英文:
How to obtain the interval limits from a decision tree with scikit-learn?
问题
你可以通过以下方式获取类似所需的分割点数组：
import numpy as np
# 你的树节点分割描述
tree_structure = "|--- feature_0 <= 0.08\n| |--- class: 0\n|--- feature_0 > 0.08\n| |--- feature_0 <= 8.50\n| | |--- feature_0 <= 1.50\n| | | |--- class: 1\n| | |--- feature_0 > 1.50\n| | | |--- class: 1\n| |--- feature_0 > 8.50\n| | |--- feature_0 <= 60.25\n| | | |--- class: 0\n| | |--- feature_0 > 60.25\n| | | |--- class: 0"
# 从树结构中提取分割点限制
limits = []
lines = tree_structure.split("\n")
for line in lines:
if "<=" in line or ">" in line:
limit = float(line.split()[-1])
limits.append(limit)
# 添加负无穷和正无穷
limits.insert(0, -np.inf)
limits.append(np.inf)
# 打印结果
print(limits)
这段代码会从你的决策树结构描述中提取出分割点的限制，并添加负无穷和正无穷作为数组的第一个和最后一个元素，得到你所需的分割点数组。
英文:
Say I am using the titanic dataset, with the variable age only:
import pandas as pd
data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')[["age", "survived"]]
data = data.replace('?', np.nan)
data = data.fillna(0)
print(data)
the result:
age survived
0 29 1
1 0.9167 1
2 2 0
3 30 0
4 25 0
... ... ...
1304 14.5 0
1305 0 0
1306 26.5 0
1307 27 0
1308 29 0
[1309 rows x 2 columns]
Now I train a decision tree to predict survival from age:
from sklearn.tree import DecisionTreeClassifier
tree_model = DecisionTreeClassifier(max_depth=3)
tree_model.fit(data['age'].to_frame(),data["survived"])
And if I print the structure of the tree:
from sklearn import tree
print(tree.export_text(tree_model))
I obtain:
|--- feature_0 <= 0.08
| |--- class: 0
|--- feature_0 > 0.08
| |--- feature_0 <= 8.50
| | |--- feature_0 <= 1.50
| | | |--- class: 1
| | |--- feature_0 > 1.50
| | | |--- class: 1
| |--- feature_0 > 8.50
| | |--- feature_0 <= 60.25
| | | |--- class: 0
| | |--- feature_0 > 60.25
| | | |--- class: 0
These means that the final division for every node is:
0-0.08 ; 0.08-1.50; 1.50-8.50 ; 8.50-60; >60
My question is, how can I capture those limits in an array that looks like this:
[-np.inf, 0.08, 1.5, 8.5, 60, np.inf]
Thank you!
答案1
得分: 3
决策分类器，在这种情况下 `tree_model` 具有名为 `tree_` 的属性，允许访问底层属性。
print(tree_model.tree_.threshold)
array([ 0.08335, -2. , 8.5 , 1.5 , -2. , -2. ,
60.25 , -2. , -2. ])
print(tree_model.tree_.feature)
array([ 0, -2, 0, 0, -2, -2, 0, -2, -2], dtype=int64)
`feature` 和 `threshold` 数组仅适用于分裂节点。因此，这些数组中的叶节点的值是任意的。
要获取特征的分割/阈值，可以使用 `feature` 数组过滤阈值。
``` python
threshold = tree_model.tree_.threshold
feature = tree_model.tree_.feature
feature_threshold = threshold[feature == 0]
thresholds = sorted(feature_threshold)
print(thresholds)
[0.08335000276565552, 1.5, 8.5, 60.25]
要使用 np.inf，您需要自行添加。
thresholds = [-np.inf] + thresholds + [np.inf]
print(thresholds)
[-inf, 0.08335000276565552, 1.5, 8.5, 60.25, inf]
参考：理解决策树结构。
<details>
<summary>英文:</summary>
The decision classifier, in this case `tree_model` has an attribute called `tree_` which allows access to low level attributes.
``` python
print(tree_model.tree_.threshold)
array([ 0.08335, -2. , 8.5 , 1.5 , -2. , -2. ,
60.25 , -2. , -2. ])
print(tree_model.tree_.feature)
array([ 0, -2, 0, 0, -2, -2, 0, -2, -2], dtype=int64)
The arrays feature and threshold only apply to split nodes. The values for leaf nodes in these arrays are therefore arbitrary.
To get the division/threshold of a feature, you can filter the threshold using the feature array.
threshold = tree_model.tree_.threshold
feature = tree_model.tree_.feature
feature_threshold = threshold[feature == 0]
thresholds = sorted(feature_threshold)
print(thresholds)
[0.08335000276565552, 1.5, 8.5, 60.25]
To have np.inf, you need to add it yourself.
thresholds = [-np.inf] + thresholds + [np.inf]
print(thresholds)
[-inf, 0.08335000276565552, 1.5, 8.5, 60.25, inf]
Reference: Understanding the decision tree structure.
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