%%bash
/usr/bin/yes | apt-get install graphviz
pip install --upgrade graphviz
/usr/bin/yes | pip uninstall pyparsing
pip install -Iv https://pypi.python.org/packages/source/p/pyparsing/pyparsing-1.5.7.tar.gz
pip install --upgrade pydot

!pip install pydotplus

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_iris

iris = load_iris() # Irisデータ読み込み

iris.data

iris.target

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris['data'], iris['target'],test_size=0.3, random_state=0)

iris_df = pd.DataFrame(X_train, columns=iris.feature_names)
iris_df['species'] = y_train

print('tarin data',len(iris_df))
print('0:setosa ',len(iris_df[iris_df['species']==0]))
print('1:versicolor ', len(iris_df[iris_df['species']==1]))
print('2:virginica', len(iris_df[iris_df['species']==2]))

import seaborn as sns
sns.pairplot(iris_df, hue='species')

from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=3) 
clf = clf.fit(X_train, y_train) 

from sklearn import metrics
predict_train = clf.predict(X_train)
ac_score = metrics.accuracy_score(y_train, predict_train)
print('score: {0:.2f}%'.format(ac_score * 100))

import pydotplus
from sklearn.externals.six import StringIO
from IPython.display import Image
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data,feature_names=iris.feature_names,class_names=iris.target_names,filled=True, rounded=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())