Python Kmeans 打印每个簇中单词的绝对频率

Question

你好有没有办法打印出集群中每个单词的绝对频率？我的代码如下所示：

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(list)

true_k = 4

model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)

model.fit(X)

print("Top terms per cluster:")

order_centroids = model.cluster_centers_.argsort()[:, ::-1]
terms = vectorizer.get_feature_names()
for i in range(true_k):
    print("Cluster %d:" % i,)
    for ind in order_centroids[i, :5]:
        print(' %s' % terms[ind],)
    print

我的结果例如：

每个集群的热门术语：

簇 0：

房子

屋顶

table

椅子

电视

集群 1：

...

但我想要这样的东西，每个词的绝对频率：

簇 0：

房子 65

屋顶 45

table 44

椅子 33

电视 18

提前谢谢你:)

Answer 1

不确定 tfidfvectorizer 对单词的需求是什么。但无论如何，使用 kmeans 只是预测每个单词的集群标签。并通过 df[df.cluster==#somelabel].words.value_counts

简单地检查每个集群中的词频

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

words = ['this','is','a','very','long','text','my','name','is','not','cortana','today','I','will',
'write','a','long','text','I','am','from','planet','earth','this','text','does','not','make',
 'sense']

#tfidf
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(words)

#kmeans
true_k = 4
model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)
model.fit(X)
lab = model.predict(X)

#save cluster labels for each sample in a dataframe 
df = pd.DataFrame({'words':words, 'cluster':lab})

#check word freq for cluster==1
df[df.cluster==1].words.value_counts()

Python Kmeans 打印每个簇中单词的绝对频率

Python Kmeans Print absolute frequency of words in each cluster

python

cluster-analysis

k-means

frequency

tf-idf