ValueError：输入包含NaN，无穷大或者对于dtype（'float64'）而言太大而且scikit-learn

Question

from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.feature_extraction.text import TfidfVectorizer
def number_normalizer(tokens):
    """ Map all numeric tokens to a placeholder.
    For many applications, tokens that begin with a number are not directly
    useful, but the fact that such a token exists can be relevant.  By applying
    this form of dimensionality reduction, some methods may perform better.
    """
    return ("#NUMBER" if token[0].isdigit() else token for token in tokens)


class NumberNormalizingVectorizer(TfidfVectorizer):

    def build_tokenizer(self):
        tokenize = super(NumberNormalizingVectorizer, self).build_tokenizer()
        return lambda doc: list(number_normalizer(tokenize(doc)))

vectorizer = NumberNormalizingVectorizer(stop_words='english', min_df=5)
cocluster = SpectralCoclustering(n_clusters=5, svd_method='arpack', random_state=0)
X = vectorizer.fit_transform(data)

cocluster.fit（X）

我选择SpectralCoclustering来聚集大约30k条推文，在将数据X放入“cocluster”之前一切顺利。

它会引发以下错误。

.env/lib/python3.5/site-packages/sklearn/utils/validation.py", line 43, in _assert_all_finite
    " or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

当我输入代码时报告错误，但它是“假”。发生错误时应该是True，对吧？

那么还有什么可以找到这个bug吗？谢谢！

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py#L43

X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum()) and not np.isfinite(X).all()

假