def create_predict(HudongItem_csv):
# 读取neo4j内容
db = Neo4j()
db.connectDB()
data_set = db.getLabeledHudongItem('labels.txt')
classifier = Classifier('wiki.zh.bin')
classifier.load_trainSet(data_set)
classifier.set_parameter(weight=[1.0, 3.0, 0.2, 4.0, 0],k=10)
predict_List = readCSVbyColumn(HudongItem_csv, 'title')
file_object = open('predict_labels2.txt','a')
count = 0
vis = set()
for p in predict_List:
cur = HudongItem(db.matchHudongItembyTitle(p))
if count > 200:
break
count += 1
if count <140 :
continue
title = cur.title
if title in vis:
continue
vis.add(title)
label = classifier.KNN_predict(cur)
print(str(title)+" "+str(label)+": "+str(count)+"/"+str(len(predict_List)))
file_object.write(str(title)+" "+str(label)+"\n")
file_object.close()
def create_predict(HudongItem_csv):
classifier = Classifier('wiki.zh.bin')
data_set=[]
f = open("Dataset1.txt", "rb")
while 1:
try:
obj = pickle.load(f)
data_set.append(obj)
except:
break
f.close()
classifier.load_trainSet(data_set)
classifier.set_parameter(weight=[1.0, 3.0, 0.2, 4.0, 0],k=10)
file_object = open('predict_labels.txt','a')
count = 0
vis = set()
file = open("cur_list1.txt", "rb")
cur_list=[]
while 1:
try:
obj = pickle.load(file)
cur_list.append(obj)
except:
break
file.close()
for cur in cur_list:
title = cur.title
if title in vis:
continue
vis.add(title)
label = classifier.KNN_predict(cur)
print(str(title)+" "+str(label)+": "+str(count)+"/")
file_object.write(str(title)+" "+str(label)+"\n")
file_object.close()
