在 Jupyter Notebook 中可视化决策树

Posted 2023-03-12

【中文标题】在 Jupyter Notebook 中可视化决策树

【英文标题】：Visualizing a Decision Tree in Jupyter Notebook

【发布时间】：2020-12-12 09:37:13

【问题描述】：

有没有办法在 Jupyter Notebook 上“分解”以下树？它是一个简单的决策树，但我不知道是什么让它看起来崩溃了。以下是相关代码 sn-ps 和树本身。

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (10, 8)

import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import collections
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score


#some more code


# Some feature values are present in train and absent in test and vice-versa.
y = df_train['Should_Vacation_there']
df_train, df_test = intersect_features(train=df_train, test=df_test)
df_train

#training a decision tree
dt = DecisionTreeClassifier(criterion='entropy', random_state=17)
dt.fit(df_train, y);

#displaying the tree
plot_tree(dt, feature_names=df_train.columns, filled=True,
         class_names=["Should go there", "Shouldn't go there"]);

【问题讨论】：

检查您绘制图形的轴的大小。您可以增加matplotlib.pyplot.figure 中的figsize 参数以增加轴的大小。类似plt.figure(figsize=(10, 5))。

一种方法是使用 Graphviz。查看here。

@NikhilKumar 这可能无法完全解决问题。 Scikit-learn 对决策树的可视化效果很差。

【参考方案1】：

#%config InlineBackend.figure_format = 'retina' 是这里的罪魁祸首。评论它会生成一个格式良好的树。

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import collections
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

%matplotlib inline
#%config InlineBackend.figure_format = 'retina'

iris = load_iris()
plt.rcParams['figure.figsize'] = (10, 8)

#some more code
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)

# Some feature values are present in train and absent in test and vice-versa.
#y = df_train['Should_Vacation_there']
#df_train, df_test = intersect_features(train=df_train, test=df_test)
#df_train

#training a decision tree
dt = DecisionTreeClassifier(criterion='entropy', random_state=17)
dt.fit(X_train, y_train)

#displaying the tree
plot_tree(dt, feature_names=iris.feature_names, filled=True,
         class_names=iris.target_names);

【参考方案2】：

取出%config InlineBackend.figure_format = 'retina'。改用'svg'，您将获得出色的分辨率。

from matplotlib import pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier 
from sklearn import tree

# Prepare the data data
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Fit the classifier with default hyper-parameters
clf = DecisionTreeClassifier(random_state=1234)
model = clf.fit(X, y)

1：

fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(clf, 
                   feature_names=iris.feature_names,  
                   class_names=iris.target_names,
                   filled=True)

#2

fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(clf, 
                   feature_names=iris.feature_names,  
                   class_names=iris.target_names,
                   filled=True)