吴裕雄 python 机器学习——支持向量机线性分类LinearSVC模型

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets, linear_model,svm

from sklearn.model_selection import train_test_split

def load_data_classfication():

    '''

    加载用于分类问题的数据集

    '''

    # 使用 scikit-learn 自带的 iris 数据集

    iris=datasets.load_iris()

    X_train=iris.data

    y_train=iris.target

    # 分层采样拆分成训练集和测试集，测试集大小为原始数据集大小的 1/4

    return train_test_split(X_train, y_train,test_size=0.25,random_state=0,stratify=y_train)

#支持向量机线性分类LinearSVC模型

def test_LinearSVC(*data):

    X_train,X_test,y_train,y_test=data

    cls=svm.LinearSVC()

    cls.fit(X_train,y_train)

    print('Coefficients:%s, intercept %s'%(cls.coef_,cls.intercept_))

    print('Score: %.2f' % cls.score(X_test, y_test))

# 生成用于分类的数据集

X_train,X_test,y_train,y_test=load_data_classfication()

# 调用 test_LinearSVC

test_LinearSVC(X_train,X_test,y_train,y_test) 

def test_LinearSVC_loss(*data):

    '''

    测试 LinearSVC 的预测性能随损失函数的影响

    '''

    X_train,X_test,y_train,y_test=data

    losses=['hinge','squared_hinge']

    for loss in losses:

        cls=svm.LinearSVC(loss=loss)

        cls.fit(X_train,y_train)

        print("Loss:%s"%loss)

        print('Coefficients:%s, intercept %s'%(cls.coef_,cls.intercept_))

        print('Score: %.2f' % cls.score(X_test, y_test))

# 调用 test_LinearSVC_loss

test_LinearSVC_loss(X_train,X_test,y_train,y_test) 

def test_LinearSVC_L12(*data):

    '''

    测试 LinearSVC 的预测性能随正则化形式的影响

    '''

    X_train,X_test,y_train,y_test=data

    L12=['l1','l2']

    for p in L12:

        cls=svm.LinearSVC(penalty=p,dual=False)

        cls.fit(X_train,y_train)

        print("penalty:%s"%p)

        print('Coefficients:%s, intercept %s'%(cls.coef_,cls.intercept_))

        print('Score: %.2f' % cls.score(X_test, y_test))

# 调用 test_LinearSVC_L12

test_LinearSVC_L12(X_train,X_test,y_train,y_test) 

def test_LinearSVC_C(*data):

    '''

    测试 LinearSVC 的预测性能随参数 C 的影响

    '''

    X_train,X_test,y_train,y_test=data

    Cs=np.logspace(-2,1)

    train_scores=[]

    test_scores=[]

    for C in Cs:

        cls=svm.LinearSVC(C=C)

        cls.fit(X_train,y_train)

        train_scores.append(cls.score(X_train,y_train))

        test_scores.append(cls.score(X_test,y_test))

    ## 绘图

    fig=plt.figure()

    ax=fig.add_subplot(1,1,1)

    ax.plot(Cs,train_scores,label="Traing score")

    ax.plot(Cs,test_scores,label="Testing score")

    ax.set_xlabel(r"C")

    ax.set_ylabel(r"score")

    ax.set_xscale('log')

    ax.set_title("LinearSVC")

    ax.legend(loc='best')

    plt.show()

# 调用 test_LinearSVC_C

test_LinearSVC_C(X_train,X_test,y_train,y_test)