- 半监督学习
- 代码
import numpy
from sklearn import datasets
iris=datasets.load_iris()
labels=np.copy(iris.target)
random_unlabeled_points=np.random.rand(len(iris.target))
random_unlabeled_points=random_unlabeled_points<0.3
Y=labels[random_unlabeled_points]
labels[random_unlabeled_points]=-1
print("Unlabeled Number:",list(labels).count(-1))
from sklearn.semi_supervised import LabelPropagation
label_prop_model=LabelPropagation()
label_prop_model.fit(iris.data,labels)
Y_pred=label_prop_model.predict(iris.data)
Y_pred=Y_pred[random_unlabeled_points]
from sklearn.metrics import accuracy_score,recall_score,f1_score
print('ACC:',accuracy_score(Y,Y_pred))
print('recall:',recall_score(Y,Y_pred,average= 'macro'))
print('f1_socre:',f1_score(Y,Y_pred,average= 'macro'))
