我有一个很小的短字符串的很小的列表，我想对其进行(1)聚类，并且(2)使用该模型预测新字符串属于哪个聚类。
运行第一部分工作正常，但无法获得新字符串的预测。
第一部分from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
# List of
documents_lst = ['a small, narrow river',
'a continuous flow of liquid, air, or gas',
'a continuous flow of data or instructions, typically one having a constant or predictable rate.',
'a group in which schoolchildren of the same age and ability are taught',
'(of liquid, air, gas, etc.) run or flow in a continuous current in a specified direction',
'transmit or receive (data, especially video and audio material) over the Internet as a steady, continuous flow.',
'put (schoolchildren) in groups of the same age and ability to be taught together',
'a natural body of running water flowing on or under the earth']
# 1. Vectorize the text
tfidf_vectorizer = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf_vectorizer.fit_transform(documents_lst)
print('tfidf_matrix.shape: ', tfidf_matrix.shape)
# 2. Get the number of clusters to make .. (find a better way than random)
num_clusters = 3
# 3. Cluster the defintions
km = KMeans(n_clusters=num_clusters, init='k-means++').fit(tfidf_matrix)
clusters = km.labels_.tolist()
print(clusters)
哪个返回:tfidf_matrix.shape: (8, 39)
[0, 1, 0, 2, 1, 0, 2, 0]
第二部分
失败的部分:predict_doc = ['A stream is a body of water with a current, confined within a bed and banks.']
tfidf_vectorizer = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf_vectorizer.fit_transform(predict_doc)
print('tfidf_matrix.shape: ', tfidf_matrix.shape)
km.predict(tfidf_matrix)
错误:ValueError: Incorrect number of features. Got 7 features, expected 39
FWIW:我有点理解训练和预测在向量化之后具有不同的特征...
我愿意接受任何解决方案，包括从kmeans更改为更适合于短文本聚类的算法。
提前致谢
最佳答案
为了完整起见，我将使用here的答案回答我自己的问题，但不能回答该问题。但是我的答案
from sklearn.cluster import KMeans
list1 = ["My name is xyz", "My name is pqr", "I work in abc"]
list2 = ["My name is xyz", "I work in abc"]
vectorizer = TfidfVectorizer(min_df = 0, max_df=0.5, stop_words = "english", charset_error = "ignore", ngram_range = (1,3))
vec = vectorizer.fit(list1) # train vec using list1
vectorized = vec.transform(list1) # transform list1 using vec
km = KMeans(n_clusters=2, init='k-means++', n_init=10, max_iter=1000, tol=0.0001, precompute_distances=True, verbose=0, random_state=None, n_jobs=1)
km.fit(vectorized)
list2Vec = vec.transform(list2) # transform list2 using vec
km.predict(list2Vec)
归功于@IrshadBhat
关于python-3.x - 使用带有kmeans(sklearn)的新文本预测吗？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/42825655/