这就是我调用TFIDFVectorizer的方式:

vectorizer = TfidfVectorizer(
                vocabulary=selected_vocabulary,
                stop_words='english',
                use_idf=True,
                norm=norm,
                tokenizer=self.tokenize,
                lowercase=True,
                smooth_idf=True)

当我打电话时收到此错误
vectorizer.transform(data_to_vectorize)

错误:
  File "/root/anaconda/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 1305, in transform
    return self._tfidf.transform(X, copy=False)

  File "/root/anaconda/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 1024, in transform
    raise ValueError("idf vector not fitted")

ValueError: idf vector not fitted

这个错误在这里意味着什么?

最佳答案

您需要先拟合模型(例如,根据数据构建词汇表),然后才能转换任意文本:

vectorizer.fit(data_to_vectorize)
X = vectorizer.transform(data_to_vectorize)

或者
X = vectorizer.fit_transform(data_to_vectorize)

关于python - 这个错误是什么意思 "idf vector not fitted",我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/28239915/

10-11 06:31