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scikit-learn で決定木分析
http://pythondatascience.plavox.info/scikit-learn/scikit-learn%E3%81%A7%E6%B1%BA%E5%AE%9A%E6%9C%A8%E5%88%86%E6%9E%90/
M:\OneDrive\myprg_main\scikit\iris_ketteigi.py
# -*- coding: utf-8 -*-
#http://pythondatascience.plavox.info/scikit-learn/scikit-learn%E3%81%A7%E6%B1%BA%E5%AE%9A%E6%9C%A8%E5%88%86%E6%9E%90/
#scikit-learn で決定木分析 (CART 法)
from sklearn.datasets import load_iris
iris = load_iris()
#iris.data
#>> array([[ 5.1, 3.5, 1.4, 0.2],
# [ 4.9, 3. , 1.4, 0.2],
# [ 4.7, 3.2, 1.3, 0.2],
# [ 4.6, 3.1, 1.5, 0.2],
# [ 5. , 3.6, 1.4, 0.2],
# [ 5.4, 3.9, 1.7, 0.4],
# [ 4.6, 3.4, 1.4, 0.3],
# [ 5. , 3.4, 1.5, 0.2],
# [ 4.4, 2.9, 1.4, 0.2],
# [ 4.9, 3.1, 1.5, 0.1],
# [ 5.4, 3.7, 1.5, 0.2],
# [ 4.8, 3.4, 1.6, 0.2],
# [ 4.8, 3. , 1.4, 0.1],
# [ 4.3, 3. , 1.1, 0.1],
# [ 5.8, 4. , 1.2, 0.2],
# [ 5.7, 4.4, 1.5, 0.4],
# [ 5.4, 3.9, 1.3, 0.4],
# [ 5.1, 3.5, 1.4, 0.3],
# [ 5.7, 3.8, 1.7, 0.3],
# [ 5.1, 3.8, 1.5, 0.3],
# [ 5.4, 3.4, 1.7, 0.2],
# [ 5.1, 3.7, 1.5, 0.4],
# [ 4.6, 3.6, 1. , 0.2],
# [ 5.1, 3.3, 1.7, 0.5],
# [ 4.8, 3.4, 1.9, 0.2],
# [ 5. , 3. , 1.6, 0.2],
# [ 5. , 3.4, 1.6, 0.4],
# [ 5.2, 3.5, 1.5, 0.2],
# [ 5.2, 3.4, 1.4, 0.2],
# [ 4.7, 3.2, 1.6, 0.2],
# [ 4.8, 3.1, 1.6, 0.2],
# [ 5.4, 3.4, 1.5, 0.4],
# [ 5.2, 4.1, 1.5, 0.1],
# [ 5.5, 4.2, 1.4, 0.2],
# [ 4.9, 3.1, 1.5, 0.1],
# [ 5. , 3.2, 1.2, 0.2],
# [ 5.5, 3.5, 1.3, 0.2],
# [ 4.9, 3.1, 1.5, 0.1],
# [ 4.4, 3. , 1.3, 0.2],
# [ 5.1, 3.4, 1.5, 0.2],
# [ 5. , 3.5, 1.3, 0.3],
# [ 4.5, 2.3, 1.3, 0.3],
# [ 4.4, 3.2, 1.3, 0.2],
# [ 5. , 3.5, 1.6, 0.6],
# [ 5.1, 3.8, 1.9, 0.4],
# [ 4.8, 3. , 1.4, 0.3],
# [ 5.1, 3.8, 1.6, 0.2],
# [ 4.6, 3.2, 1.4, 0.2],
# [ 5.3, 3.7, 1.5, 0.2],
# [ 5. , 3.3, 1.4, 0.2],
# [ 7. , 3.2, 4.7, 1.4],
# [ 6.4, 3.2, 4.5, 1.5],
# [ 6.9, 3.1, 4.9, 1.5],
# [ 5.5, 2.3, 4. , 1.3],
# [ 6.5, 2.8, 4.6, 1.5],
# [ 5.7, 2.8, 4.5, 1.3],
# [ 6.3, 3.3, 4.7, 1.6],
# [ 4.9, 2.4, 3.3, 1. ],
# [ 6.6, 2.9, 4.6, 1.3],
# [ 5.2, 2.7, 3.9, 1.4],
# [ 5. , 2. , 3.5, 1. ],
# [ 5.9, 3. , 4.2, 1.5],
# [ 6. , 2.2, 4. , 1. ],
# [ 6.1, 2.9, 4.7, 1.4],
# [ 5.6, 2.9, 3.6, 1.3],
# [ 6.7, 3.1, 4.4, 1.4],
# [ 5.6, 3. , 4.5, 1.5],
# [ 5.8, 2.7, 4.1, 1. ],
# [ 6.2, 2.2, 4.5, 1.5],
# [ 5.6, 2.5, 3.9, 1.1],
# [ 5.9, 3.2, 4.8, 1.8],
# [ 6.1, 2.8, 4. , 1.3],
# [ 6.3, 2.5, 4.9, 1.5],
# [ 6.1, 2.8, 4.7, 1.2],
# [ 6.4, 2.9, 4.3, 1.3],
# [ 6.6, 3. , 4.4, 1.4],
# [ 6.8, 2.8, 4.8, 1.4],
# [ 6.7, 3. , 5. , 1.7],
# [ 6. , 2.9, 4.5, 1.5],
# [ 5.7, 2.6, 3.5, 1. ],
# [ 5.5, 2.4, 3.8, 1.1],
# [ 5.5, 2.4, 3.7, 1. ],
# [ 5.8, 2.7, 3.9, 1.2],
# [ 6. , 2.7, 5.1, 1.6],
# [ 5.4, 3. , 4.5, 1.5],
# [ 6. , 3.4, 4.5, 1.6],
# [ 6.7, 3.1, 4.7, 1.5],
# [ 6.3, 2.3, 4.4, 1.3],
# [ 5.6, 3. , 4.1, 1.3],
# [ 5.5, 2.5, 4. , 1.3],
# [ 5.5, 2.6, 4.4, 1.2],
# [ 6.1, 3. , 4.6, 1.4],
# [ 5.8, 2.6, 4. , 1.2],
# [ 5. , 2.3, 3.3, 1. ],
# [ 5.6, 2.7, 4.2, 1.3],
# [ 5.7, 3. , 4.2, 1.2],
# [ 5.7, 2.9, 4.2, 1.3],
# [ 6.2, 2.9, 4.3, 1.3],
# [ 5.1, 2.5, 3. , 1.1],
# [ 5.7, 2.8, 4.1, 1.3],
# [ 6.3, 3.3, 6. , 2.5],
# [ 5.8, 2.7, 5.1, 1.9],
# [ 7.1, 3. , 5.9, 2.1],
# [ 6.3, 2.9, 5.6, 1.8],
# [ 6.5, 3. , 5.8, 2.2],
# [ 7.6, 3. , 6.6, 2.1],
# [ 4.9, 2.5, 4.5, 1.7],
# [ 7.3, 2.9, 6.3, 1.8],
# [ 6.7, 2.5, 5.8, 1.8],
# [ 7.2, 3.6, 6.1, 2.5],
# [ 6.5, 3.2, 5.1, 2. ],
# [ 6.4, 2.7, 5.3, 1.9],
# [ 6.8, 3. , 5.5, 2.1],
# [ 5.7, 2.5, 5. , 2. ],
# [ 5.8, 2.8, 5.1, 2.4],
# [ 6.4, 3.2, 5.3, 2.3],
# [ 6.5, 3. , 5.5, 1.8],
# [ 7.7, 3.8, 6.7, 2.2],
# [ 7.7, 2.6, 6.9, 2.3],
# [ 6. , 2.2, 5. , 1.5],
# [ 6.9, 3.2, 5.7, 2.3],
# [ 5.6, 2.8, 4.9, 2. ],
# [ 7.7, 2.8, 6.7, 2. ],
# [ 6.3, 2.7, 4.9, 1.8],
# [ 6.7, 3.3, 5.7, 2.1],
# [ 7.2, 3.2, 6. , 1.8],
# [ 6.2, 2.8, 4.8, 1.8],
# [ 6.1, 3. , 4.9, 1.8],
# [ 6.4, 2.8, 5.6, 2.1],
# [ 7.2, 3. , 5.8, 1.6],
# [ 7.4, 2.8, 6.1, 1.9],
# [ 7.9, 3.8, 6.4, 2. ],
# [ 6.4, 2.8, 5.6, 2.2],
# [ 6.3, 2.8, 5.1, 1.5],
# [ 6.1, 2.6, 5.6, 1.4],
# [ 7.7, 3. , 6.1, 2.3],
# [ 6.3, 3.4, 5.6, 2.4],
# [ 6.4, 3.1, 5.5, 1.8],
# [ 6. , 3. , 4.8, 1.8],
# [ 6.9, 3.1, 5.4, 2.1],
# [ 6.7, 3.1, 5.6, 2.4],
# [ 6.9, 3.1, 5.1, 2.3],
# [ 5.8, 2.7, 5.1, 1.9],
# [ 6.8, 3.2, 5.9, 2.3],
# [ 6.7, 3.3, 5.7, 2.5],
# [ 6.7, 3. , 5.2, 2.3],
# [ 6.3, 2.5, 5. , 1.9],
# [ 6.5, 3. , 5.2, 2. ],
# [ 6.2, 3.4, 5.4, 2.3],
# [ 5.9, 3. , 5.1, 1.8]])
#>> iris.target
#array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
# 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
# 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
# 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=3)
clf = clf.fit(iris.data, iris.target)
predicted = clf.predict(iris.data)
# 作成したモデルを用いて予測を実行
#>> predicted
#>> array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
# 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
# 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,
# 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
# 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
#>> sum(predicted==iris.target) * 1.0 / len(iris.target)
#.97333333333333338
#import graphviz
#tree.dotファイルにエクスポート
tree.export_graphviz(clf, out_file="tree.dot", feature_names=iris.feature_names, class_names=iris.target_names, filled=True, rounded=True)
#エラーがでる
#Traceback (most recent call last):
# File "", line 1, in
#TypeError: export_graphviz() got an unexpected keyword argument 'class_names'
# いったんanacondaに戻りアップデート
#>>conda update scikit-learn
#dotファイルをpdfファイルに変換、出力
import pydotplus
from sklearn.externals.six import StringIO
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
#以下のように pydotでする方法もあるみたい。結果は未確認
#import pydot
#graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("graph.pdf")
#以下のエラーがでる。実行するファイル？がない
#pydotplus GraphViz's executables not found
#そのまま英語で検索、英語のサイトで対処法みつける。
#windows 用 graphvizをインストール。そのbinフォルダまで
#パスをとおす。
#すると graph.pdfができる。
#でも、私のadobu 8.12 でみると色、コーナーの角がとれていないで表示
#windowsのgraphviz2.38の gvedit.exeでみるとちゃんと表示されていた。
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