def main():
# 设置结果保存的目录
result_dir = r'D:\semantic analysis\2016-10-05结果\html标记分句2//'
txt_dir = r"D:\semantic analysis\2016-10-05结果\新词分句//"
set_dir = r"D:\semantic analysis\2016-10-05结果\新词//"
k_list = util.get_key_list()
for key in k_list:
print(key)
# 文件目录
file_list = sorted(util.get_file_list(txt_dir + key, ".txt"))
# 集合目录
set_list = sorted(util.get_file_list(set_dir + key, ".pkl"))
util.create_directory(result_dir + "新词//" + key + "//")
i = 0
while i < len(file_list):
s_list = util.get_list_from_file(txt_dir + key + "//" +
set_list[i][0:-4] + ".txt")
new_word_list = util.get_nw(set_dir + key + "//" + set_list[i])
# 过滤相同的语句，防止重复计算
s_list = list(set(s_list))
w_list = remark(s_list, new_word_list, key)
html_name = file_list[i][:-4] + '.html'
util.save_file(result_dir + "新词//" + key + "//" + html_name,
w_list)
i += 1
def main1():
# date_list = ["2012-08-05","2011-04-05","2011-03-28","2011-10-20","2012-12-30","2011-07-30","2011-06-09","2012-02-05","2012-12-16","2011-08-01","2011-05-19","2013-09-01","2012-08-01","2013-12-01"]
# key_list = ["吐槽","纠结","淡定","自拍","正能量","山寨","达人","腹黑","接地气","扯淡","闷骚","不明觉厉","完爆","人艰不拆"]
date_list = [
"2013-12-31", "2013-12-31", "2013-12-31", "2013-12-31", "2013-12-31",
"2013-12-31", "2013-12-31", "2013-12-31", "2013-12-31", "2013-12-31",
"2013-12-31"
]
key_list = [
'努力',
'感觉',
'简单',
'无聊',
'希望',
'美好',
'气质',
'害怕',
'喜欢',
'不约而同',
'喜闻乐见',
]
# 设置结果保存的目录
result_dir = r'D:\semantic analysis\2016-10-09结果\html标记结果//'
txt_dir = r"D:\semantic analysis\纯文本\常用词分句//"
set_dir = r"D:\semantic analysis\2016-10-09结果\中间结果//"
i = 0
while i < len(key_list):
key = key_list[i]
print(key)
# 文件目录
file_list = sorted(util.get_file_list(txt_dir + key, ".txt"))
# 集合目录
set_dir_list = util.get_file_list(set_dir + key, ".pkl")
set_list = []
for set_list_dir in set_dir_list:
set_list.append(util.get_nw(set_dir + key + "//" + set_list_dir))
print(set_list_dir)
util.create_directory(result_dir + key + "//")
rr = cal_index2(date_list[i], txt_dir + key_list[i])
j = 0
# 每个分段
while j < len(rr):
k = 0
while k < rr[j]:
print(file_list[k][:-4])
print(rr[j])
txt_list = util.get_list_from_file(txt_dir + key + "//" +
file_list[k])
w_list = remark(txt_list, set_list[j], key)
html_name = file_list[k][:-4] + '.html'
util.save_file(result_dir + key + "//" + html_name, w_list)
k += 1
j += 1
i += 1
def cal_union_set(keyword, pkl_dir):
set_list = sorted(util.get_file_list(pkl_dir, "pkl"))
r_list = []
d_list = []
i = 0
r_set0 = set()
while i < len(set_list):
r_set0 = r_set0 | util.get_nw(pkl_dir + set_list[i])
r_list.append(set_list[i][0:-4] + '\t' + str(len(r_set0)))
d_list.append(len(r_set0))
i += 1
i = 0
r_list2 = []
d_list2 = []
while i < len(r_list)-1:
r_list2.append(set_list[i][0:-4] + '\t' + str(d_list[i+1]-d_list[i]))
d_list2.append(d_list[i+1]-d_list[i])
i += 1
i = 15
r_list3 = []
while i < len(d_list2):
r_list3.append(set_list[i][0:-4] + '\t' + str(d_list2[i]/d_list[i]))
i += 1
return r_list, r_list2,r_list3
def cal_mcs(div, num, pkl_dir):
os.chdir(pkl_dir)
pkl_list = util.get_file_list(pkl_dir, '.pkl')
# 从小到大排序
pkl_list = sorted(pkl_list)
# part_num = len(pkl_list) // div
part_num = 200
print("part_nume")
print(part_num)
if part_num < num:
print("参数有误")
return
i = 0
set_list = []
# 块内的循环
while i < div:
# 进行合并操作
j = 0
r_set = set()
while j < num:
pkl_num = part_num * i + j
set1 = util.get_nw(pkl_list[pkl_num])
r_set = set1 | r_set
j += 1
# 把合并的结果加入列表中
set_list.append(r_set)
print(len(r_set))
i += 1
def loop_compare(keyword_list, pkl_dir1, txt_dir1, result_dir, mode=1, lap=1):
for key in keyword_list:
print(key)
if mode == 0:
util.create_directory(result_dir + key + "//")
pkl_dir = pkl_dir1.format(key)
txt_dir = txt_dir1.format(key)
# 获取日期列表
d_list = util.get_file_list(pkl_dir, '.pkl')
d_list = [d.split(".")[0] for d in d_list]
result_list = []
# 升序排序
d_list = sorted(d_list)
ii = len(d_list) - 1
while ii - lap >= 0:
g1 = get_core_graph(pkl_dir + d_list[ii] + ".pkl")
d1 = get_txt_dict(txt_dir + d_list[ii] + ".txt")
# 迭代生成子图
k = 1
while k < lap:
g1 = nx.compose(g1, util.get_nw(d_list[ii - k]))
k += 1
result_list.append(compare_function(d1, g1))
ii -= lap
util.save_file(result_dir + key + ".txt", result_list)
def cal_index2(date, pkl_dir):
r_list = []
f_list = util.get_file_list(pkl_dir, '.txt')
import os
os.chdir(pkl_dir)
# 升序排序
set_list = sorted(f_list)
date2 = set_list[0][0:-4]
i = 0
date1 = date
# 查找出分割点所在的序号
while i < len(set_list) and util.compare_date(date1, date2) > 0.0:
date2 = set_list[i][0:-4]
i += 1
r_list.append(i)
j = 0
part = 2
part_num = (len(f_list) - i) // part
while j < part - 1:
j += 1
r_list.append(i + j * part_num)
r_list.append(len(f_list))
return r_list
def cal_mcs(pkl_dir, mcs_dir, is_front, key_word, lap=1):
f_list = util.get_file_list(pkl_dir, '.pkl')
os.chdir(pkl_dir)
# 升序排序
nw_list = sorted(f_list)
record_list = []
num_list = []
enum_list = []
ii = len(nw_list) - 1
# g2是2号 g1是1号,此处获取最末端的网络
g2 = util.get_nw(nw_list[ii])
# 迭代生成子图
k = 1
while k < lap:
g2 = mcs(g2, util.get_nw(nw_list[ii - k]))
k += 1
while ii > 0:
jj = ii
ii -= lap
# print(nw_list[ii])
g1 = util.get_nw(nw_list[ii])
# 迭代生成子图
k = 1
while k < lap:
g1 = mcs(g1, util.get_nw(nw_list[ii - k]))
k += 1
# 生成连通子图
g1 = mcs(g2, g1)
# 生成文件名字
filename = nw_list[ii][0:-4] + '-' + nw_list[jj][0:-4] + '.pkl'
# 2016.9.20 用于测试，保存结果
util.save_nw(
g1, 'D://semantic analysis//nalyze_data//result//过程结果//连通子图//' +
filename)
if is_front:
# 计算比例，1，2 跟1比
pr = mcs_ratio(g1, g1, key_word)
record_list.append(nw_list[jj][0:-4] + '\t' + str(pr))
else:
# 计算比例，1，2 跟2比
pr = mcs_ratio(g1, g2, key_word)
record_list.append(nw_list[jj][0:-4] + '\t' + str(pr))
num_list.append(nw_list[jj][0:-4] + '\t' + str(g1.number_of_nodes()))
enum_list.append(nw_list[jj][0:-4] + '\t' + str(g1.number_of_edges()))
# 统计节点数
# with open(mcs_dir + filename[0:-4]+'.txt','w',encoding='utf-8') as file:
# for node in g1.nodes():
# file.write(node+'\n')
# util.save_nw(g1,mcs_dir + filename)
g2 = g1
def create(txt_dirs, xls_name):
data_dict = {}
for txt_dir in txt_dirs:
os.chdir(txt_dir)
file_list = util.get_file_list(txt_dir, 'txt')
for file in file_list:
key = file[0:-4]
data_list = util.get_list_from_file(file)
x_list = []
y_list = []
for data in data_list:
item = data.split('\t')
x_list.append(datetime.datetime.strptime(item[0], '%Y-%m-%d'))
y_list.append(float(item[1]))
if key in data_dict.keys():
data_dict[key] = data_dict[key] + [x_list]
data_dict[key] = data_dict[key] + [y_list]
else:
data_dict[key] = [x_list, y_list]
bl_list = []
ii = Num - 1
while ii < Num:
ii += 1
bl_list.append(str(ii))
# util.create_xlsx(data_dict, bl_list, txt_dirs[0] + xls_name + '.xlsx')
util.create_xlsx(data_dict, bl_list, xls_name)
def loop_compare(com_function,
keyword_list,
pkl_dir1,
result_dir,
mode=1,
lap=1,
type="pkl"):
for key in keyword_list:
global keyword
keyword = key
print(key)
if mode == 0:
util.create_directory(result_dir + key + "//")
pkl_dir = pkl_dir1.format(key)
f_list = util.get_file_list(pkl_dir, '.pkl')
os.chdir(pkl_dir)
result_list = []
# 升序排序
nw_list = sorted(f_list)
ii = len(nw_list) - 1
while ii - 2 * lap >= 0:
g2 = util.get_nw(nw_list[ii])
# 迭代生成子图
# k = 1
# while k < lap:
# g2 = nx.compose(g2, util.get_nw(nw_list[ii - k]))
# k += 1
ii -= lap
g1 = util.get_nw(nw_list[ii])
# 迭代生成子图
# k = 1
# while k < lap:
# g1 = nx.compose(g1, util.get_nw(nw_list[ii - k]))
# k += 1
# 生成连通子图
# 相互比例
if mode == 1:
r1, r2 = com_function(copy.deepcopy(g1), copy.deepcopy(g2))
result_list.append(nw_list[ii + lap][0:-4] + "\t" + str(r1))
result_list.append((nw_list[ii][0:-4] + "\t" + str(r2)))
# 一对一
elif mode == 0:
result_list = com_function(copy.deepcopy(g1),
copy.deepcopy(g2))
util.save_file(
result_dir + key + "//" + nw_list[ii + lap][0:-4] + ".txt",
result_list)
# n对一
elif mode == 2:
r1 = com_function(copy.deepcopy(g1), copy.deepcopy(g2), type)
result_list.append(nw_list[ii + lap][0:-4] + "\t" + str(r1))
ii -= lap
if mode != 0:
result_list.reverse()
util.save_file(result_dir + key + ".txt", result_list)
def count_num_of_node(pkl_dir):
pkl_file_list = util.get_file_list(pkl_dir, '.pkl')
r_list = []
for file in pkl_file_list:
g = util.get_nw(file)
s = file[0:10] + '\t' + str(g.number_of_nodes())
r_list.append(s)
return r_list
def main2():
fir = "D:\semantic analysis\整理文本\正能量//"
xml_list = util.get_file_list(fir, "txt")
for xml in xml_list:
w_list = set(util.get_list_from_file(fir + xml))
r_list = remark(w_list, (['正能量']), None)
html_name = xml[:-4] + '.html'
util.save_file(fir + html_name, r_list)
def loop_compare(com_function,
keyword_list,
pkl_dir1,
result_dir,
mode=1,
lap=1,
type="pkl"):
for key in keyword_list:
print(key)
if mode == 0:
util.create_directory(result_dir + key + "//")
pkl_dir = pkl_dir1.format(key)
f_list = util.get_file_list(pkl_dir, '.txt')
os.chdir(pkl_dir)
result_list = []
# 升序排序
nw_list = sorted(f_list)
ii = len(nw_list) - 1
while ii - 2 * lap >= 0:
g2 = util.txt2dict(util.get_list_from_file(nw_list[ii]))
# 迭代生成子图
k = 1
while k < lap:
g2 = nx.compose(g2, util.get_nw(nw_list[ii - k]))
k += 1
ii -= lap
g1 = util.txt2dict(util.get_list_from_file(nw_list[ii]))
d1 = util.get_nw(
"D:\semantic analysis\新结果\去虚词去单字共现网络//{0}//p//".format(key) +
nw_list[ii].split(".")[0] + ".pkl")
# 迭代生成子图
k = 1
while k < lap:
g1 = nx.compose(g1, util.get_nw(nw_list[ii - k]))
k += 1
# 生成连通子图
if mode == 1:
r1, r2 = com_function(copy.deepcopy(g1), copy.deepcopy(g2))
result_list.append(nw_list[ii + lap][0:-4] + "\t" + str(r1))
result_list.append((nw_list[ii][0:-4] + "\t" + str(r2)))
elif mode == 0:
result_list = com_function(copy.deepcopy(g1),
copy.deepcopy(g2))
util.save_file(
result_dir + key + "//" + nw_list[ii + lap][0:-4] + ".txt",
result_list)
elif mode == 2:
r1 = com_function(copy.deepcopy(g1), copy.deepcopy(g2), d1)
# result_list.append(str(r1))
result_list.append(nw_list[ii + lap][0:-4] + "\t" + str(r1))
ii -= lap
if mode != 0:
result_list.reverse()
util.save_file(result_dir + key + ".txt", result_list)
def main():
# 设置结果保存的目录
result_dir = r'D:\semantic analysis\新结果\共现网络//'
txt_dir = r"D:\semantic analysis\新纯文本\1常用词//"
# k_list = util.get_key_list()
# k_list = ['不约而同', '喜闻乐见', '努力', '感觉', '简单', '无聊', '希望', '美好']
# 中心词
k_list = ['美好']
# 结巴分词词典的目录
# jieba.set_dictionary("D:\semantic analysis\分词\词库\导出结果\dict1.txt")
# jieba.initialize()
pynlpir.open()
for key in k_list:
print(key)
pynlpir.nlpir.AddUserWord(c_char_p(key.encode()))
for key in k_list:
print(key)
# 文件目录
file_list = util.get_file_list(txt_dir + key, ".txt")
# 建立目录
mk_dir(result_dir + key)
# mk_dir(result_dir+key+'//w')
mk_dir(result_dir + key + '//p')
for n_file in file_list:
s_list = util.get_list_from_file(txt_dir + key + "//" + n_file)
# 过滤相同的语句，防止重复计算
print(len(s_list))
s_list = list(set(s_list))
print(len(s_list))
# 生成所有句子的网络
# ps_list, mn, pps_list,pmn = create_matrix(s_list,key)
pps_list, pmn = create_matrix(s_list, key)
pkl_name = n_file[:-4] + '.pkl'
# for w_list in ps_list:
# # 创建整句话的网络
# mn.add_edges(w_list)
# util.save_nw(mn.get_network(), result_dir+key+'//w//' + pkl_name)
for w_list in pps_list:
# pmn.add_edges(w_list)
pmn.add_gram_edges(w_list)
util.save_nw(pmn.get_network(),
result_dir + key + '//p//' + pkl_name)
print(n_file)
print(
time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())))
with open(result_dir + key + '//record.txt', 'a',
encoding='utf-8') as rf:
rf.write(n_file + '\n')
pynlpir.close()
def individual_word(fre_path):
plots_list = []
file_list = util.get_file_list(fre_path, ".txt")
for file in file_list:
rl = util.get_list_from_dicttxt(fre_path + file)
max_num = len(rl)
x = list(range(max_num))
y = rl
plots_list.append((x, y))
return file_list, plots_list
def cal_mcs(pkl_dir):
os.chdir(pkl_dir)
pkl_list = util.get_file_list(pkl_dir, '.pkl')
# 从小到大排序
pkl_list = sorted(pkl_list)
m_set = util.get_nw(r"D:\semantic analysis\2016-10-03结果\达人//上升集合1.pkl")
for file in pkl_list:
set1 = util.get_nw(file)
r_set = m_set & set1
print(len(r_set) / len(m_set))
def main3():
# keyword_list = ['扯淡', '腹黑', '接地气', '闷骚', '完爆', '正能量', '达人']
keyword_list = ['淡定', '纠结', '山寨', '吐槽','自拍']
for key in keyword_list:
file_list = util.get_file_list(r"D:\semantic analysis\纯文本\新词//"+key,".txt")
date_list = []
for file_name in file_list:
date_list.append(file_name[:-4])
urlList,dateList = create_inital_address3(key,date_list)
with open("url_list.txt","a") as f:
for url in urlList:
f.write(url+"\n")
def main1(keyword):
dirr = 'D:\semantic analysis\pNet1\\' + keyword + '//p//'
r_dir = 'D:\semantic analysis//3次采集结果\连续比例4//'
pkl_list = util.get_file_list(dirr, '.pkl')
pkl_list = sorted(pkl_list)
for pkl in pkl_list:
print(pkl)
ll = len(pkl_list) - 1
ii = ll
g = util.get_nw(dirr + '\\' + pkl_list[ii])
r_list = []
n_list = []
# 生成五个图的公共子图
while ii >= ll - 3:
ii -= 1
g2 = util.get_nw(dirr + '\\' + pkl_list[ii])
g = mcs(g2, g)
print(pkl_list[ii] + '\t' + str(g.number_of_nodes()))
ii = len(pkl_list) - 1
while ii > 0:
ii -= 1
g2 = util.get_nw(dirr + '\\' + pkl_list[ii])
rr, nn = mcs_ratio_advanced(g2, g, keyword)
r_list.append(pkl_list[ii][0:-4] + '\t' + str(rr))
n_list.append(pkl_list[ii][0:-4] + '\t' + str(nn))
util.save_file(r_dir + keyword + '.txt', r_list)
util.save_file(r_dir + 'n' + keyword + '.txt', n_list)
# for key in key_list:
# print(key)
# main1(key)
# g2 = util.get_nw('D:\semantic analysis\c_date\给力\p//2011-03-31.pkl')
# g1 = util.get_nw('D:\semantic analysis\c_date\给力\p//2011-03-30.pkl')
# g1 = util.get_nw('D:\semantic analysis\c_date\给力\p//2011-03-29.pkl')
# g4 = util.get_nw('D:\semantic analysis\c_date\给力\p//2011-03-28.pkl')
#
#
# g5 = mcs(g2, g1)
# g6 = mcs(g1, g5)
# g7 = mcs(g4, g6)
# r = g1.number_of_nodes() / g2.number_of_nodes()
# print("的节点数："+str(g2.number_of_nodes()))
# print("的节点数："+str(g1.number_of_nodes()))
# print("两者公共子图的节点数："+str(g5.number_of_nodes()))
# print("两者公共子图的节点数："+str(g6.number_of_nodes()))
# print("两者公共子图的节点数："+str(g7.number_of_nodes()))
# print("比值1：" + str(g1.number_of_nodes() / g2.number_of_nodes()))
# print("比值2：" + str(g1.number_of_nodes() / g1.number_of_nodes()))
def individual_word_xy(fre_path):
plots_list = []
file_list = util.get_file_list(fre_path, ".txt")
# 删除首个
for file in file_list:
xy_list = util.get_list_from_file(fre_path + file)
del [xy_list[-1]]
x = []
y = []
for xy in xy_list:
x.append(xy.split("\t")[0])
y.append(xy.split("\t")[1])
plots_list.append((x, y))
return file_list, plots_list
def accumlate_ratio(fre_path):
file_name_list = []
plots_list = []
file_list = util.get_file_list(fre_path, ".txt")
file_name_list.extend(file_list)
for file in file_list:
print(file)
r = util.get_list_from_file(fre_path + file)
rl = []
for ii in r:
rl.append(float(ii))
# rl.append(float(ii.split("\t")[1]))
max_num = len(rl)
x = list(range(max_num))
y = rl
# print(sum(y[0:100]))
plots_list.append((x, y))
return file_name_list, plots_list
def cal_index(date_list, pkl_dir):
r_list = []
f_list = util.get_file_list(pkl_dir, '.pkl')
os.chdir(pkl_dir)
# 升序排序
set_list = sorted(f_list)
date2 = set_list[0][0:-4]
i = 0
for date in date_list:
date1 = date
# 查找出分割点所在的序号
while i < len(set_list) and util.compare_date(date1, date2) > 0.0:
date2 = set_list[i][0:-4]
i += 1
r_list.append(i)
r_list.append(len(f_list))
return r_list
def loop_compare(com_function,
keyword_list,
pkl_dir1,
result_dir,
mode=1,
lap=1,
type="pkl"):
for keyword in keyword_list:
pkl_dir = pkl_dir1.format(keyword)
f_list = util.get_file_list(pkl_dir, '.pkl')
os.chdir(pkl_dir)
# 升序排序
nw_list = sorted(f_list)
record_list = []
ii = len(nw_list) - 1
# g2是2号 g1是1号,此处获取最末端的网络
g2 = util.get_nw(nw_list[ii])
# 迭代生成子图
k = 1
while k < lap:
g2 = nx.compose(g2, util.get_nw(nw_list[ii - k]))
k += 1
while ii - lap >= 0:
jj = ii
ii -= lap
# print(nw_list[ii])
g1 = util.get_nw(nw_list[ii])
# 迭代生成子图
k = 1
while k < lap:
g1 = nx.compose(g1, util.get_nw(nw_list[ii - k]))
k += 1
# 计算比例
r1 = com_function(g1, g2)
record_list.append(nw_list[jj][0:-4] + '\t' + str(r1))
g2 = g1
record_list.reverse()
util.save_file(result_dir + keyword + ".txt", record_list)
def main():
k_list = util.get_key_list()
jieba.set_dictionary("D:\semantic analysis\分词\词库\导出结果\dict1.txt")
jieba.initialize()
for key in k_list:
print(key)
file_list = util.get_file_list(
'D://semantic analysis//analyze_data//fc//' + key, ".txt")
# 建立目录
mk_dir('./w')
mk_dir('./p')
for n_file in file_list:
s_list = util.get_list_from_file(n_file)
# 过滤相同的语句，防止重复计算
print(len(s_list))
s_list = list(set(s_list))
print(len(s_list))
wg = nx.Graph()
pg = nx.Graph()
for sentence in s_list:
# 创建整句话的网络
ll = util.input_filer(sentence)
wg = add_s2g(wg, ' '.join(ll))
# 只创建关键词所在的分句的网络
for ss in ll:
if (key in ss):
pg = add_s2g(pg, ss)
pkl_name = n_file[:-4] + '.pkl'
util.save_nw(pg, './/p//' + pkl_name)
util.save_nw(wg, './/w//' + pkl_name)
print(n_file)
print(
time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())))
with open('record.txt', 'a', encoding='utf-8') as rf:
rf.write(n_file + '\n')
def cal_node_mcs(pkl_dir, mcs_dir, key_word, lap=2):
f_list = util.get_file_list(pkl_dir, '.pkl')
os.chdir(pkl_dir)
# 升序排序
nw_list = sorted(f_list)
record_list = []
num_list = []
enum_list = []
ii = len(nw_list) - 1
while (ii - lap + 1) >= 0:
# print(nw_list[ii])
g1 = util.get_nw(nw_list[ii])
# 迭代生成子图
k = 1
while k < lap:
g1 = mcs(g1, util.get_nw(nw_list[ii - k]))
k += 1
# 生成文件名字
filename = nw_list[ii][0:-4] + '.pkl'
# 保存结果
pkl_dir = r"D:\semantic analysis\公共子图节点数\新词\30公共子图//" + key_word + "//"
util.create_directory(pkl_dir)
util.save_nw(g1, pkl_dir + nw_list[ii][0:-4])
num_list.append(nw_list[ii][0:-4] + '\t' + str(g1.number_of_nodes()))
enum_list.append(nw_list[ii][0:-4] + '\t' + str(g1.number_of_edges()))
# 统计节点数
# with open(mcs_dir + filename[0:-4]+'.txt','w',encoding='utf-8') as file:
# for node in g1.nodes():
# file.write(node+'\n')
# util.save_nw(g1,mcs_dir + filename)
ii -= lap
# util.save_file(mcs_dir + key_word+'mcs.txt', record_list)
util.save_file(mcs_dir + 'n' + key_word + 'mcs.txt', num_list)
util.save_file(mcs_dir + 'e' + key_word + 'mcs.txt', enum_list)
def loop_key2(pkl_dir, result_dir, key_word, lap=1):
pkl_dir = pkl_dir.format(key_word)
f_list = util.get_file_list(pkl_dir, '.pkl')
os.chdir(pkl_dir)
# 升序排序
nw_list = sorted(f_list)
ii = 0
# g2是2号 g1是1号,此处获取最末端的网络
g1 = util.get_nw(nw_list[ii])
util.create_directory(result_dir + key_word)
while ii < len(nw_list) - lap:
ii += lap
g2 = util.get_nw(nw_list[ii])
# 生成文件名字
filename = nw_list[ii][0:-4] + '.txt'
result_list = extract_new_nodes_attributes(g1, g2)
util.save_file(result_dir + key_word + "//" + filename, result_list)
g1 = nx.compose(g1, g2)
def main():
# 设置结果保存的目录
result_dir = r'D:\semantic analysis\新纯文本\1新词分句//'
txt_dir = r"D:\semantic analysis\新纯文本\1新词//"
k_list = util.get_key_list()
for key in k_list:
print(key)
# 文件目录
file_list = util.get_file_list(txt_dir + key, ".txt")
# 建立目录
# mk_dir(result_dir+"新词整句//"+key)
mk_dir(result_dir + key)
for file in file_list:
s_list = util.get_list_from_file(txt_dir + key + "//" + file)
# 过滤相同的语句，防止重复计算
# s_list = list(set(s_list))
w_list, p_list = extract_sentence(s_list, key)
util.save_file(result_dir + key + "//" + file, p_list, True)
def cal_mcs(pkl_dir):
os.chdir(pkl_dir)
pkl_list = util.get_file_list(pkl_dir, '.pkl')
# 从小到大排序
pkl_list = sorted(pkl_list)
r_set = set()
r_set1 = set()
i = 0
while i < 40:
set1 = util.get_nw(pkl_list[i])
set2 = util.get_nw(pkl_list[len(pkl_list) - 1 - i])
r_set = set1 | r_set
r_set1 = set2 | r_set1
i += 1
print(len(r_set))
print(len(r_set1))
r_set2 = r_set & r_set1
print(len(r_set2))
print(len(r_set2) / len(r_set))
def loop_key(pkl_dir, result_dir, key_word, lap=1):
pkl_dir = pkl_dir.format(key_word)
f_list = util.get_file_list(pkl_dir, '.pkl')
os.chdir(pkl_dir)
# 升序排序
nw_list = sorted(f_list)
ii = len(nw_list) - 1
# g2是2号 g1是1号,此处获取最末端的网络
g2 = util.get_nw(nw_list[ii])
util.create_directory(result_dir + key_word)
while ii > 0:
jj = ii
ii -= lap
# print(nw_list[ii])
g1 = util.get_nw(nw_list[ii])
# 生成文件名字
filename = nw_list[ii][0:-4] + '-' + nw_list[jj][0:-4] + '.txt'
result_list = cal_connect_real_probability(g1, g2, key_word)
util.save_file(result_dir + key_word + "//" + filename, result_list)
g2 = g1
import re
import tool.util as util
key_list = util.get_key_list()
dict_dir = r"D:\semantic analysis\2016-10-09结果\词频1//"
for key in key_list:
print(key)
set_dict, file_name = util.get_objdict_list(dict_dir + key, ".txt")
date_list = util.get_file_list(dict_dir + key, ".txt")
pattern = re.compile(r"(\d*-\d*)-\d*")
month_array = pattern.findall(" ".join(date_list))
month_array = ["2010"]
util.create_directory(r"D:\semantic analysis\2016-10-12结果\2010年频数统计//" +
key)
# 循环查找月份
for month in month_array:
pattern = re.compile(r"(" + month + "-\d*-\d*)")
date_array = pattern.findall(" ".join(date_list))
print(date_array)
# 循环合并月份频数字典
r_dict = dict()
for file_date in date_array:
r_dict = util.union_dicts(set_dict[file_date + ".txt"], r_dict)
util.save_dict_list(
r_dict,
r"D:\semantic analysis\2016-10-12结果\2010年频数统计//" + key + ".txt")
import tool.util as util
# 计算所有文件里面所有词语出现的次数
key_list = util.get_key_list() + util.get_key_list2()
txt_dir = r"D:\semantic analysis\2016-10-09结果\词频1//"
r_dir = r"D:\semantic analysis\2016-10-09结果\总数1//"
for key in key_list:
print(key)
file_dir = txt_dir + key
dict_list = []
r_list = []
file_list = util.get_file_list(file_dir, ".txt")
for file_name in file_list:
word_list = sorted(
util.get_list_from_file(txt_dir + key + "//" + file_name))
dict_list.append(util.txt2dict(word_list))
r_dict = dict_list[0]
for word_dict in dict_list:
r_dict = util.union_dicts(r_dict, word_dict)
# 对key进行排序
kk = util.sort_by_value(r_dict)
w_list = util.create_dict_list(kk, r_dict)
# 创建目录
util.save_file(r"D:\semantic analysis\2016-10-09结果\总数1//" + key + ".txt",
w_list, False)
import networkx as nx
import tool.util as util
import os
dir = "D:\semantic analysis\新结果\去虚词去单字共现网络\纠结\p//"
os.chdir(dir)
file_list = util.get_file_list(dir, "pkl")
s1 = set()
l1 = list()
sg = util.get_nw(file_list[0])
for file in file_list[0:20]:
g = util.get_nw(file)
sg = nx.compose(g, sg)
gs = set(sg.nodes())
print("gs ", end=" ")
print(len(gs))
for file in file_list[0:20]:
g = util.get_nw(file)
s1 = s1 | set(nx.k_core(g).nodes())
l1.extend(nx.k_core(g).nodes())
print("s1 " + str(len(s1)))
print("l1 " + str(len(l1)))
s2 = set()
l2 = list()
for file in file_list[-50:]:
g = util.get_nw(file)
s2 = s2 | set(nx.k_core(g).nodes())
