active.py 文件源码

def al_just_expert(adata, clf, thresh_random = 3):
    n = len(adata.taken_items)        # examples taken
    m = adata.mat.shape[0]          # examples available
    if m < 1: return -1
    if n < thresh_random or not adata.taken_both_classes():
        i = random.randint(0, m-1)
        return adata.query_expert_direct(i)

    # uncertainty sampling

    # undersample:
    #mat, rel = undersam(adata.taken_mat.tocsr(), adata.taken_rel)
    #clf.fit(mat, rel)

    clf.fit(adata.taken_mat, adata.taken_rel)
    pp = clf.predict_proba(adata.mat)
    uncertain = np.abs(pp[:,0] - 0.5)

    i = np.argmin(uncertain)
    j = np.argmin(pp[:,0])
    #print pp[i,0]
    return adata.query_expert_direct(i)