学习目标：

逻辑回归——kaggle糖尿病预测实战

【实验所需数据私聊可发】

实验背景：

1、糖尿病是一组以高血糖为特征的代谢性疾病，由于胰岛素分泌缺陷或其生物作用受损则引起高血糖。长期存在的高血糖，会导致身体各种组织，特别是眼、肾、心脏、血管、神经的慢性损害和功能障碍。

2、通过2小时血浆葡萄糖浓度、2小时血清胰岛素、身体质量指数等特征来预测某个人是否罹患糖尿病，在众名的因素中，找到最能导致该病的关键特征。

实验步骤：

1.1#数据导入

import warningswarnings.filterwarnings('ignore')#忽略匹配警告data=np.loadtxt(r"D:\pima-indians-diabetes.data.csv",delimiter=",",skiprows=1,dtype=np.float)data

运行结果：

array([[ 6. , 148. , 72. , ..., 0.627, 50. , 1. ],[ 1. , 85. , 66. , ..., 0.351, 31. , 0. ],[ 8. , 183. , 64. , ..., 0.672, 32. , 1. ],...,[ 5. , 121. , 72. , ..., 0.245, 30. , 0. ],[ 1. , 126. , 60. , ..., 0.349, 47. , 1. ],[ 1. , 93. , 70. , ..., 0.315, 23. , 0. ]])

1.2#分离特征变量和分类变量

X=data[:,:-1]y=data[:,-1]

1.3#特征标准化

mu=X.mean(axis=0)std=X.std(axis=0)X=(X-mu)/std

1.4#添加全1列

#添加全1列x_ones=np.ones((X.shape[0],1))X=np.hstack((X,x_ones))

1.5#拆分数据

from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=8)

1.6#将因变量转为列向量

y_train=y_train.reshape(-1,1)y_test=y_test.reshape(-1,1)print(y_train.shape,y_test.shape)

结果：

(537, 1) (231, 1)

1.7#初始化theta值

theta=np.ones([X_train.shape[1],1])theta

结果：

array([[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.]])

1.8#设置步长值

alpha=0.001

1.9#定义sigmoid函数

def sigmoid(z):s=1.0/(1+np.exp(-z))return snum_iters=10000m=200

for i in range(num_iters):h=sigmoid(np.dot(X_train,theta))theta=theta-alpha*np.dot(X_train.T,(h-y_train))/mprint(theta)

结果为：

[[ 0.39210287][ 1.10657783][-0.24092243][ 0.0223229 ][-0.17137676][ 0.61819121][ 0.45880179][ 0.12971106][-0.84498429]]

1.10#预测

pred_y=sigmoid(np.dot(X_test,theta))

1.11#预测结果二值化

pred_y[pred_y>0.5]=1pred_y[pred_y<=0.5]=0

print(pred_y.reshape(1,-1))

结果为：

[[0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1.0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0.0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0.0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1.0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0.1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0.0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0.0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0.]]

print(y_test.reshape(1,-1))

结果为：

[[0. 1. 1. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1.0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0.0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 1.0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0.0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0.1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1.0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0.1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1.1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0.0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]]

1.12#预测准确率：

print("预测准确率为：",np.sum(pred_y==y_test)/len(y_test))

结果为：

预测准确率为： 0.7878787878787878

【sklearn 实现逻辑回归】：

2.1#导入数据

data = np.loadtxt(r"D:\pima-indians-diabetes.data.csv",deliniter = ",",skiprows = 1,dtype = np.float)

2.2#分离特征变量和分类变量

X = data[:,:-1]y = data[:,-1]

2.3#特征标准化

mu = X.mean(axis = 0)std = X.std(axis = 0)X = (X - mu) / std

2.4#拆分训练集和测试集

from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3,random_state = 8

from sklearn.linear_model import LogisticRegression#实例化logist=LogisticRegression()#模型训练logist.fit(X_train,y_train)#模型预测y_predict=logist.predict(X_test)print(y_predict)

结果为：

[0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1.0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0.0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0.0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1.0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0.0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0.0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0.]

#计算模型准确率

print("准确率:",np.sum((y_predict==y_test))/len(y_test))

结果为：

准确率: 0.7792207792207793

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