python - MiniBatchKMeans 溢出错误 : cannot convert float infinity to integer?

Question

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

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type(k)是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;类型(k)

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

即使运行不同的模型也能工作:

>>> 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) 使用 n_clusters=k 初始化模型
• 让我们看一下第一次迭代:
  • 使用n_clusters=0
  • 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!