如何从 GridSearchCV 输出可视化 XGBoost 树？

Question

我正在使用XGBRegressor 来拟合使用gridsearchcv 的模型。我想对树木进行可视化。

这是我关注的链接（如果重复）how to plot a decision tree from gridsearchcv?

xgb = XGBRegressor(learning_rate=0.02, n_estimators=600,silent=True, nthread=1)
folds = 5
grid = GridSearchCV(estimator=xgb, param_grid=params, scoring='neg_mean_squared_error', n_jobs=4, verbose=3 )
model=grid.fit(X_train, y_train)

方法一：

 dot_data = tree.export_graphviz(model.best_estimator_, out_file=None, 
        filled=True, rounded=True, feature_names=X_train.columns)
 dot_data

 Error: NotFittedError: This XGBRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

方法二：

tree.export_graphviz(best_clf, out_file='tree.dot',feature_names=X_train.columns,leaves_parallel=True)
subprocess.call(['dot', '-Tpdf', 'tree.dot', '-o' 'tree.pdf'])

同样的错误。

Answer 1

scikit-learn 的 tree.export_graphviz 在这里不起作用，因为您的 best_estimator_ 不是一棵树，而是一整棵树。

使用 XGBoost 自己的 plot_tree 和波士顿住房数据可以做到这一点：

from xgboost import XGBRegressor, plot_tree
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt

X, y = load_boston(return_X_y=True)

params = {'learning_rate':[0.1, 0.5], 'n_estimators':[5, 10]} # dummy, for demonstration only

xgb = XGBRegressor(learning_rate=0.02, n_estimators=600,silent=True, nthread=1)
grid = GridSearchCV(estimator=xgb, param_grid=params, scoring='neg_mean_squared_error', n_jobs=4)

grid.fit(X, y)

我们最好的估算器是：

grid.best_estimator_
# result (details may be different due to randomness):
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, gamma=0,
             importance_type='gain', learning_rate=0.5, max_delta_step=0,
             max_depth=3, min_child_weight=1, missing=None, n_estimators=10,
             n_jobs=1, nthread=1, objective='reg:linear', random_state=0,
             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
             silent=True, subsample=1, verbosity=1)

做完之后，利用this SO thread 的答案来绘制，比如说，树#4：

fig, ax = plt.subplots(figsize=(30, 30))
plot_tree(grid.best_estimator_, num_trees=4, ax=ax)
plt.show()

同样，对于树 #1：

fig, ax = plt.subplots(figsize=(30, 30))
plot_tree(grid.best_estimator_, num_trees=1, ax=ax)
plt.show()