def __init__(self,num,data):

self.data = data

self.num_clusters = num

self.Q_list,self.tt = self.data_load()

self.tfidf_matrix = self.tf_idf()

self.result = self.Kmeans()

#global data

def data_load(self):

if type(self.data) == list:

pass

else:

try:

suffix = str(self.data).split('.')[1]

if suffix == 'csv':

with open(self.data,errors='ignore') as f:

self.data = pd.read_csv(f).values.tolist()

#data = open(self.data,errors='ignore').values.tolist()

elif suffix == 'xlsx':

with pd.read_excel(self.data,header = None) as f:

self.data = f.values.tolist()

print('已读取文件: ',suffix)

except Exception:

print('文件读取失败！只能读取list、csv、xlsx文件')

pass

Q_list = [i[0] for i in self.data]

return Q_list,self.data

def tf_idf(self):

#analyse.set_stop_words('all_filter.txt')

def jieba_tockenize(text):

return jieba.lcut(text)

tfidf_vectorizer = TfidfVectorizer(tokenizer=jieba_tockenize,

sublinear_tf=True, lowercase=False)

tfidf_vectorizer.fit(self.Q_list)

vbchart = dict(map(lambda t:(t[1],t[0]),tfidf_vectorizer.vocabulary_.items()))

tfidf_matrix = tfidf_vectorizer.transform(self.Q_list)

return tfidf_matrix

def Kmeans(self):

km_cluster = KMeans(n_clusters=self.num_clusters,max_iter=200,

n_init=40,tol=1e-6,init='random',n_jobs=-1

)

result = km_cluster.fit_predict(self.tfidf_matrix)

return result

def disp(self):

key_temp = []

keyword_list = []

res = self.result

tt = self.tt

for i in range(self.num_clusters):

text = ''

for j in range(len(res)):

if res[j] == i:

text += self.Q_list[j]

keywords = analyse.textrank(text)

#keyword_list.append(",".join(keywords[:3]))

for i in keywords:

key_temp.append(i)

key_temp = list(set(key_temp))

keyword_list.append(",".join(keywords))

#keyword_list.append(key_temp[:3])

#tt[i][0],tt[i][1],tt[i][2],tt[i][3]

ans = [[res[i],keyword_list[res[i]],tt[i][0],tt[i][1],tt[i][2],tt[i][3],tt[i][4]] for i in range(len(res))]

ans = sorted(ans,key=lambda x:x[0])

dff = pd.DataFrame(ans)

dff.to_csv('ans\\cluster_ans.csv',index=False,header=False,encoding='utf-8-sig')