sklearn调用SVM算法

1、支撑向量机SVM是一种非常重要和广泛的机器学习算法，它的算法出发点是尽可能找到最优的决策边界，使得模型的泛化能力尽可能地好，因此SVM对未来数据的预测也是更加准确的。

2、SVM既可以解决分类问题，又可以解决回归问题，原理整体相似，不过也稍有不同。

在sklearn章调用SVM算法的代码实现如下所示：

#（一）sklearn中利用SVM算法解决分类问题

import numpy as npimport matplotlib.pyplot as pltfrom sklearn import  datasetsd=datasets.load_iris()x=d.datay=d.targetx=x[y<2,:2]y=y[y<2]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()#进行数据据标准化处理（线性方式）from sklearn.preprocessing import StandardScalers1=StandardScaler()s1.fit(x)x_standard=s1.transform(x)print(np.hstack([x,x_standard]))#导入sklearn中SVM的线性分类算法LinearSVCfrom sklearn.svm import LinearSVCs11=LinearSVC(C=1e9) #多分类问题的实现需要提交参数penalty=l1/l2（正则化方式）以及multi_class=ovo/ovr（采用何种方式多分类训练）s11.fit(x_standard,y)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)plot_decision_boundary(s11,axis=([-3,3,-3,3]))plt.scatter(x_standard[y==0,0],x_standard[y==0,1],color="r")plt.scatter(x_standard[y==1,0],x_standard[y==1,1],color="g")plt.show()print(s11.coef_)print(s11.intercept_)#输出svc函数的决策边界def plot_svc_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)    w=model.coef_[0]    b=model.intercept_[0]    x1=np.linspace(axis[0],axis[1],200)    upy=-w[0]*x1/w[1]-b/w[1]+1/w[1]    downy=-w[0]*x1/w[1]-b/w[1]-1/w[1]    upindex=((upy>axis[2])&(upy<axis[3]))    downindex = ((downy > axis[2]) & (downy < axis[3]))    plt.plot(x1[upindex],upy[upindex],"r")    plt.plot(x1[downindex],downy[downindex],"g")plot_svc_decision_boundary(s11,axis=([-3,3,-3,3]))plt.scatter(x_standard[y==0,0],x_standard[y==0,1],color="r")plt.scatter(x_standard[y==1,0],x_standard[y==1,1],color="g")plt.show()

#sklearn中对于非线性数据的svm应用（多项式应用方式）#1利用管道pipeline来进行多项式核函数的SVM算法import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasetsx,y=datasets.make_moons(noise=0.05,random_state=666)  #生成数据默认为100个数据样本print(x.shape)print(y.shape)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.preprocessing import PolynomialFeaturesfrom sklearn.preprocessing import StandardScalerfrom sklearn.svm import LinearSVCfrom sklearn.pipeline import Pipelinedef polyniomailSVC(degree,C=1.0):    return Pipeline([("poly",PolynomialFeatures(degree=degree)),                      ("std_scaler",StandardScaler()),                      ("LinearSVC",LinearSVC(C=C))                    ])

p=polyniomailSVC(degree=3)p.fit(x,y)plot_decision_boundary(p,axis=([-1,2.5,-1,1.5]))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()#2直接利用sklearn中自带的多项式核函数SVM算法，主要的参数kernel="poly"from sklearn.svm import SVCdef polynomialkernelSVC(degree,C=1.0):    return Pipeline(        [            ("std_canler",StandardScaler()),            ("kernelsvc",SVC(kernel="poly",degree=degree,C=C))        ]    )p1=polynomialkernelSVC(degree=3)p1.fit(x,y)plot_decision_boundary(p1,axis=([-1,2.5,-1,1.5]))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()#直观理解高斯核函数import  numpy as npimport matplotlib.pyplot as pltx=np.arange(-4,5,1)y=np.array((x>=-2)&(x<=2),dtype="int")print(x)print(y)plt.figure()plt.scatter(x[y==0],[0]*len(x[y==0]),color="r")plt.scatter(x[y==1],[0]*len(x[y==1]),color="g")plt.show()def gauss(x,y):    gamma=1    return np.exp(-gamma*(x-y)**2)l1,l2=-1,1x_new=np.empty((len(x),2))for i ,data in enumerate(x):    x_new[i,0]=gauss(data,l1)    x_new[i,1]=gauss(data,l2)plt.scatter(x_new[y==0,0],x_new[y==0,1],color="r")plt.scatter(x_new[y==1,0],x_new[y==1,1],color="g")plt.show()#调用sklearn中的高斯核函数RBF核（超参数主要是gamma）import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasetsx,y=datasets.make_moons(noise=0.1,random_state=666)  #生成数据默认为100个数据样本print(x.shape)print(y.shape)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_splitx_train,x_test,y_train,y_test=train_test_split(x,y,random_state=666)from sklearn.preprocessing import StandardScalerfrom sklearn.svm import SVCfrom sklearn.pipeline import Pipelinedef RBFkernelSVC(gamma):    return Pipeline([        ("std",StandardScaler()),        ("svc",SVC(kernel="rbf",gamma=gamma))    ])sv=RBFkernelSVC(gamma=1)sv.fit(x_train,y_train)plot_decision_boundary(sv,axis=([-1.5,2.5,-1,1.5]))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()print(sv.score(x_test,y_test))from sklearn import datasetsd=datasets.load_iris()x=d.datay=d.targetfrom sklearn.model_selection import train_test_splitx_train,x_test,y_train,y_test=train_test_split(x,y,random_state=666)sv=RBFkernelSVC(gamma=10)sv.fit(x_train,y_train)print(sv.score(x_test,y_test))

#（二）sklearn中利用SVM算法解决回归问题（epsilon为重要的超参数）from sklearn import datasetsd=datasets.load_boston()x=d.datay=d.targetfrom sklearn.preprocessing import StandardScalers1=StandardScaler()s1.fit(x)x=s1.transform(x)from sklearn.model_selection import train_test_splitx_train,x_test,y_train,y_test=train_test_split(x,y,random_state=666)from sklearn.svm import LinearSVRfrom sklearn.svm import SVRfrom sklearn.preprocessing import StandardScalerdef StandardLinearSVR(epsilon):    return Pipeline([        ("std",StandardScaler()),        ("svr",LinearSVR(epsilon=epsilon))    ])sv=LinearSVR()param_grid=[{    "epsilon":[i for i in np.arange(0,10,0.001)]}]from sklearn.model_selection import GridSearchCVgrid_search=GridSearchCV(sv,param_grid,n_jobs=-1,verbose=0)grid_search.fit(x_train,y_train)print(grid_search.best_params_)print(grid_search.best_score_)def polyniomailSVR(degree,C,epsilon):    return Pipeline([("poly",PolynomialFeatures(degree=degree)),                      ("std_scaler",StandardScaler()),                      ("LinearSVC",LinearSVR(C=C,epsilon=epsilon))                    ])p1=polyniomailSVR(degree=2,C=1,epsilon=0.5)p1.fit(x_train,y_train)print(p1.score(x_test,y_test))

def polynomialkernelSVR(degree,coefo,epsilon):    return Pipeline(        [            ("std_canler",StandardScaler()),            ("kernelsvc",SVR(kernel="poly",degree=degree,coef0=coefo,epsilon=epsilon))        ]    )p1=polynomialkernelSVR(degree=3,C=1,epsilon=0.1)p1.fit(x_train,y_train)print(p1.score(x_test,y_test))

def RBFkernelSVR(gamma,epsilon):    return Pipeline([        ("std",StandardScaler()),        ("svc",SVR(kernel="rbf",gamma=gamma,epsilon=epsilon))    ])p2=RBFkernelSVR(gamma=0.05,epsilon=0.1)p2.fit(x_train,y_train)print(p2.score(x_test,y_test))

运行结果如下所示：

原文地址：https://www.cnblogs.com/Yanjy-OnlyOne/p/11368253.html