c.execute('''CREATE TABLE knearest
(id INTEGER PRIMARY KEY, first INTEGER, second INTEGER,
third INTEGER, fourth INTEGER, fifth INTEGER)''')
for docid, doclist in self.k_nearest:
c.execute("INSERT INTO knearest VALUES (?, ?, ?, ?, ?, ?)", tuple([docid] + doclist))
conn.commit()
conn.close()
def is_number(self, s):
try:
float(s)
return True
except ValueError:
return False
def construct_dt_matrix(self, files, topK = 200):
jieba.analyse.set_stop_words(self.stop_words_path)
jieba.analyse.set_idf_path(self.idf_path)
M = len(files)
N = 1
terms = {}
dt = []
for i in files:
root = ET.parse(self.doc_dir_path + i).getroot()
title = root.find('title').text
body = root.find('body').text
docid = int(root.find('id').text)
tags = jieba.analyse.extract_tags(title + '。' + body, topK=topK, withWeight=True)
#tags = jieba.analyse.extract_tags(title, topK=topK, withWeight=True)
cleaned_dict = {}
for word, tfidf in tags:
word = word.strip().lower()
if word == '' or self.is_number(word):
continue
cleaned_dict[word] = tfidf
if word not in terms:
terms[word] = N
N += 1
dt.append([docid, cleaned_dict])
dt_matrix = [[0 for i in range(N)] for j in range(M)]
i =0
for docid, t_tfidf in dt:
dt_matrix[i][0] = docid
for term, tfidf in t_tfidf.items():
dt_matrix[i][terms[term]] = tfidf
i += 1
dt_matrix = pd.DataFrame(dt_matrix)
dt_matrix.index = dt_matrix[0]
print('dt_matrix shape:(%d %d)'%(dt_matrix.shape))
return dt_matrix
def construct_k_nearest_matrix(self, dt_matrix, k):
tmp = np.array(1 - pairwise_distances(dt_matrix[dt_matrix.columns[1:]], metric = "cosine"))
similarity_matrix = pd.DataFrame(tmp, index = dt_matrix.index.tolist(), columns = dt_matrix.index.tolist())
for i in similarity_matrix.index:
tmp = [int(i),[]]
j = 0
while j < k:
max_col = similarity_matrix.loc[i].idxmax(axis = 1)
similarity_matrix.loc[i][max_col] = -1
if max_col != i:
tmp[1].append(int(max_col)) #max column name
j += 1
self.k_nearest.append(tmp)
def gen_idf_file(self):
files = listdir(self.doc_dir_path)
n = float(len(files))
idf = {}
for i in files:
root = ET.parse(self.doc_dir_path + i).getroot()
title = root.find('title').text
body = root.find('body').text
seg_list = jieba.lcut(title + '。' + body, cut_all=False)
seg_list = set(seg_list) - self.stop_words
for word in seg_list:
word = word.strip().lower()
if word == '' or self.is_number(word):
continue
if word not in idf:
idf[word] = 1
else:
idf[word] = idf[word] + 1
idf_file = open(self.idf_path, 'w', encoding = 'utf-8')
for word, df in idf.items():
idf_file.write('%s %.9f\n'%(word, math.log(n / df)))
idf_file.close()
def find_k_nearest(self, k, topK):
self.gen_idf_file()
files = listdir(self.doc_dir_path)
dt_matrix = self.construct_dt_matrix(files, topK)
self.construct_k_nearest_matrix(dt_matrix, k)
self.write_k_nearest_matrix_to_db()
if __name__ == "__main__":
print('-----start time: %s-----'%(datetime.today()))
rm = RecommendationModule('../config.ini', 'utf-8')
rm.find_k_nearest(5, 25)
print('-----finish time: %s-----'%(datetime.today()))