python scikit-learn SelfTrainingClassifier实践

python scikit-learn SelfTrainingClassifier实践

最近用scikit-learn试了一下半监督学习，我这里分享一下我写的代码： 我的数据集的地址展示：

['./FeatureLearningRotNet/feat/train/disgust/Training_73540712.jpg.npy', './FeatureLearningRotNet/feat/train/disgust/Training_82025016.jpg.npy', './FeatureLearningRotNet/feat/train/disgust/Training_98173595.jpg.npy', './FeatureLearningRotNet/feat/train/disgust/Training_96698508.jpg.npy', './FeatureLearningRotNet/feat/train/disgust/Training_76134387.jpg.npy']

训练

import numpy as npfrom sklearn import datasetsfrom sklearn.semi_supervised import SelfTrainingClassifierfrom sklearn.svm import SVCimport globfrom tqdm import tqdm data_dir='./FeatureLearningRotNet/feat/train/*/*.npy'file_names=glob.glob(data_dir)classes=['happy','fear','disgust','angry','neutral','sad','surprise']def load_feat(file_name): data=np.load(file_name) d1=np.mean(data,axis=(1,2)) return d1.flatten()feats=[load_feat(item) for item in file_names]labels=[]for item in tqdm(file_names): label=item.split('/') label=label[-2] idx=classes.index(label) labels.append(idx)labels=np.array(labels)train_data=np.array(feats)rng = np.random.RandomState(42)random_unlabeled_points = rng.rand(len(feats)) < 0.3labels[random_unlabeled_points] = -1svc = SVC(probability=True, gamma="auto")self_training_model = SelfTrainingClassifier(svc)self_training_model.fit(train_data, labels)

测试

test_data_dir='./FeatureLearningRotNet/feat/test/*/*.npy'test_file_names=glob.glob(test_data_dir)test_feats=[load_feat(item) for item in test_file_names]test_labels=[classes.index(item.split('/')[-2]) for item in test_file_names]res=self_training_model.predict(test_feats)count=0for y_pred,y_true in zip(res,test_labels): if(y_pred==y_true): count+=1 accuracy=count/len(test_labels)print('Accuracy: {}%'.format(round(accuracy * 100, 2)))

参考文献

​​sklearn.semi_supervised.SelfTrainingClassifier​​

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