import pandas as pd
titanic=pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')

X=titanic[['pclass','age','sex']]
y=titanic['survived']


X['age'].fillna(X['age'].mean(),inplace=True)

from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=33)

from sklearn.feature_extraction import DictVectorizer
vec=DictVectorizer(sparse=False)

X_train=vec.fit_transform(X_train.to_dict(orient='record'))
#print vec.feature_names_

X_test=vec.transform(X_test.to_dict(orient='record'))

from sklearn.tree import DecisionTreeClassifier
dtc=DecisionTreeClassifier()

dtc.fit(X_train,y_train)

y_predict=dtc.predict(X_test)

from sklearn.metrics import classification_report

print dtc.score(X_test,y_test)
print classification_report(y_predict,y_test,target_names=['died','survived'])


#視覺化決策樹，還差一步
from sklearn.tree import export_graphviz
with open("tree.dot", 'w') as f:
  f = export_graphviz(dtc.fit(X_train,y_train), out_file = f)