10wk-37: 아이스크림 – 의사결정나무, max_depth

1. 강의영상

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)

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- 결과 시각화

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

- 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.
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- 결과 시각화

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

- 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 672x480 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