我正在尝试在Streamlit.io应用程序上拟合我的模型，但出现上述Value-Error。但这在Jupyter Notebook上不会给出相同的错误，请使用任何更好的方法都将有所帮助。
我位于VScode上基于Conda的环境中，因此VScode上使用的Pandas，Sklearn和Python版本与Jupyter Notebook相同。
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
File "c:\users\8470p\anaconda3\lib\site-packages\streamlit\ScriptRunner.py", line 311, in _run_script exec(code, module.__dict__)
File "C:\Users\8470p\app2.py", line 122, in bow_transformer = CountVectorizer(analyzer=text_process).fit(messages['message'])
File "c:\users\8470p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1024, in fit self.fit_transform(raw_documents)
File "c:\users\8470p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1058, in fit_transform self.fixed_vocabulary_)
File "c:\users\8470p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 962, in _count_vocab analyze = self.build_analyzer()
File "c:\users\8470p\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 339, in build_analyzer if self.analyzer == 'char':
File "c:\users\8470p\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
msg_train, msg_test, label_train, label_test = train_test_split(messages['message'], messages['label'], test_size=0.2)
@st.cache(suppress_st_warning = True)
def Pipeline_Processing(a = msg_train, b = msg_test, c = label_train, d =label_test):
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
])
pipeline.fit(msg_train,label_train)
predictions = pipeline.predict(msg_test)
return predictions
Pipeline_Processing()
if __name__ == "__main__":
main()