pybaobabdt) DT Tree Visualization 해보기

 

목차

패키지 설치

sudo apt-get install graphviz graphviz-dev
pip install pygraphviz
pip install pybaobabdt

라이브러리 로드

import pybaobabdt
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import matplotlib
from sklearn import datasets

pybaobabdt 패키지는 시각화에 대한 새로운 관점을 제공합니다.

주요 아이디어는 의미 있는 시각화를 통해 사용자가 트리를 이해하고 해석할 수 있도록 돕는 것입니다.

Decision Tree

tree 시각화

X , y = datasets.load_wine(return_X_y = True , as_frame =True)
clf = DecisionTreeClassifier(max_depth=5).fit(X,y)
features = list(X)

ax = pybaobabdt.drawTree(clf, size=10, dpi=72, features=features)
ax.get_figure().safeaturesvefig('tree.png', format='png', dpi=300, transparent=True)

colors = ["gray", "gray", "purple", "gray"]
colorMap = matplotlib.colors.ListedColormap(colors)

ax = pybaobabdt.drawTree(clf, size=10, dpi=72, features=features, colormap=colorMap)
ax.get_figure().safeaturesvefig('tree.png', format='png', dpi=300, transparent=True)

RandomForest

randomforest의 각각의 tree별로 시각화

clf = RandomForestClassifier(n_estimators=20, n_jobs=-1, random_state=0)
clf.fit(X, y)
size = (15,15)
plt.rcParams['figure.figsize'] = size
fig = plt.figure(figsize=size, dpi=300)

for idx, tree in enumerate(clf.estimators_):
    ax1 = fig.add_subplot(5, 4, idx+1)
    pybaobabdt.drawTree(tree, model=clf, size=15, dpi=300, features=features, ax=ax1)
    
fig.savefig('random-forest.png', format='png', dpi=1200, transparent=True)

 

 

 

 

 

 

 

 

https://towardsdatascience.com/visualizing-decision-trees-with-pybaobabdt-f8eb5b3d0d17 

Visualizing Decision Trees with Pybaobabdt

https://pypi.org/project/pybaobabdt/

pybaobabdt

https://ieeexplore.ieee.org/document/6102453

BaobabView: Interactive construction and analysis of decision trees