11wk-040: Medical Cost / 의사결정나무의 시각화

최규빈
2023-11-16

1. 강의영상

https://youtu.be/playlist?list=PLQqh36zP38-wy5HZfyF9HIZKvAXrdBwzO&si=vTJ3EiJLr487p2DT

2. Imports

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.tree
import graphviz
#---#
import warnings
warnings.filterwarnings('ignore')

3. 데이터준비 + 적합

df_train = pd.read_csv('https://raw.githubusercontent.com/guebin/MP2023/main/posts/insurance.csv')
df_train

	age	sex	bmi	children	smoker	region	charges
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520
...	...	...	...	...	...	...	...
1333	50	male	30.970	3	no	northwest	10600.54830
1334	18	female	31.920	0	no	northeast	2205.98080
1335	18	female	36.850	0	no	southeast	1629.83350
1336	21	female	25.800	0	no	southwest	2007.94500
1337	61	female	29.070	0	yes	northwest	29141.36030

1338 rows × 7 columns

# step1 
X = pd.get_dummies(df_train.loc[:,'age':'region'])
y = df_train['charges']
# step2 
predictr = sklearn.tree.DecisionTreeRegressor(max_depth=3)
# step3 
predictr.fit(X,y)
# step4 -- pass

DecisionTreeRegressor(max_depth=3)

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4. matplotlib 기반 시각화

A. 기본시각화

sklearn.tree.plot_tree(predictr);

잘 안보임

B. max_depth 조정

sklearn.tree.plot_tree(
    predictr,
    max_depth=0
);

C. 변수이름 추가

sklearn.tree.plot_tree(
    predictr,
    max_depth=0,
    feature_names=X.columns
);

D. fig 오브젝트

- plt.gcf()를 이용하여 fig 오브젝트 추출

sklearn.tree.plot_tree(
    predictr,
    max_depth=1,
    feature_names=X.columns
);
fig = plt.gcf()

- fig.suptitle 을 이용하여 제목을 붙일 수도 있지 않을까?

fig.suptitle("title??")

Text(0.5, 0.98, 'title??')

fig

- dpi(해상도) 조정

fig.set_dpi(250)
fig

E. `matplotlib`의 ax에 그리기

fig = plt.figure()
ax = fig.subplots(2,1)
ax[0].plot(y,y,'--')
ax[0].plot(y,predictr.predict(X),'o',alpha=0.1)
sklearn.tree.plot_tree(predictr,feature_names=X.columns,ax=ax[1],max_depth=0);

5. GraphViz 를 이용

g = sklearn.tree.export_graphviz(
    predictr,
    feature_names=X.columns
)

graphviz.Source(g)

보기에 훨씬 깔끔함

- 파일로 추출하여 저장하는 방법

g = sklearn.tree.export_graphviz(
    predictr,
    feature_names=X.columns
)
graphviz.Source(g).render('tree',format="pdf")

'tree.pdf'

tree.pdf 이 생성되고 이 파일을 열면 tree의 구조를 쉽게 파악할 수 있음.

6. HW

tree.pdf 파일을 제출