#逻辑回归算法是一个二分类的算法，但是通过变形可以解决多分类的任务
#逻辑回归将数据的特征转变为数据的发生概率，然后与阈值作比较，判断是0还是1，所以也可以叫做回归算法
import numpy as np
import matplotlib.pyplot as plt
#定义概率转换函数sigmoid函数
def sigmoid(t):
return 1/(1+np.exp(-t))
x=np.linspace(-10,10,100)
y=sigmoid(x)
plt.figure()
plt.plot(x,y,"r",label="Sigmoid")
plt.legend(loc=2)
plt.show()
#定义逻辑回归算法的损失函数，与线性回归算法比较一致
#逻辑回归算法的数学原理底层实现编写
def J1(theta,x_b,y): #损失函数的定义
y_hat=sigmoid(x_b.dot(theta))
return np.sum(y*np.log(y_hat)+(1-y)*np.log(1-y_hat))/len(x_b)
def DJ2(theta,x_b,y):
res=np.empty(len(theta))
res[0]=np.sum(sigmoid(x_b.dot(theta))-y)
for i in range(1,len(theta)):
res[i]=np.sum((sigmoid(x_b.dot(theta))-y).dot(x_b[:,i]))
return res*2/len(x_b)
def DJ1(theta, x_b, y): #梯度计算公式
return x_b.T.dot(sigmoid(x_b.dot(theta))-y)/len(y)
def gradient_descent1(x_b,y,eta,theta_initial,erro=1e-8, n=1e5): #采用批量梯度下降法来进行寻找损失函数的最小值
theta=theta_initial
i=0
while i<n:
gradient = DJ1(theta,x_b,y)
last_theta = theta
theta = theta - gradient * eta
if (abs(J1(theta,x_b,y) - J1(last_theta,x_b,y)))<erro:
break
i+=1
return theta
#利用iris数据集进行原理代码的验证
from sklearn import datasets
d=datasets.load_iris()
x=d.data
y=d.target
x=x[y<2,:2]
y=y[y<2] #逻辑回归适用于二元分类数据
print(x)
print(y)
plt.figure()
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1)
x_b=np.hstack([np.ones((len(x_train),1)),x_train])
print(x_b)
theta0=np.zeros(x_b.shape[1])
eta=0.1
theta1=gradient_descent1(x_b,y_train,eta,theta0)
print(theta1)
from sklearn.metrics import accuracy_score
x_b=np.hstack([np.ones((len(x_test),1)),x_test])
y_hat=sigmoid(x_b.dot(theta1))
print(y_hat)
p=np.array(y_hat>0.5,dtype="int") #如果概率大于0.5，输出为1，不然输出为0
print(p) #输出预测的分类结果
print(y_test) #输出实际的结果
print(accuracy_score(p,y_test)) #输出预测的准确度
#二维特征的数据的决策边界，使得线性回归函数转换为概率阈值的时候其线性参数边界
def f(x):
return (-theta1[1]*x-theta1[0])/theta1[2]
x1=np.linspace(4,7.5,100)
plt.plot(x1,f(x1))
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
#定义机器学习算法的决策边界输出函数
def plot_decision_boundary(model,axis):
x0,x1=np.meshgrid(
np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)).reshape(-1,1),
np.linspace(axis[2],axis[3], int((axis[3] - axis[2]) * 100)).reshape(-1,1)
)
x_new=np.c_[x0.ravel(),x1.ravel()]
y_pre=model.predict(x_new)
zz=y_pre.reshape(x0.shape)
from matplotlib.colors import ListedColormap
cus=ListedColormap(["#EF9A9A","#FFF59D","#90CAF9"])
plt.contourf(x0,x1,zz,cmap=cus)
#使用KNN算法展示KNN分类算法的决策边界（以二维特征作为例子，便可以在平面上进行展示）
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=666)
from sklearn.neighbors import KNeighborsClassifier
knn1=KNeighborsClassifier() #默认的k比较小，模型比较复杂
knn1.fit(x_train,y_train)
plot_decision_boundary(knn1,axis=[4,8,1,5])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
knn2=KNeighborsClassifier(n_neighbors=3) #k越大，模型越简单，也意味着过拟合的程度越轻，决策边界越清晰
knn2.fit(d.data[:,:2],d.target)
x=d.data
y=d.target
plot_decision_boundary(knn2,axis=[4,8,1,5])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.scatter(x[y==2,0],x[y==2,1],color="b")
plt.show()
knn2=KNeighborsClassifier(n_neighbors=50) #k越大，模型越简单，也意味着过拟合的程度越轻，决策边界越清晰
knn2.fit(d.data[:,:2],d.target)
x=d.data
y=d.target
plot_decision_boundary(knn2,axis=[4,8,1,5])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.scatter(x[y==2,0],x[y==2,1],color="b")
plt.show()
#sklearn中调用逻辑回归算法的过程
#1-1单纯的逻辑回归算法
x=np.random.normal(0,1,size=(200,2)) #自定义数据
y=np.array(x[:,0]**2+x[:,1]**2<1.5,dtype="int")
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=666)
from sklearn.linear_model import LogisticRegression
log=LogisticRegression()
log.fit(x_train,y_train)
print(log.score(x_test,y_test))
knn3=KNeighborsClassifier()
knn3.fit(x_train,y_train)
print(knn3.score(x_test,y_test))
plot_decision_boundary(log,axis=[-4,4,-4,4])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
plot_decision_boundary(knn3,axis=[-4,4,-4,4])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
#1-2sklearn中的逻辑回归（多项式参与，并不带正则化），采用管道的方式可以训练模型
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
def polynomiallogisticregression(degree):
return Pipeline([
("poly",PolynomialFeatures(degree=degree)),
("std_reg",StandardScaler()),
("log_reg",LogisticRegression())
])
x=np.random.normal(0,1,size=(200,2))
y=np.array(x[:,0]**2+x[:,1]<1.5,dtype="int")
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=666)
p1=polynomiallogisticregression(degree=2)
p1.fit(x_train,y_train)
print(p1.score(x_train,y_train))
print(p1.score(x_test,y_test))
plot_decision_boundary(p1,axis=[-4,4,-4,4])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
p1=polynomiallogisticregression(degree=20) #当其次数变为高次时，其训练模型已经过拟合，决策边界比较复杂
p1.fit(x_train,y_train)
print(p1.score(x_train,y_train))
print(p1.score(x_test,y_test))
plot_decision_boundary(p1,axis=[-4,4,-4,4])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
#1-3逻辑回归的正则化形式函数（减小degree次数，另外需要加入正则化系数C以及正则化方式penalty，提高泛化能力）
x=np.random.normal(0,1,size=(200,2)) #自定义数据
y=np.array(x[:,0]**2+x[:,1]<1.5,dtype="int")
#随机化重置2个点的值，使得其含有一定的噪声
for _ in range(20):
y[np.random.randint(200)]=1
def Polynomiallogisticregression(degree,C,penalty): #逻辑回归的三大超参数：多项式系数，正则化系数，正则化方式L1/L2
return Pipeline([
("poly",PolynomialFeatures(degree=degree)),
("std_reg",StandardScaler()),
("log_reg",LogisticRegression(C=C,penalty=penalty))
])
p1=Polynomiallogisticregression(degree=20,C=1,penalty="l1") #当其次数变为高次时，其训练模型已经过拟合
p1.fit(x_train,y_train)
print(p1.score(x_train,y_train))
print(p1.score(x_test,y_test))
plot_decision_boundary(p1,axis=[-4,4,-4,4])
plt.scatter(x[y==0,0],x[y==0,1],color="r")
plt.scatter(x[y==1,0],x[y==1,1],color="g")
plt.show()
#多分类任务的封装OVR(n个)和OVO（Cmn个）
#sklearn中采用的逻辑回归是可以进行多分类任务的，默认采用ovr方式
from sklearn import datasets
d=datasets.load_iris()
x=d.data
y=d.target
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1)
#默认的OVR的多分类任务，时间更短，准确度较低
log1=LogisticRegression()
log1.fit(x_train,y_train)
print(log1.score(x_test,y_test))
#修改默认参数，使得其成为OVO的多分类算法，准确度更高一点，时间更长
log2=LogisticRegression(multi_class="multinomial",solver="newton-cg")
log2.fit(x_train,y_train)
print(log2.score(x_test,y_test))
#sklearn中封装的OVO和OVR
#sklearn中对于所有的二分类算法提供了统一的OVR和OVO的分类器函数，可以方便调用实现所有二分类算法的多分类实现
from sklearn.multiclass import OneVsOneClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import train_test_split
log_reg=LogisticRegression() #1-1定义一种二分类算法
ovr=OneVsRestClassifier(log_reg) #1-2进行多分类转换OVR
ovo=OneVsOneClassifier(log_reg) #1-2进行多分类转换OVO
ovr.fit(x_train,y_train) #1-3进行数据训练与预测
print(ovr.score(x_test,y_test))
ovo.fit(x_train,y_train)
print(ovo.score(x_test,y_test))
