ML实战：手动实现逻辑回归

• 使用了sklearn自带的乳腺癌数据集

代码实现

LogisticRegression类

import numpy as np
from sklearn.preprocessing import StandardScaler

np.set_printoptions(suppress=True)

class LogisticRegression:
    def __init__(self,x,y):
        '''
        :param x: 数据集
        :param y: 数据标签
        :param theta:参数
        '''
        transfer = StandardScaler()
        x = transfer.fit_transform(x)
        self.x=x
        self.y=np.array(y).reshape(-1,1)
        self.theta=np.random.rand(len(x[0])+1,1)
        one = np.ones(len(x))
        self.x = np.c_[one, x]

    def h(self):
        #假设函数sigmoid
        z=np.matmul(self.x,self.theta)
        return 1/(1+np.exp(-z))

    def single_iter(self,alpha):
        #单次迭代函数
        theta_size = len(self.theta)
        size = len(self.x)
        res = self.h()
        res = res - self.y
        for i in range(theta_size):
            temp = self.theta[i]
            temp = temp - alpha / size * (np.matmul(self.x[:, i], res))
            self.theta[i]= temp

    def fit(self,alpha=1,iter_count=500):
        #参数拟合
        for i in range(iter_count):
            self.single_iter(alpha)

    def predict(self,x):
        #预测函数
        transfer = StandardScaler()
        x = transfer.fit_transform(x)
        one = np.ones(len(x))
        res = np.c_[one, x]
        res=np.matmul(res,self.theta)
        res=1/(1+np.exp(-res))
        for i in range(len(res)):
            if res[i]>0.5:
                res[i]=1
            else:
                res[i]=0
        return res

主函数

from sklearn import datasets
import numpy as np
from Logistic_class import LogisticRegression
import sys
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

np.set_printoptions(suppress=True)

#读取数据集
x=datasets.load_breast_cancer().data
y=datasets.load_breast_cancer().target

#分割数据集
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1,random_state=np.random.randint(0,30))
X = np.arange(1, len(y_test) + 1)

#逻辑回归模型建立
logistic=LogisticRegression(x_train,y_train)
logistic.fit(10,4000)
y_predict=logistic.predict(x_test)

#画图
plt.figure(figsize=(15,4),dpi=80)
plt.scatter(X,y_test,label='real',marker='s',color='blue')
plt.scatter(X,y_predict,label='predict',marker='x',color='red')
plt.legend(loc=[1,0])
plt.grid(True,linestyle='--',alpha=0.5)
plt.yticks(y_test[::1])
plt.xticks(X[::1])
plt.xlabel('index of tests')
plt.ylabel('target')
plt.savefig('E:\python\ml\ml by myself\Logistic_Regression\Logistic_Regression.png')

sys.exit(0)

结果