# for index1,coauthor1 in enumerate(authors):
# if index1==len(authors)-1: break
# for index2,coauthor2 in enumerate(authors):
# if index2<=index1:continue
## num=get_coauthor_num(coauthor1,coauthor2,author_name)
# num = get_coauthor_num2(coauthor1,coauthor2)-1
# if num>=1:
# graph.add_edge(pids[index1],pids[index2])
conn_comp = list(nx.connected_components(graph))
conn_comp = [list(c) for c in conn_comp]
return conn_comp
if __name__ == '__main__':
valid_data, tag = get_train_data()
# valid_data = get_valid_data()
f1_dict = {}
# 分析某个同名作者的文章信息
author_list_all = list(tag.keys())
author_list = author_list_all[1:20]
for author_select in author_list:
p_list = valid_data[author_select]
pids, titles, keywords, abstract, authors, venue, year = get_paper_detail(
p_list)
real_result = {author_select: tag[author_select]}
# model_result={author_select:rand_cluster(p_list)}
model_result = {
author_select: co_author_cluster(pids, authors, author_select)
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 14 09:11:35 2019
@author: chizj
"""
from common import get_train_data
if __name__ == '__main__':
train_data, train_tag = get_train_data(['li_guo'])
"""
Created on Wed Nov 13 11:24:47 2019
@author: chizj
"""
from common import get_train_data
my_stopwords = "a : “ ” , v the its and as on 's ( ) : % . based".split(' ')
if __name__ == '__main__':
author_list = [
'li_guo', 'bo_shen', 'di_wang', 'long_wang', 'qiang_xu', 'xiang_wang',
'changming_liu', 'kenji_kaneko', 'guohua_chen', 'hai_jin', 'jia_li',
'guoliang_li', 'lan_wang', 'alessandro_giuliani', 'jiang_he',
'xiang_gao', 'jianping_wu', 'peng_shi', 'feng_wu', 'jing_zhu'
]
train_data, train_tag = get_train_data(author_list)
# au='li_guo'
from base import PaperGraph
pg_dict = {}
for au in author_list:
pg = PaperGraph(au, train_data[au], train_tag[au])
g1 = pg.get_graph_by_coauthor(pg.origin_graph)
pg.get_score(g1)
pg_dict[au] = (pg, g1)
for au, v in pg_dict.items():
v[0].get_score(v[1])
from common import get_train_data
import nltk
from gensim.utils import tokenize
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import pairwise_distances
#nltk.download('stopwords')
#nltk.download('punkt')
my_stopwords = [
'a', 'one', 'two', 'three', 'four', 'six', 'first', 'second', 'third', 'i',
'h', 'l', 'c', 'via', 'iv'
]
train_data, train_tag = get_train_data()
# 做所有文章title的语料
paper_t_words = {}
for author, paper_list in train_data.items():
print(author, len(paper_list))
titles = {}
for paper in paper_list:
title = paper['title']
words = tokenize(title)
words = [w.lower() for w in words]
words = [
w for w in words if w not in nltk.corpus.stopwords.words('english')
]
words = [w for w in words if w not in my_stopwords]
titles[paper['id']] = words