模型可视化

system

XGBoost模型可视化

# 在环境变量中加入安装的Graphviz路径
import os
os.environ["PATH"] += os.pathsep + 'E:/Program Files (x86)/Graphviz2.38/bin'

import xgboost as xgb
from sklearn.datasets import load_iris
iris = load_iris()

xgb_clf = xgb.XGBClassifier()
xgb_clf.fit(iris.data, iris.target)
xgb.to_graphviz(xgb_clf, num_trees=1)

也可以通过Digraph对象可以将保存文件并查看

digraph = xgb.to_graphviz(xgb_clf, num_trees=1)
digraph.format = 'png'
digraph.view('./iris_xgb')

xgboost中提供了另一个api plot_tree，使用matplotlib可视化树模型。效果上没有graphviz清楚。

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10, 10))
ax = fig.subplots()
xgb.plot_tree(xgb_clf, num_trees=1, ax=ax)
plt.show()

system

LightGBM模型可视化

参考文档 https://lightgbm.readthedocs.io/en/latest/Python-API.html#plotting
lgb中，对应的可视化函数是lightgbm.create_tree_digraph。以iris数据为例，训练一个lgb分类模型并可视化

#在环境变量中加入安装的Graphviz路径

import os
os.environ["PATH"] += os.pathsep + 'E:/Program Files (x86)/Graphviz2.38/bin'

from sklearn.datasets import load_iris
import lightgbm as lgb

iris = load_iris()
lgb_clf = lgb.LGBMClassifier()
lgb_clf.fit(iris.data, iris.target)
lgb.create_tree_digraph(lgb_clf, tree_index=1)

lgb中提供了另一个api plot_tree，使用matplotlib可视化树模型。效果上没有graphviz清楚。

import matplotlib.pyplot as plt
fig2 = plt.figure(figsize=(20, 20))
ax = fig2.subplots()
lgb.plot_tree(lgb_clf, tree_index=1, ax=ax)
plt.show()

print(‘在训练过程中绘图…’)
ax = lgb.plot_metric(evals_result, metric=‘l1’)
plt.show()

print(‘画出特征重要度…’)
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()

print(‘画出第84颗树…’)
ax = lgb.plot_tree(gbm, tree_index=83, figsize=(20, 8), show_info=[‘split_gain’])
plt.show()

#print(‘用graphviz画出第84颗树…’)
#graph = lgb.create_tree_digraph(gbm, tree_index=83, name=‘Tree84’)
#graph.render(view=True)