Streamlit ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

Question

我正在尝试让我的模型适合 Streamlit.io 应用程序，但我遇到了上述值错误。但它不会在 Jupyter Notebook 上给出相同的错误请任何更好的方法都会有很大帮助。

 
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
File "c:\users70p\anaconda3\lib\site-packages\streamlit\ScriptRunner.py", line 311, in _run_script exec(code, module.__dict__)
File "C:\Users70p\app2.py", line 122, in  bow_transformer = CountVectorizer(analyzer=text_process).fit(messages['message'])
File "c:\users70p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1024, in fit self.fit_transform(raw_documents)
File "c:\users70p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1058, in fit_transform self.fixed_vocabulary_)
File "c:\users70p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 962, in _count_vocab analyze = self.build_analyzer()
File "c:\users70p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 339, in build_analyzer if self.analyzer == 'char':
File "c:\users70p\anaconda3\lib\site-packages\pandas\core\generic.py", line 1555, in __nonzero__ self.__class__.__name__

在此处输入代码



    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import Pipeline
    from sklearn.metrics import classification_report
    from sklearn.feature_extraction.text import TfidfTransformer
    from sklearn.naive_bayes import MultinomialNB

    bow_transformer = 
    CountVectorizer(analyzer=text_process).fit(messages['message'])

    msg_train, msg_test, label_train, label_test = 
    train_test_split(messages['message'], messages['label'], test_size=0.2)

    pipeline = Pipeline([
      ('bow', CountVectorizer(analyzer=text_process)),  # strings to token 
    integer counts
    ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores
    ('classifier', MultinomialNB()),  # train on TF-IDF vectors w/ Naive Bayes 
    classifier
    ])

    NB_Clasifier = pipeline.fit(msg_train,label_train)

Answer 1

一个重要线索是它在 Jupyter notebook 中有效，但在 Streamlit 中无效，这表明您的工作环境存在差异。

当未正确比较系列时，Pandas 会发出您看到的错误。有一个.

但是由于你的错误隐藏在 sklearn 中（而不是你自己的代码），你遇到的问题很可能可以通过将 Jupyter 中使用的 sklearn 版本与你在使用时安装的版本相匹配来解决Streamlit.

如果您将 post 更新为您在每种情况下使用的 Pandas、SKlearn 和 Python 的版本（Jupyter 和 Streamlit），它将更容易提供帮助你弄明白了。

它也可能有助于 post 将整个回溯（不仅仅是上半部分）作为纯文本而不是屏幕截图。

感谢您试用 Streamlit！

Streamlit ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

Streamlit ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

python

nlp

model

countvectorizer

streamlit