classifier of chains

from skmultilearn.problem_transform import ClassifierChain
from sklearn.naive_bayes import GaussianNB
#scikit-learn version>=0.21 
from sklearn.metrics import multilabel_confusion_matrix
from scipy.sparse import csr_matrix
def svm():
　　

　　clf = ClassifierChain(GaussianNB())
　　S = csr_matrix(train_labels)
　　#S为sprase matrix
　　clf.fit(train_features, S)
　　

　　prediction = clf.predict(csr_matrix(test_features))
　　

　　cnf_matrix = multilabel_confusion_matrix(y_true, y_prediction)
# print(cnf_matrix)

　　TN = cnf_matrix[:, 0, 0]
　　TP = cnf_matrix[:, 1, 1]
　　FN = cnf_matrix[:, 1, 0]
　　FP = cnf_matrix[:, 0, 1]
　　

　　ACC = accuracy_score(y_true, y_prediction)
　　TPR =  metrics.recall_score(y_true, y_prediction, average='macro')
　　TNR =  np.mean(TN / (TN + FP))
　　F1_score =  metrics.f1_score(y_true, y_prediction, average='macro')