def input_data(name): #测试输入
uid_list = []
reader = csv.reader(file(abs_path + '/weibo_data/0122_uid.txt', 'rb'))
for line in reader:
uid = line[0].strip('\t\r\n')
uid = uid.strip('\xef\xbb\xbf')
uid_list.append(uid)
uid_weibo = dict()
sw = load_scws()
reader = csv.reader(file(abs_path + '/test_weibo/com_weibo0126.csv', 'rb'))
for mid, w_text, ts in reader:
mid = mid.strip('\xef\xbb\xbf')
if mid in uid_list:
if uid_weibo.has_key(mid):
item = uid_weibo[mid]
item = item + '_' + w_text
uid_weibo[mid] = item
else:
item = w_text
uid_weibo[mid] = item
uid_word = dict()
for k, v in uid_weibo.iteritems():
item = dict()
words = sw.participle(v)
for word in words:
if item.has_key(word[0]):
item[word[0]] = item[word[0]] + 1
else:
item[word[0]] = 1
uid_word[k] = item
return uid_list, uid_word
def input_data(name):#测试输入
uid_list = []
reader = csv.reader(file(abs_path + '/weibo_data/0122_uid.txt', 'rb'))
for line in reader:
uid = line[0].strip('\t\r\n')
uid = uid.strip('\xef\xbb\xbf')
uid_list.append(uid)
uid_weibo = dict()
sw = load_scws()
reader = csv.reader(file(abs_path + '/test_weibo/com_weibo0126.csv', 'rb'))
for mid,w_text,ts in reader:
mid = mid.strip('\xef\xbb\xbf')
if mid in uid_list:
if uid_weibo.has_key(mid):
item = uid_weibo[mid]
item = item + '_' + w_text
uid_weibo[mid] = item
else:
item = w_text
uid_weibo[mid] = item
uid_word = dict()
for k,v in uid_weibo.iteritems():
item = dict()
words = sw.participle(v)
for word in words:
if item.has_key(word[0]):
item[word[0]] = item[word[0]] + 1
else:
item[word[0]] = 1
uid_word[k] = item
return uid_list,uid_word
def input_data(): #测试输入
sw = load_scws()
uid_weibo = dict()
uid_list = []
reader = csv.reader(file('./weibo_data/uid_text_0728.csv', 'rb'))
for mid, w_text in reader:
if uid_weibo.has_key(str(mid)):
uid_weibo[str(mid)] = uid_weibo[str(mid)] + '-' + w_text
else:
uid_weibo[str(mid)] = w_text
if mid not in uid_list:
uid_list.append(mid)
uid_word = dict()
for k, v in uid_weibo.items():
words = sw.participle(v)
word_list = dict()
for word in words:
if (word[1] in cx_dict) and 3 < len(word[0]) < 30 and (
word[0] not in black_word
) and (word[0]
not in single_word_whitelist): #选择分词结果的名词、动词、形容词，并去掉单个词
if word_list.has_key(word[0]):
word_list[word[0]] = word_list[word[0]] + 1
else:
word_list[word[0]] = 1
uid_word[k] = word_list
return uid_list, uid_word
def input_data(): #测试输入
sw = load_scws()
uid_weibo = dict()
uid_list = []
reader = csv.reader(file(abs_path + '/weibo_data/uid_text_0728.csv', 'rb'))
#print 'reader::',reader
for mid, w_text in reader:
print 'mid:::', mid
print 'w_text:::', w_text
v = re_cut(w_text.decode('utf-8'))
words = sw.participle(v.encode('utf-8'))
word_list = dict()
for word in words:
if (word[1] in cx_dict) and 3 < len(word[0]) < 30 and (
word[0] not in black_word
) and (word[0]
not in single_word_whitelist): #选择分词结果的名词、动词、形容词，并去掉单个词
if word_list.has_key(word[0]):
word_list[word[0]] = word_list[word[0]] + 1
else:
word_list[word[0]] = 1
uid_list.append(mid)
uid_weibo[mid] = word_list
return uid_list, uid_weibo
def load_weibo(uid_weibo):
ts = time.time()
domain_dict, domain_count = load_train()
end = time.time()
print '%s' % (end - ts)
len_dict = dict()
total = 0
for k, v in domain_dict.items():
len_dict[k] = len(v)
total = total + len(v)
sw = load_scws()
black = load_black_words()
result_data = dict()
ts = time.time()
for k, v in uid_weibo.items():
words = sw.participle(v)
domain_p = start_p(name_list)
word_list = dict()
for word in words:
if (word[1] in cx_dict) and 3 < len(
word[0]) < 30 and (word[0] not in black) and (
word[0] not in single_word_whitelist
) and (word[0] not in word_list): #选择分词结果的名词、动词、形容词，并去掉单个词
if word_list.has_key(word[0]):
word_list[word[0]] = word_list[word[0]] + 1
else:
word_list[word[0]] = 1
for d_k in domain_p.keys():
start = time.time()
domain_p[d_k] = com_p(word_list, domain_dict[d_k],
domain_count[d_k], len_dict[d_k],
total) #计算文档属于每一个类的概率
end_time = time.time()
print '%s' % (end_time - start)
result_data[k] = domain_p
end = time.time()
print '%s takes %s...' % (k, end - ts)
ts = end
return result_data
def psychology_classfiy(uid_weibo): # 心理状态分类主函数
"""
用户心理状态分类主函数
输入数据示例：字典
{uid1:[weibo1,weibo2,weibo3,...]}
输出数据示例：字典(每个用户对应两个字典，一个是一层分类器的状态比例，另一个是二层分类器（消极状态）的状态比例)
{uid1:{'first':{'negemo':0.2,'posemo':0.3,'middle':0.5},'second':{'anger':0.2,'anx':0.5,'sad':0.1,'other':0.2}}...}
"""
df_dict, df_count = load_dict(f_label)
ds_dict, ds_count = load_dict(s_label)
data_s = s_label
data_f = f_label
data_s.append("other")
data_f.append("middle")
sw = load_scws()
result_data = dict()
for k, v in uid_weibo.items():
domain_f = start_p(data_f)
domain_s = start_p(data_s)
for i in range(0, len(v)):
w_text = re_cut(v[i])
if not len(w_text):
continue
label_f, label_s = find_label(w_text, sw, df_dict, df_count, ds_dict, ds_count)
domain_f[label_f] = domain_f[label_f] + 1
domain_s[label_s] = domain_s[label_s] + 1
for k1, v1 in domain_f.items():
domain_f[k1] = float(v1) / float(len(v))
for k1, v1 in domain_s.items():
if domain_f["negemo"] != 0:
domain_s[k1] = float(v1) / float(len(v))
else:
domain_s[k1] = 0
result_data[k] = {"first": domain_f, "second": domain_s}
return result_data
def psychology_classfiy(uid_weibo):#心理状态分类主函数
'''
用户心理状态分类主函数
输入数据示例：字典
{uid1:[weibo1,weibo2,weibo3,...]}
输出数据示例：字典(每个用户对应两个字典，一个是一层分类器的状态比例，另一个是二层分类器（消极状态）的状态比例)
{uid1:{'first':{'negemo':0.2,'posemo':0.3,'middle':0.5},'second':{'anger':0.2,'anx':0.5,'sad':0.1,'other':0.2}}...}
'''
df_dict,df_count = load_dict(f_label)
ds_dict,ds_count = load_dict(s_label)
data_s = s_label
data_f = f_label
data_s.append('other')
data_f.append('middle')
sw = load_scws()
result_data = dict()
for k,v in uid_weibo.items():
domain_f = start_p(data_f)
domain_s = start_p(data_s)
for i in range(0,len(v)):
w_text = re_cut(v[i])
if not len(w_text):
continue
label_f,label_s = find_label(w_text,sw,df_dict,df_count,ds_dict,ds_count)
domain_f[label_f] = domain_f[label_f] + 1
domain_s[label_s] = domain_s[label_s] + 1
for k1,v1 in domain_f.items():
domain_f[k1] = float(v1)/float(len(v))
for k1,v1 in domain_s.items():
if domain_f['negemo'] != 0:
domain_s[k1] = float(v1)/float(len(v))
else:
domain_s[k1] = 0
result_data[k] = {'first' : domain_f, 'second' : domain_s}
return result_data
def input_data():#测试输入
sw = load_scws()
uid_weibo = dict()
uid_list = []
reader = csv.reader(file(abs_path+'/weibo_data/uid_text_0728.csv', 'rb'))
for mid,w_text in reader:
v = re_cut(w_text.decode('utf-8'))
words = sw.participle(v.encode('utf-8'))
word_list = dict()
for word in words:
if (word[1] in cx_dict) and 3 < len(word[0]) < 30 and (word[0] not in black_word) and (word[0] not in single_word_whitelist):#选择分词结果的名词、动词、形容词，并去掉单个词
if word_list.has_key(word[0]):
word_list[word[0]] = word_list[word[0]] + 1
else:
word_list[word[0]] = 1
uid_list.append(mid)
uid_weibo[mid] = word_list
return uid_list,uid_weibo
def input_data(): # 测试输入
uid_weibo = dict()
uid_list = []
sw = load_scws()
reader = csv.reader(file("./weibo_data/uid_text_0728.csv", "rb"))
for mid, w_text in reader:
text = re_cut(w_text)
if mid not in uid_list:
uid_list.append(mid)
if uid_weibo.has_key(mid):
word_dict = uid_weibo[mid]
words = sw.participle(text)
for word in words:
if (
(word[1] in cx_dict)
and (3 < len(word[0]) < 30 or word[0] in single_word_whitelist)
and (word[0] not in black_word)
): # 选择分词结果的名词、动词、形容词，并去掉单个词
if word_dict.has_key(str(word[0])):
word_dict[str(word[0])] = word_dict[str(word[0])] + 1
else:
word_dict[str(word[0])] = 1
uid_weibo[mid] = word_dict
else:
word_dict = dict()
words = sw.participle(text)
for word in words:
if (
(word[1] in cx_dict)
and (3 < len(word[0]) < 30 or word[0] in single_word_whitelist)
and (word[0] not in black_word)
): # 选择分词结果的名词、动词、形容词，并去掉单个词
if word_dict.has_key(str(word[0])):
word_dict[str(word[0])] = word_dict[str(word[0])] + 1
else:
word_dict[str(word[0])] = 1
uid_weibo[mid] = word_dict
return uid_list, uid_weibo
def load_weibo(uid_weibo):
ts = time.time()
domain_dict,domain_count = load_train()
end = time.time()
print '%s' % (end-ts)
len_dict = dict()
total = 0
for k,v in domain_dict.items():
len_dict[k] = len(v)
total = total + len(v)
sw = load_scws()
black = load_black_words()
result_data = dict()
ts = time.time()
for k,v in uid_weibo.items():
words = sw.participle(v)
domain_p = start_p(name_list)
word_list = dict()
for word in words:
if (word[1] in cx_dict) and 3 < len(word[0]) < 30 and (word[0] not in black) and (word[0] not in single_word_whitelist) and (word[0] not in word_list):#选择分词结果的名词、动词、形容词，并去掉单个词
if word_list.has_key(word[0]):
word_list[word[0]] = word_list[word[0]] + 1
else:
word_list[word[0]] = 1
for d_k in domain_p.keys():
start = time.time()
domain_p[d_k] = com_p(word_list,domain_dict[d_k],domain_count[d_k],len_dict[d_k],total)#计算文档属于每一个类的概率
end_time = time.time()
print '%s' % (end_time-start)
result_data[k] = domain_p
end = time.time()
print '%s takes %s...' % (k,end-ts)
ts = end
return result_data
def read_csv(domain_dict,domain_count,d_time):
sw = load_scws()
black = load_black_words()
text = ''
word_dict = dict()
reader = csv.reader(file('./add_dict/%s_new.csv'% d_time, 'rb'))
for line in reader:
#line = line[0].strip('\xef\xbb\xbf')
#line = line.strip('\n')
text = text + ',' + line
#print text.encode('utf-8')
words = sw.participle(text)
for word in words:
if (word[1] in cx_dict) and (3 < len(word[0]) < 30 or word[0] in single_word_whitelist) and (word[0] not in black):#选择分词结果的名词、动词、形容词，并去掉单个词
if domain_dict.has_key(str(word[0])):
domain_dict[str(word[0])] = domain_dict[str(word[0])] + 1
else:
domain_dict[str(word[0])] = 1
domain_count = domain_count + 1
return domain_dict,domain_count
def input_data(): #测试输入
uid_weibo = dict()
uid_list = []
sw = load_scws()
reader = csv.reader(file('./weibo_data/uid_text_0728.csv', 'rb'))
for mid, w_text in reader:
text = re_cut(w_text)
if mid not in uid_list:
uid_list.append(mid)
if uid_weibo.has_key(mid):
word_dict = uid_weibo[mid]
words = sw.participle(text)
for word in words:
if (word[1] in cx_dict) and (
3 < len(word[0]) < 30
or word[0] in single_word_whitelist
) and (word[0] not in black_word): #选择分词结果的名词、动词、形容词，并去掉单个词
if word_dict.has_key(str(word[0])):
word_dict[str(word[0])] = word_dict[str(word[0])] + 1
else:
word_dict[str(word[0])] = 1
uid_weibo[mid] = word_dict
else:
word_dict = dict()
words = sw.participle(text)
for word in words:
if (word[1] in cx_dict) and (
3 < len(word[0]) < 30
or word[0] in single_word_whitelist
) and (word[0] not in black_word): #选择分词结果的名词、动词、形容词，并去掉单个词
if word_dict.has_key(str(word[0])):
word_dict[str(word[0])] = word_dict[str(word[0])] + 1
else:
word_dict[str(word[0])] = 1
uid_weibo[mid] = word_dict
return uid_list, uid_weibo
def read_csv(domain_dict, domain_count, d_time):
sw = load_scws()
black = load_black_words()
text = ''
word_dict = dict()
reader = csv.reader(file('./add_dict/%s_new.csv' % d_time, 'rb'))
for line in reader:
#line = line[0].strip('\xef\xbb\xbf')
#line = line.strip('\n')
text = text + ',' + line
#print text.encode('utf-8')
words = sw.participle(text)
for word in words:
if (word[1] in cx_dict) and (3 < len(
word[0]) < 30 or word[0] in single_word_whitelist) and (
word[0] not in black): #选择分词结果的名词、动词、形容词，并去掉单个词
if domain_dict.has_key(str(word[0])):
domain_dict[str(word[0])] = domain_dict[str(word[0])] + 1
else:
domain_dict[str(word[0])] = 1
domain_count = domain_count + 1
return domain_dict, domain_count
#from global_utils import es_flow_text as es_text
#from global_utils import flow_text_index_name_pre, flow_text_index_type
#from time_utils import ts2datetime, datetime2ts
#from parameter import SOCIAL_SENSOR_TIME_INTERVAL as time_interval
#from parameter import SOCIAL_SENSOR_FORWARD_RANGE as forward_time_range
PROCESS_GRAM = 3
Min_CLUSTER_NUM = 2
MAX_CLUSTER_NUM = 15
CLUTO_FOLDER = 'cluto'
COMMENT_WORDS_CLUSTER_NUM = 10
CLUSTERING_KMEANS_CLUSTERING_NUM = 10
CLUTO_EXECUTE_PATH = './cluto-2.1.2/Linux-i686/vcluster'
AB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), './')
sw = load_scws()
#cx_dict = set(['Ag','a','an','Ng','n','nr','ns','nt','nz','Vg','v','vd','vn','@','j'])
cx_dict = set(['Ng', 'n', 'nr', 'ns', 'nt', 'nz']) # 关键词词性词典, 保留名词
def freq_word(items):
"""
统计一条文本的词频，对文本进行过滤后再分词
input:
items:微博字典，{"mid": 12345, "text": text}
output:
top_word:词和词频构成的字典，如:{词:词频, 词:词频}
"""
word_list = []
text = items["text"]
#print type(text)
domain_dict[str(word[0])] = domain_dict[str(word[0])] + 1
else:
domain_dict[str(word[0])] = 1
domain_count = domain_count + 1
return domain_dict,domain_count
def write_has(filename,has_word):
n = len(has_word)
keyword = TopkHeap(n)
for k,v in has_word.items():
keyword.Push((v,k))
keyword_data = keyword.TopK()
with open('%s/topic_dict/%s_ori.csv' % (abs_path,filename), 'wb') as f:
writer = csv.writer(f)
for i in range(0,len(keyword_data)):
if keyword_data[i][0] > 1:
writer.writerow((keyword_data[i][0],keyword_data[i][1]))
if __name__ == '__main__':
sw = load_scws()
for j in name_list:
new_dict,new_count = read_csv(sw,DOMAIN_DICT_ORI[j],DOMAIN_COUNT_ORI[j],j)#更新类型字典
#write_has(j,new_dict)#将结果写入文件
