MiniBatchKMeans OverflowError：无法将浮点无穷大转换为整数？

Question

k我正在尝试根据使用的轮廓分数找到正确的簇数sklearn.cluster.MiniBatchKMeans。

from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import HashingVectorizer

docs = ['hello monkey goodbye thank you', 'goodbye thank you hello', 'i am going home goodbye thanks', 'thank you very much sir', 'good golly i am going home finally']

vectorizer = HashingVectorizer()

X = vectorizer.fit_transform(docs)

for k in range(5):
    model = MiniBatchKMeans(n_clusters = k)
    model.fit(X)

我收到此错误：

Warning (from warnings module):
  File "C:\Python34\lib\site-packages\sklearn\cluster\k_means_.py", line 1279
    0, n_samples - 1, init_size)
DeprecationWarning: This function is deprecated. Please call randint(0, 4 + 1) instead
Traceback (most recent call last):
  File "<pyshell#85>", line 3, in <module>
    model.fit(X)
  File "C:\Python34\lib\site-packages\sklearn\cluster\k_means_.py", line 1300, in fit
    init_size=init_size)
  File "C:\Python34\lib\site-packages\sklearn\cluster\k_means_.py", line 640, in _init_centroids
    x_squared_norms=x_squared_norms)
  File "C:\Python34\lib\site-packages\sklearn\cluster\k_means_.py", line 88, in _k_init
    n_local_trials = 2 + int(np.log(n_clusters))
OverflowError: cannot convert float infinity to integer

我知道这个type(k)问题int，所以我不知道这个问题来自哪里。我可以很好地运行以下命令，但我似乎无法迭代列表中的整数，即使type(2)等于k = 2; type(k)

model = MiniBatchKMeans(n_clusters = 2)
model.fit(X)

甚至运行不同的model作品：

>>> model = KMeans(n_clusters = 2)
>>> model.fit(X)
KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=2, n_init=10,
    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,
    verbose=0)

Answer 1

让我们分析一下您的代码：

• for k in range(5)返回以下序列：
  • 0, 1, 2, 3, 4
• model = MiniBatchKMeans(n_clusters = k)初始化模型n_clusters=k
• 让我们看一下第一次迭代：
  • n_clusters=0用来
  • 在优化代码中（查看输出）：
  • int(np.log(n_clusters))
  • =int(np.log(0))
  • =int(-inf)
  • 错误：整数没有无穷大定义！
  • -> 将 -inf 的浮点值转换为 int 是不可能的！

设置n_clusters=0没有意义！