xg_plot.py 文件源码

def fea_plot(xg_model, feature, label, type = 'weight', max_num_features = None):
    fig, AX = plt.subplots(nrows=1, ncols=2)
    xgb.plot_importance(xg_model, xlabel=type, importance_type='weight', ax=AX[0], max_num_features=max_num_features)

    fscore = xg_model.get_score(importance_type=type)
    fscore = sorted(fscore.items(), key=itemgetter(1), reverse=True) # sort scores
    fea_index = get_fea_index(fscore, max_num_features)
    feature = feature[:, fea_index]
    dimension = len(fea_index)
    X = range(1, dimension+1)
    Yp = np.mean(feature[np.where(label==1)[0]], axis=0)
    Yn = np.mean(feature[np.where(label!=1)[0]], axis=0)
    for i in range(0, dimension):
        param = np.fmax(Yp[i], Yn[i])
        Yp[i] /= param
        Yn[i] /= param
    p1 = AX[1].bar(X, +Yp, facecolor='#ff9999', edgecolor='white')
    p2 = AX[1].bar(X, -Yn, facecolor='#9999ff', edgecolor='white')
    AX[1].legend((p1,p2), ('Malware', 'Normal'))
    AX[1].set_title('Comparison of selected features by their means')
    AX[1].set_xlabel('Feature Index')
    AX[1].set_ylabel('Mean Value')
    AX[1].set_ylim(-1.1, 1.1)
    plt.xticks(X, fea_index+1, rotation=80)
    plt.suptitle('Feature Selection results')