Pythonライブラリ(決定木の可視化)：dtreeviz

!pip install graphviz
!pip install dtreeviz

import numpy as np
import pandas as pd 
from sklearn import datasets
from sklearn.model_selection import train_test_split


iris = datasets.load_iris() #Irisデータを読み込む
data, target = iris.data, iris.target #データとラベルを分ける
x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=0) # 学習データとテストデータへ7:3で分割

print(x_train.dtype, x_test.dtype, y_train.dtype, y_test.dtype) #データ型の確認
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) #データ数の確認

from sklearn.tree import DecisionTreeClassifier

tree = DecisionTreeClassifier() #分類問題のモデルを作成
tree.fit(x_train, y_train) # 学習
y_pred = tree.predict(x_test) # テストデータの予測値

print(tree.get_params())
print(y_pred)
print('学習時スコア：', tree.score(x_train, y_train), '検証スコア', tree.score(x_test, y_test))

import matplotlib.pyplot as plt

x = iris.feature_names #特徴量名 ->['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']
y = tree.feature_importances_ #特徴量の重要度

print(y)
plt.barh(x, y)

[0.02150464 0.02150464 0.90006666 0.05692405]

<BarContainer object of 4 artists>

<Figure size 432x288 with 1 Axes>

import graphviz
from sklearn.tree import export_graphviz

dot = export_graphviz(tree) #決定木モデルのdot形式を取得
graph = graphviz.Source(dot) #DOT記法をレンダリング
# print(dot) #Raw-Dotが出力
graph #グラフを出力

import graphviz
from sklearn.tree import export_graphviz

dot = export_graphviz(tree, filled=True, rounded=True, 
                      class_names=['setosa', 'versicolor', 'virginica'],
                      feature_names=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'],
                      out_file=None) 

graph = graphviz.Source(dot) #DOT記法をレンダリング
graph #グラフを出力

from dtreeviz.trees import dtreeviz

viz = dtreeviz(
    tree, # 決定木モデル
    iris.data, #データ
    iris.target, #データラベル
    target_name='variety', #正解値のラベル
    feature_names=iris.feature_names, #特徴量名
    class_names=[str(i) for i in iris.target_names], #クラス名：['setosa', 'versicolor', 'virginica']
) 

# viz.view() #ブラウザ上で表示
viz

from dtreeviz.trees import dtreeviz

viz = dtreeviz(
    tree, # 決定木モデル
    iris.data, #データ
    iris.target, #データラベル
    target_name='variety', #正解値のラベル
    feature_names=iris.feature_names, #特徴量名
    class_names=[str(i) for i in iris.target_names], #クラス名：['setosa', 'versicolor', 'virginica'],
    X = [1,2,3,4] #適当なデータ
) 

# viz.view() #ブラウザ上で表示
display(viz)