active_learning.py 文件源码

def __uncertainty_sampling(self, clf, X_unlabeled):
        probs = clf.predict_proba(X_unlabeled)

        if self.strategy == 'least_confident':
            return 1 - np.amax(probs, axis=1)

        elif self.strategy == 'max_margin':
            margin = np.partition(-probs, 1, axis=1)
            return -np.abs(margin[:,0] - margin[:, 1])

        elif self.strategy == 'entropy':
            return np.apply_along_axis(entropy, 1, probs)