Scikit学习如何检查模型(例如TfidfVectorizer)是否已经适合 [英] Scikit-learn how to check if model (e.g. TfidfVectorizer) has been already fit

问题描述

要从文本中提取特征，如何检查矢量数据(例如TfIdfVectorizer或CountVectorizer)是否已经适合训练数据?
特别是，我希望代码自动确定矢量化器是否已经适合.

For feature extraction from text, how to check if a vectorizer (e.g. TfIdfVectorizer or CountVectorizer) has been already fit on a training data?
In particular, I want the code to automatically figure out if a vectorizer has been already fit.

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer()

def vectorize_data(texts):
  # if vectorizer has not been already fit
  vectorizer.fit_transform(texts)
  # else
  vectorizer.transform(texts)

推荐答案

您可以使用

You can use the check_is_fitted which is basically made for doing this.

在 TfidfVectorizer.transform() ，您可以检查其用法:

In the source of TfidfVectorizer.transform() you can check its usage:

def transform(self, raw_documents, copy=True):

    # This is what you need.
    check_is_fitted(self, '_tfidf', 'The tfidf vector is not fitted')

    X = super(TfidfVectorizer, self).transform(raw_documents)
    return self._tfidf.transform(X, copy=False)

因此，您可以这样做:

from sklearn.utils.validation import check_is_fitted

def vectorize_data(texts):

    try:
        check_is_fitted(vectorizer, '_tfidf', 'The tfidf vector is not fitted')
    except NotFittedError:
        vectorizer.fit(texts)

    # In all cases vectorizer if fit here, so just call transform()
    vectorizer.transform(texts)

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