我正在使用scikit-learn的MultinomialNB和Vectorizer来构建一个预测模型，用于判断评论是好还是坏。
在对标记数据进行训练后，我该如何使用它来预测新的评论（或现有评论）？我收到了下面的错误信息。
from sklearn.feature_extraction.text import CountVectorizerfrom sklearn.cross_validation import train_test_splitfrom sklearn.naive_bayes import MultinomialNBX = vectorizer.fit_transform(df.quote)X = X.tocsc()Y = (df.fresh == 'fresh').values.astype(np.int)xtrain, xtest, ytrain, ytest = train_test_split(X, Y)clf = MultinomialNB().fit(xtrain, ytrain)new_review = ['this is a new review, movie was awesome']new_review = vectorizer.fit_transform(new_review)print df.quote[15]print(clf.predict(df.quote[10])) #predict existing review in dataframeprint(clf.predict(new_review)) #predict new reviewTechnically, Toy Story is nearly flawless.---------------------------------------------------------------------------TypeError Traceback (most recent call last)<ipython-input-91-27a0698bbd1f> in <module>() 15 16 print df.quote[15]---> 17 print(clf.predict(df.quote[10])) #predict existing quote in dataframe 18 print(clf.predict(new_review)) #predict new review//anaconda/lib/python2.7/site-packages/sklearn/naive_bayes.pyc in predict(self, X) 60 Predicted target values for X 61 """---> 62 jll = self._joint_log_likelihood(X) 63 return self.classes_[np.argmax(jll, axis=1)] 64 //anaconda/lib/python2.7/site-packages/sklearn/naive_bayes.pyc in _joint_log_likelihood(self, X) 439 """Calculate the posterior log probability of the samples X""" 440 X = atleast2d_or_csr(X)--> 441 return (safe_sparse_dot(X, self.feature_log_prob_.T) 442 + self.class_log_prior_) 443 //anaconda/lib/python2.7/site-packages/sklearn/utils/extmath.pyc in safe_sparse_dot(a, b, dense_output) 178 return ret 179 else:--> 180 return fast_dot(a, b) 181 182 TypeError: Cannot cast array data from dtype('float64') to dtype('S32') according to the rule 'safe'回答：
你需要传递一个词袋表示给predict，而不是直接传递文本。你对new_review的处理几乎是正确的，只需将new_review = vectorizer.transform(new_review)进行更改（参见@Stergios的评论）。试试这个：
print(clf.predict(X[10, :]))