决策树、随机森林可视化

分享一个Python可视化工具pybaobabdt，轻松对决策树、随机森林可视化，例如，

图怎么看：每一种颜色代表一个class，link的宽度表示从一个节点流向另一个节点的items数量。

安装

pip install pybaobabdt
pip install pygraphviz

可视化决策树

import pybaobabdt
import pandas as pd
from scipy.io import arff
from sklearn.tree import DecisionTreeClassifier #导入决策树算法

data = arff.loadarff('winequality-red.arff')
df = pd.DataFrame(data[0])

y = list(df['class'])
features = list(df.columns)
features.remove('class')
X = df.loc[:, features]

clf = DecisionTreeClassifier().fit(X, y)
ax = pybaobabdt.drawTree(clf, size=10, dpi=300, features=features) #可视化主函数pybaobabdt.drawTree

可视化决策树-设置树深度

通过maxdepth限制需要渲染的树深度，对large trees特别友好，

ax = pybaobabdt.drawTree(
    clf,
    size=10,
    dpi=100,
    maxdepth=6,  #设置渲染的树的最大深度
    features=features)

可视化决策树-突出class?

通过colormap凸显感兴趣的class，

from matplotlib.colors import ListedColormap

ax = pybaobabdt.drawTree(
    clf,
    size=10,
    dpi=600,
    colormap=ListedColormap(["#01a2d9", "gray", "#d5695d",
                             "gray"]),  #通过颜色突出展示class 5和6 变量
    features=features)

可视化随机森林

import pybaobabdt
import pandas as pd
from scipy.io import arff
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier  #导入随机森林算法

data = arff.loadarff('vehicle.arff')

df = pd.DataFrame(data[0])
y = list(df['class'])
features = list(df.columns)
features.remove('class')
X = df.loc[:, features]

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,
                        maxdepth=8,
                        features=features,
                        ax=ax1)

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