unsup_df是一个DataFrame只有一列：review。
我想形成 2 个评论集群。一正一负。
from sklearn.cluster import KMeans
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(unsup_df)
num_clusters = 2
km = KMeans(n_clusters=num_clusters)
km.fit(tfidf_matrix)
clusters = km.labels_.tolist()
上面这段代码抛出了一个错误：
ValueError: n_samples=1 应该 >= n_clusters=2
在线上km.fit(tfidf_matrix)
以下是如何将 k-means 拟合到 Pandas 中的一维文本数据：
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
df = pd.DataFrame({"corpus": ["I am Sam. Sam-I-am",
"That Sam-I-am! That Sam-I-am! I do not like that Sam-I-am",
"Do you like green eggs and ham?",
"I do not like them, Sam-I-am. I do not like green eggs and ham"]})
x = TfidfVectorizer().fit_transform(df.corpus)
km = KMeans(n_clusters=2).fit(x)
km.labels_.tolist() # Results in a list similar to this: [0, 0, 1, 1]
你unsup_df的形状一定是错误的。否则，它应该工作。
