我有一个充满 .txt 文件(文档)的目录。首先，我加载文档并去掉一些括号并删除一些引号，因此文档如下所示，例如:
document1:
is a scientific discipline that explores the construction and study of algorithms that can learn from data Such algorithms operate by building a model
document2:
Machine learning can be considered a subfield of computer science and statistics It has strong ties to artificial intelligence and optimization which deliver methods
所以我从这样的目录加载文件:
preprocessDocuments =[[' '.join(x) for x in sample[:-1]] for sample in load(directory)]
documents = ''.join( i for i in ''.join(str(v) for v
in preprocessDocuments) if i not in "',()")
然后我尝试对 document1 和 document2 进行矢量化，以创建训练矩阵，如下所示:
from sklearn.feature_extraction.text import HashingVectorizer
vectorizer = HashingVectorizer(analyzer='word')
X = HashingVectorizer.fit_transform(documents)
X.toarray()
然后这是输出:
raise ValueError("empty vocabulary; perhaps the documents only"
ValueError: empty vocabulary; perhaps the documents only contain stop words
鉴于此，我如何创建矢量表示？我以为我在documents中携带了加载的文件，但似乎无法安装文档。
最佳答案
文档的内容是什么？ It looks like它应该是文件名或带有标记的字符串的列表。另外，您应该使用对象调用 fit_transform，而不是像静态方法那样，即。 e. vectorizer.fit_transform(文档)。
例如，这在这里有效:
from sklearn.feature_extraction.text import HashingVectorizer
documents=['this is a test', 'another test']
vectorizer = HashingVectorizer(analyzer='word')
X = vectorizer.fit_transform(documents)
关于python - scikit-learn 中的词汇匹配问题？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/27631797/