我正在尝试通过sklearn学习如何处理文本数据，但遇到一个我无法解决的问题。
我正在遵循的教程是：http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
输入是一个包含两列的pandas数据框。一列是文本，另一列是二元类别。
代码：
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.pipeline import Pipelinetraindf, testdf = train_test_split(nlp_df, stratify=nlp_df['class'])x_train = traindf['text']x_test = traindf['text']y_train = traindf['class']y_test = testdf['class']# CVcount_vect = CountVectorizer(stop_words='english')x_train_modified = count_vect.fit_transform(x_train)x_test_modified = count_vect.transform(x_test)# TF-IDFidf = TfidfTransformer()fit = idf.fit(x_train_modified)x_train_mod2 = fit.transform(x_train_modified)# MNBmnb = MultinomialNB()x_train_data = mnb.fit(x_train_mod2, y_train)text_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB()), ])predicted = text_clf.predict(x_test_modified)当我尝试运行最后一行时：
---------------------------------------------------------------------------NotFittedError Traceback (most recent call last)<ipython-input-64-8815003b4713> in <module>()----> 1 predicted = text_clf.predict(x_test_modified)~/anaconda3/lib/python3.6/site-packages/sklearn/utils/metaestimators.py in <lambda>(*args, **kwargs) 113 114 # lambda, but not partial, allows help() to work with update_wrapper--> 115 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) 116 # update the docstring of the returned function 117 update_wrapper(out, self.fn)~/anaconda3/lib/python3.6/site-packages/sklearn/pipeline.py in predict(self, X) 304 for name, transform in self.steps[:-1]: 305 if transform is not None:--> 306 Xt = transform.transform(Xt) 307 return self.steps[-1][-1].predict(Xt) 308 ~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in transform(self, raw_documents) 918 self._validate_vocabulary() 919 --> 920 self._check_vocabulary() 921 922 # use the same matrix-building strategy as fit_transform~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in _check_vocabulary(self) 301 """Check if vocabulary is empty or missing (not fit-ed)""" 302 msg = "%(name)s - Vocabulary wasn't fitted."--> 303 check_is_fitted(self, 'vocabulary_', msg=msg), 304 305 if len(self.vocabulary_) == 0:~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in check_is_fitted(estimator, attributes, msg, all_or_any) 766 767 if not all_or_any([hasattr(estimator, attr) for attr in attributes]):--> 768 raise NotFittedError(msg % {'name': type(estimator).__name__}) 769 770 NotFittedError: CountVectorizer - Vocabulary wasn't fitted.关于如何修复此错误，您有什么建议吗？我已经正确地在测试数据上转换了CV模型。我甚至检查了词汇列表是否为空，它不是（count_vect.vocabulary_）
谢谢！
回答：
你的问题中有几个问题。
首先，你实际上没有对管道进行拟合，因此出现了错误。仔细查看链接的教程，你会看到有一个步骤text_clf.fit（其中text_clf确实是管道）。
其次，你没有正确使用管道的概念，管道的目的是端到端地拟合整个流程；相反，你一个接一个地拟合其各个组件…如果你再次查看教程，你会看到管道拟合的代码：
text_clf.fit(twenty_train.data, twenty_train.target) 使用的是数据的初始形式，不是它们的中间转换，如你所做的那样；教程的目的是展示如何将各个转换包装在（并替换为）一个管道中，不是在这些转换之上使用管道…
第三，你应该避免将变量命名为fit – 这是保留关键字；同样，我们不使用CV来缩写Count Vectorizer（在机器学习术语中，CV代表交叉验证）。
话虽如此，这是使用你的管道正确的方式：
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.pipeline import Pipelinetraindf, testdf = train_test_split(nlp_df, stratify=nlp_df['class'])x_train = traindf['text']x_test = traindf['text']y_train = traindf['class']y_test = testdf['class']text_clf = Pipeline([('vect', CountVectorizer(stop_words='english')), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB()), ])text_clf.fit(x_train, y_train) predicted = text_clf.predict(x_test)如你所见，管道的目的是使事情变得更简单（与依次使用各个组件相比），而不是使事情变得更加复杂…
