如何在 python /R 中访问 xgboost 模型的各个树

Question

如何在python/R中访问xgboost模型的单个树？

下面我从随机森林树从sklearn

.

estimator = RandomForestRegressor(
    oob_score=True, 
    n_estimators=10, 
    max_features='auto'
) 
estimator.fit(tarning_data,traning_target) 
tree1 = estimator.estimators_[0]
leftChild = tree1.tree_.children_left  
rightChild = tree1.tree_.children_right 

Answer 1

你想检查树木吗？

在 Python 中，您可以将树转储为字符串列表：

m = xgb.XGBClassifier(max_depth=2, n_estimators=3).fit(X, y)
m.get_booster().get_dump()

>

['0:[sincelastrun<23.2917] yes=1,no=2,missing=2nt1:[sincelastrun<18.0417] yes=3,no=4,missing=4ntt3:leaf=-0.0965415ntt4:leaf=-0.0679503nt2:[sincelastrun<695.025] yes=5,no=6,missing=6ntt5:leaf=-0.0992546ntt6:leaf=-0.0984374n',
 '0:[sincelastrun<23.2917] yes=1,no=2,missing=2nt1:[sincelastrun<16.8917] yes=3,no=4,missing=4ntt3:leaf=-0.0928132ntt4:leaf=-0.0676056nt2:[sincelastrun<695.025] yes=5,no=6,missing=6ntt5:leaf=-0.0945284ntt6:leaf=-0.0937463n',
 '0:[sincelastrun<23.2917] yes=1,no=2,missing=2nt1:[sincelastrun<18.175] yes=3,no=4,missing=4ntt3:leaf=-0.0878571ntt4:leaf=-0.0610089nt2:[sincelastrun<695.025] yes=5,no=6,missing=6ntt5:leaf=-0.0904395ntt6:leaf=-0.0896808n']

或者将它们转储到一个文件中（格式很好）：

m.get_booster().dump_model("out.txt")

>

booster[0]:
0:[sincelastrun<23.2917] yes=1,no=2,missing=2
    1:[sincelastrun<18.0417] yes=3,no=4,missing=4
        3:leaf=-0.0965415
        4:leaf=-0.0679503
    2:[sincelastrun<695.025] yes=5,no=6,missing=6
        5:leaf=-0.0992546
        6:leaf=-0.0984374
booster[1]:
0:[sincelastrun<23.2917] yes=1,no=2,missing=2
    1:[sincelastrun<16.8917] yes=3,no=4,missing=4
        3:leaf=-0.0928132
        4:leaf=-0.0676056
    2:[sincelastrun<695.025] yes=5,no=6,missing=6
        5:leaf=-0.0945284
        6:leaf=-0.0937463
booster[2]:
0:[sincelastrun<23.2917] yes=1,no=2,missing=2
    1:[sincelastrun<18.175] yes=3,no=4,missing=4
        3:leaf=-0.0878571
        4:leaf=-0.0610089
    2:[sincelastrun<695.025] yes=5,no=6,missing=6
        5:leaf=-0.0904395
        6:leaf=-0.0896808