我已经解决了以下问题:
如何提取梯度提升分类器的决策规则
如何从 scikit-learn 决策树中提取决策规则?
然而以上两个并没有解决我的目的。以下是我的查询:
我需要使用gradientboostingclassifer在Python中构建一个模型,并在SAS平台中实现该模型。为此,我需要从gradientboostingclassifer中提取决策规则。
以下是我迄今为止尝试过的:
在 IRIS 数据上构建模型:
# import the most common dataset
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
from IPython.display import Image
X, y = load_iris(return_X_y=True)
# there are 150 observations and 4 features
print(X.shape) # (150, 4)
# let's build a small model = 5 trees with depth no more than 2
model = GradientBoostingClassifier(n_estimators=5, max_depth=3, learning_rate=1.0)
model.fit(X, y==2) # predict 2nd class vs rest, for simplicity
# we can access individual trees
trees = model.estimators_.ravel()
def plot_tree(clf):
dot_data = StringIO()
export_graphviz(clf, out_file=dot_data, node_ids=True,
filled=True, rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data([enter image description here][3]dot_data.getvalue())
return Image(graph.create_png())
# now we can plot the first tree
plot_tree(trees[0])
绘制图表后,我检查了第一棵树的图表源代码并使用以下代码写入文本文件:
with open("C:\\Users\XXXX\Desktop\Python\input_tree.txt", "w") as wrt:
wrt.write(export_graphviz(trees[0], out_file=None, node_ids=True,
filled=True, rounded=True,
special_characters=True))
下面是输出文件:
digraph Tree {
node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
edge [fontname=helvetica] ;
0 [label=<node #0<br/>X<SUB>3</SUB> ≤ 1.75<br/>friedman_mse = 0.222<br/>samples = 150<br/>value = 0.0>, fillcolor="#e5813955"] ;
1 [label=<node #1<br/>X<SUB>2</SUB> ≤ 4.95<br/>friedman_mse = 0.046<br/>samples = 104<br/>value = -0.285>, fillcolor="#e5813945"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label=<node #2<br/>X<SUB>3</SUB> ≤ 1.65<br/>friedman_mse = 0.01<br/>samples = 98<br/>value = -0.323>, fillcolor="#e5813943"] ;
1 -> 2 ;
3 [label=<node #3<br/>friedman_mse = 0.0<br/>samples = 97<br/>value = -1.5>, fillcolor="#e5813900"] ;
2 -> 3 ;
4 [label=<node #4<br/>friedman_mse = -0.0<br/>samples = 1<br/>value = 3.0>, fillcolor="#e58139ff"] ;
2 -> 4 ;
5 [label=<node #5<br/>X<SUB>3</SUB> ≤ 1.55<br/>friedman_mse = 0.222<br/>samples = 6<br/>value = 0.333>, fillcolor="#e5813968"] ;
1 -> 5 ;
6 [label=<node #6<br/>friedman_mse = 0.0<br/>samples = 3<br/>value = 3.0>, fillcolor="#e58139ff"] ;
5 -> 6 ;
7 [label=<node #7<br/>friedman_mse = 0.222<br/>samples = 3<br/>value = 0.0>, fillcolor="#e5813955"] ;
5 -> 7 ;
8 [label=<node #8<br/>X<SUB>2</SUB> ≤ 4.85<br/>friedman_mse = 0.021<br/>samples = 46<br/>value = 0.645>, fillcolor="#e581397a"] ;
0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
9 [label=<node #9<br/>X<SUB>1</SUB> ≤ 3.1<br/>friedman_mse = 0.222<br/>samples = 3<br/>value = 0.333>, fillcolor="#e5813968"] ;
8 -> 9 ;
10 [label=<node #10<br/>friedman_mse = 0.0<br/>samples = 2<br/>value = 3.0>, fillcolor="#e58139ff"] ;
9 -> 10 ;
11 [label=<node #11<br/>friedman_mse = -0.0<br/>samples = 1<br/>value = -1.5>, fillcolor="#e5813900"] ;
9 -> 11 ;
12 [label=<node #12<br/>friedman_mse = -0.0<br/>samples = 43<br/>value = 3.0>, fillcolor="#e58139ff"] ;
8 -> 12 ;
}
为了从输出文件中提取决策规则,我尝试将以下 python RegEX 代码转换为 SAS 代码:
import re
with open("C:\\Users\XXXX\Desktop\Python\input_tree.txt") as f:
with open("C:\\Users\XXXX\Desktop\Python\output.txt", "w") as f1:
result0 = 'value = 0;'
f1.write(result0)
for line in f:
result1 = re.sub(r'^(\d+)\s+.*<br\/>([A-Z]+)<SUB>(\d+)<\/SUB>\s+(.+?)([-\d.]+)<br\/>friedman_mse.*;$',r"if \2\3 \4 \5 then do;",line)
result2 = re.sub(r'^(\d+).*(?!SUB).*(value\s+=)\s([-\d.]+).*;$',r"\2 value + \3; end;",result1)
result3 = re.sub(r'^(\d+\s+->\s+\d+\s+);$',r'\1',result2)
result4 = re.sub(r'^digraph.+|^node.+|^edge.+','',result3)
result5 = re.sub(r'&(\w{2});',r'\1',result4)
result6 = re.sub(r'}','end;',result5)
f1.write(result6)
以下是上述代码的输出 SAS:
value = 0;
if X3 le 1.75 then do;
if X2 le 4.95 then do;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
if X3 le 1.65 then do;
1 -> 2
value = value + -1.5; end;
2 -> 3
value = value + 3.0; end;
2 -> 4
if X3 le 1.55 then do;
1 -> 5
value = value + 3.0; end;
5 -> 6
value = value + 0.0; end;
5 -> 7
if X2 le 4.85 then do;
0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
if X1 le 3.1 then do;
8 -> 9
value = value + 3.0; end;
9 -> 10
value = value + -1.5; end;
9 -> 11
value = value + 3.0; end;
8 -> 12
end;
正如您所看到的,输出文件中缺少一块,即我无法正确打开/关闭 do-end 块。为此,我需要使用节点号,但我失败了,因为我无法在这里找到任何模式。
你们中的任何人都可以帮我解决这个问题吗?
除此之外,像决策树分类器一样,我可以不提取上面第二个链接中提到的children_left、children_right、阈值吗?我已经成功提取了GBM的每棵树
trees = model.estimators_.ravel()
但我没有找到任何有用的函数可以用来提取每棵树的值和规则。如果我可以以类似于 DecisionTreeclassifier 的方式使用 grapviz 对象,请提供帮助。
OR
帮助我使用任何其他可以解决我的目的的方法。