[STBDA2023] 10wk-037: 아이스크림 – 의사결정나무, max_depth

10wk-037: 아이스크림 – 의사결정나무, max_depth

최규빈
2023-11-10

1. 강의영상

https://youtu.be/playlist?list=PLQqh36zP38-zMB0zUn5uWT722eagWFDG3&si=vU6BMNkqwL0VM5Sx

2. Imports

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

3. Data

np.random.seed(43052)
temp = pd.read_csv('https://raw.githubusercontent.com/guebin/DV2022/master/posts/temp.csv').iloc[:,3].to_numpy()[:100]
temp.sort()
eps = np.random.randn(100)*3 # 오차
icecream_sales = 20 + temp * 2.5 + eps 
df_train = pd.DataFrame({'temp':temp,'sales':icecream_sales})
df_train

	temp	sales
0	-4.1	10.900261
1	-3.7	14.002524
2	-3.0	15.928335
3	-1.3	17.673681
4	-0.5	19.463362
...	...	...
95	12.4	54.926065
96	13.4	54.716129
97	14.7	56.194791
98	15.0	60.666163
99	15.2	61.561043

100 rows × 2 columns

plt.plot(df_train.temp,df_train.sales,'o')

4. DecisionTreeRegressor (max_depth)

A. max_depth=1

- step1~4

## step1
X = df_train[['temp']]
y = df_train['sales']
## step2
predictr = sklearn.tree.DecisionTreeRegressor(max_depth=1) 
## step3 
predictr.fit(X,y) 
## step4 -- pass 
# predictr.predict(X) 

DecisionTreeRegressor(max_depth=1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

- 결과 시각화

plt.plot(X,y,'o',alpha=0.5,label='True')
plt.plot(X,predictr.predict(X),'--.',label='Predicted')
plt.legend()

<matplotlib.legend.Legend at 0x7fc3258d6d90>

• 5.XX를 기준으로 24.aaa 값과 41.bbb 값을 예측함

- tree 시각화

sklearn.tree.plot_tree(predictr)

[Text(0.5, 0.75, 'x[0] <= 5.05\nsquared_error = 111.946\nsamples = 100\nvalue = 33.973'),
 Text(0.25, 0.25, 'squared_error = 34.94\nsamples = 45\nvalue = 24.788'),
 Text(0.75, 0.25, 'squared_error = 49.428\nsamples = 55\nvalue = 41.489')]

B. max_depth=2

- step1~4

## step1
X = df_train[['temp']]
y = df_train['sales']
## step2
predictr = sklearn.tree.DecisionTreeRegressor(max_depth=2) 
## step3 
predictr.fit(X,y) 
## step4 -- pass 
# predictr.predict(X) 

DecisionTreeRegressor(max_depth=2)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

- 결과 시각화

plt.plot(X,y,'o',alpha=0.5,label='True')
plt.plot(X,predictr.predict(X),'.--',label='Predicted')
plt.legend()

<matplotlib.legend.Legend at 0x7fc32583b940>

- tree 시각화

sklearn.tree.plot_tree(predictr)

[Text(0.5, 0.8333333333333334, 'x[0] <= 5.05\nsquared_error = 111.946\nsamples = 100\nvalue = 33.973'),
 Text(0.25, 0.5, 'x[0] <= 1.75\nsquared_error = 34.94\nsamples = 45\nvalue = 24.788'),
 Text(0.125, 0.16666666666666666, 'squared_error = 15.12\nsamples = 19\nvalue = 19.105'),
 Text(0.375, 0.16666666666666666, 'squared_error = 8.587\nsamples = 26\nvalue = 28.94'),
 Text(0.75, 0.5, 'x[0] <= 10.7\nsquared_error = 49.428\nsamples = 55\nvalue = 41.489'),
 Text(0.625, 0.16666666666666666, 'squared_error = 19.819\nsamples = 47\nvalue = 39.251'),
 Text(0.875, 0.16666666666666666, 'squared_error = 21.051\nsamples = 8\nvalue = 54.638')]

C. 애니메이션

- step1~4

## step1 
X = df_train[['temp']]
y = df_train['sales']
## step2
predictrs = [sklearn.tree.DecisionTreeRegressor(max_depth=k) for k in range(1,11)]
## step3 
for k in range(10):
    predictrs[k].fit(X,y)
## step4 -- pass

- 애니메이션

fig = plt.figure()

<Figure size 640x480 with 0 Axes>

def func(frame):
    ax = fig.gca()
    ax.clear()
    ax.plot(X,y,'o',alpha=0.5) 
    ax.plot(X,predictrs[frame].predict(X),'.--') 
    ax.set_title(f'max_depth={predictrs[frame].max_depth}')

ani = matplotlib.animation.FuncAnimation(
    fig,
    func,
    frames=10
)

display(IPython.display.HTML(ani.to_jshtml()))

Once Loop Reflect