ValueError：预期可迭代原始文本文档，接收到字符串对象。使用 tfidf 和选择功能预测新的测试数据

Question

所以我用 sklearn 朴素贝叶斯分类器构建了一个模型。 我需要知道如何用输入预测一个句子

当我对句子进行硬编码时，它的工作正常，看起来像这样

new_sentence = ['its so broken']
new_testdata_tfidf= tfidf.transform(new_sentence) 
#transform it to matrix to see the score TFIDF on the training data
fit_feature_selection = selection.transform(new_testdata_tfidf) 
#transform the new data to see if the feature remove or not, because after tfidf i use chi2 selection feature.
predicted = classifier.predict(feature_selection )
#then predict it. the classificaiton out, the class is -1 which is the correct answer

我需要手动输入文本数据作为输入，所以我这样使用

new_sentence = input[('')] 
#i input the same sentence its so broken 
new_testdata_tfidf= tfidf.transform(new_sentence) 
#transform it to matrix to see the score TFIDF on the training data
fit_feature_selection = selection.transform(new_testdata_tfidf) 
#transform the new data to see if the feature remove or not, because after tfidf i use chi2 selection feature.
predicted = classifier.predict(feature_selection )

但它给了我输出

  File "C:\Users\Myfile\OneDrive\Desktop\model.py", line 170, in <module>
   new_testdata_tfidf= tfidf.transform(new_sentence) 

  File "E:\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1898, in transform
    X = super().transform(raw_documents)

  File "E:\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1265, in transform
    "Iterable over raw text documents expected, "

ValueError: Iterable over raw text documents expected, string object received.

如何解决这个问题？ 任何帮助都非常感谢。

Answer 1

如果您尝试在代码中使用 new_sentence = input[('')] 传递字符串列表，那么您可能希望将其替换为

new_sentence = [input()]

希望这会有所帮助。

Answer 2

您是否尝试过将新句子作为数组传递？即

new_testdata_tfidf= tfidf.transform([new_sentence])

第一个实例是传递一个带有一个字符串元素的数组，另一个实例是简单地传递一个字符串