NaiveBayes Multi-Label Classifier

#Define a pipeline combining a text feature extractor with multi label classifer
NB_pipeline = Pipeline([
                        ('tfidf',TfidfVectorizer()),
                        ('clf', OneVsRestClassifier(MultinomialNB(fit_prior=True,class_prior=None))),
                        ])
                        
#Train the model and get the prediction
score = []
for code in label:
    # train the model
    NB_pipeline.fit(x_train,train[code])
    #compute the testing accuracy
    prediction = NB_pipeline.predict(x_test)
    score.append(roc_auc_score(test[code],prediction))