# -*- coding: utf-8 -*-

"""

Created on Mon Aug 13 19:31:32 2018

@author: Administrator

"""

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.model_selection import cross_val_score

from sklearn.model_selection import KFold

from sklearn.model_selection import LeaveOneOut

from sklearn.model_selection import ShuffleSplit

#读取数据

filename='diabetes.csv'

names=['preg','plas','pres','skin','test','mass','pedi','age','class']

data=pd.read_csv(filename)

#将数据分为输入数据和输出数据

array=data.values

X=array[:,0:8]

y=array[:,8]

'''

划分训练集和测试集

'''

#划分测试集大小为33%

test_size=0.33

#数据随机的粒度，通过指定数据随机的粒度，可以确保每次执行程序得到相同的结果

#有助于比较两个不同的算法生成的模型的结果

seed=4

#划分训练集、测试集

X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=seed,test_size=test_size)

#构建模型

model=LogisticRegression()

#训练模型

model.fit(X_train,y_train)

#训练集进行打分

print(model.score(X_train,y_train))

#测试集打分

print(model.score(X_test,y_test))

'''

K折交叉验证

'''

#交叉验证分组数，分为10组

num_folds=10

#随机种子选择7

seed=7

#构造交叉验证

kfold=KFold(n_splits=num_folds,random_state=seed)

#交叉验证结果

result=cross_val_score(model,X,y,cv=kfold)

print('算法评估结果:%.3f%%(方差为:%.3f%%)'%(result.mean()*100,result.std()*100))

'''

弃一交叉验证分离(计算成本较高)

'''

loocv=LeaveOneOut()

result=cross_val_score(model,X,y,cv=loocv)

print('算法评估结果:%.3f%%(方差为:%.3f%%)'%(result.mean()*100,result.std()*100))

'''

重复随机分离评估数据集与训练数据集

'''

#将重复随机分离数据集和训练集重复10次

n_splits=10

#测试集比例

test_size=0.33

#随机种子

seed=7

#交叉验证

kfold=ShuffleSplit(n_splits=n_splits,test_size=test_size,random_state=seed)

#结果

result=cross_val_score(model,X,y,cv=kfold)

print('算法评估结果:%.3f%%(方差为:%.3f%%)'%(result.mean()*100,result.std()*100))