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#!/usr/bin/env python
# encoding: utf-8
"""
@version:
@author:
@time: 2016/12/24 23:37
@remark:
"""
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from sklearn.ensemble import VotingClassifier
from sklearn.externals import joblib
from sklearn.metrics import accuracy_score
from sklearn import svm
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
def sub():
x_train = pd.read_csv(r'E:\kaggle\titanicv2\cleaned_data\cleaned_train_feature.csv', header=None).values
y_train = pd.read_csv(r'E:\kaggle\titanicv2\cleaned_data\train_tag.csv', header=None).values
x_test = pd.read_csv(r'E:\kaggle\titanicv2\cleaned_data\cleaned_test_feature.csv', header=None).values
#集合方法
clf1 = svm.SVC(probability=True,random_state=7)
clf2 = RandomForestClassifier(random_state=7)
clf3 = GradientBoostingClassifier(random_state=7)
voting_class = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('dt', clf3)], voting='soft',
weights=[1, 1, 1])
vote = voting_class.fit(x_train, y_train)
#保存模型
#joblib.dump(vote,r'E:\kaggle\titanic\model\vote_sub.model')
#vote = joblib.load(r'E:\kaggle\titanicv2\model\0.850746268657vote1.model')
y_test_pred = vote.predict(x_test)
pre = pd.DataFrame(y_test_pred,index=None,columns=['Survived'])
pre.to_csv(r"E:\kaggle\titanicv2\sub\pre7.csv",index=None)
df = pd.read_csv(r'E:\kaggle\titanic\data\test.csv')
dfID = df.PassengerId
sub = pd.concat([dfID,pre],axis=1)
sub.to_csv(r"E:\kaggle\titanicv2\sub\sub7.csv", index=None)
print 'ok'
if __name__ == '__main__':
sub()
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