快速入门Python机器学习（15）

2022-09-23 20:01:14 浏览数 (1)

8.2.4 SVC分析红酒数据

代码语言：javascript复制
#SVC分析红酒数据
def SVC_for_load_wine():
    warnings.filterwarnings("ignore")
    myutil = util()
    X,y = datasets.load_wine().data,datasets.load_wine().target
    X1 = datasets.load_wine().data[:,:2]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    for kernel in ['linear','rbf','sigmoid','poly']:
       for gamma in ['scale', 'auto',0.1,0.01,0.001]:
              clf = SVC(max_iter=100000,kernel=kernel,gamma=gamma)
              clf.fit(X_train, y_train)
              title = "SVC_红酒数据,kernel=" kernel ",gamma=" str(gamma)
              myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
                        myutil.plot_learning_curve(SVC(max_iter=100000,kernel=kernel,gamma=gamma),X,y,title)
              myutil.show_pic(title)
              clf = SVC(max_iter=100000,kernel=kernel,gamma=gamma).fit(X1,y)
              myutil.draw_scatter_for_clf(X1,y,clf,title)

输出

代码语言：javascript复制
*SVC_红酒数据,kernel=linear,gamma=scale:
100.00%
SVC_红酒数据,kernel=linear,gamma=scale:
91.67%
*SVC_红酒数据,kernel=linear,gamma=auto:
100.00%
SVC_红酒数据,kernel=linear,gamma=auto:
91.67%
*SVC_红酒数据,kernel=linear,gamma=0.1:
100.00%
SVC_红酒数据,kernel=linear,gamma=0.1:
91.67%
*SVC_红酒数据,kernel=linear,gamma=0.01:
100.00%
SVC_红酒数据,kernel=linear,gamma=0.01:
91.67%
*SVC_红酒数据,kernel=linear,gamma=0.001:
100.00%
SVC_红酒数据,kernel=linear,gamma=0.001:
91.67%
SVC_红酒数据,kernel=rbf,gamma=scale:
71.13%
SVC_红酒数据,kernel=rbf,gamma=scale:
66.67%
*SVC_红酒数据,kernel=rbf,gamma=auto:
100.00%
SVC_红酒数据,kernel=rbf,gamma=auto:
50.00%
*SVC_红酒数据,kernel=rbf,gamma=0.1:
100.00%
SVC_红酒数据,kernel=rbf,gamma=0.1:
50.00%
*SVC_红酒数据,kernel=rbf,gamma=0.01:
99.30%
SVC_红酒数据,kernel=rbf,gamma=0.01:
69.44%
SVC_红酒数据,kernel=rbf,gamma=0.001:
83.80%
SVC_红酒数据,kernel=rbf,gamma=0.001:
72.22%
SVC_红酒数据,kernel=sigmoid,gamma=scale:
18.31%
SVC_红酒数据,kernel=sigmoid,gamma=scale:
22.22%
SVC_红酒数据,kernel=sigmoid,gamma=auto:
39.44%
SVC_红酒数据,kernel=sigmoid,gamma=auto:
41.67%
SVC_红酒数据,kernel=sigmoid,gamma=0.1:
39.44%
SVC_红酒数据,kernel=sigmoid,gamma=0.1:
41.67%
SVC_红酒数据,kernel=sigmoid,gamma=0.01:
39.44%
SVC_红酒数据,kernel=sigmoid,gamma=0.01:
41.67%
SVC_红酒数据,kernel=sigmoid,gamma=0.001:
39.44%
SVC_红酒数据,kernel=sigmoid,gamma=0.001:
41.67%
SVC_红酒数据,kernel=poly,gamma=scale:
65.49%
SVC_红酒数据,kernel=poly,gamma=scale:
80.56%
*SVC_红酒数据,kernel=poly,gamma=auto:
100.00%
SVC_红酒数据,kernel=poly,gamma=auto:
91.67%
*SVC_红酒数据,kernel=poly,gamma=0.1:
100.00%
SVC_红酒数据,kernel=poly,gamma=0.1:
91.67%
*SVC_红酒数据,kernel=poly,gamma=0.01:
100.00%
SVC_红酒数据,kernel=poly,gamma=0.01:
91.67%
*SVC_红酒数据,kernel=poly,gamma=0.001:
100.00%
SVC_红酒数据,kernel=poly,gamma=0.001:
91.67%

图（省略）

8.2.4 SVC分析乳腺癌数据

代码语言：javascript复制
#SVC分析乳腺癌数据
def SVC_for_load_breast_cancer():
       warnings.filterwarnings("ignore")
       myutil = util()
       X,y = datasets.load_breast_cancer().data,datasets.load_breast_cancer().target
       X1 = datasets.load_breast_cancer().data[:,:2]
       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
       for kernel in ['linear','rbf','sigmoid','poly']:
              for gamma in ['scale', 'auto',0.1,0.01,0.001]:
              clf = SVC(max_iter=100000,kernel=kernel,gamma=gamma)
              clf.fit(X_train, y_train)
              title = "SVC_乳腺癌数据,kernel=" kernel ",gamma=" str(gamma)
                         myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
              myutil.plot_learning_curve(SVC(max_iter=100000,kernel=kernel,gamma=gamma),X,y,title)
              myutil.show_pic(title)
              clf = SVC(max_iter=100000,kernel=kernel,gamma=gamma).fit(X1,y)
              myutil.draw_scatter_for_clf(X1,y,clf,title)

输出

代码语言：javascript复制
SVC_乳腺癌数据,kernel=linear,gamma=scale:
94.51%
SVC_乳腺癌数据,kernel=linear,gamma=scale:
91.23%
SVC_乳腺癌数据,kernel=linear,gamma=auto:
94.51%
SVC_乳腺癌数据,kernel=linear,gamma=auto:
91.23%
SVC_乳腺癌数据,kernel=linear,gamma=0.1:
94.51%
SVC_乳腺癌数据,kernel=linear,gamma=0.1:
91.23%
SVC_乳腺癌数据,kernel=linear,gamma=0.01:
94.51%
SVC_乳腺癌数据,kernel=linear,gamma=0.01:
91.23%
SVC_乳腺癌数据,kernel=linear,gamma=0.001:
94.51%
SVC_乳腺癌数据,kernel=linear,gamma=0.001:
91.23%
SVC_乳腺癌数据,kernel=rbf,gamma=scale:
91.87%
SVC_乳腺癌数据,kernel=rbf,gamma=scale:
91.23%
*SVC_乳腺癌数据,kernel=rbf,gamma=auto:
100.00%
SVC_乳腺癌数据,kernel=rbf,gamma=auto:
56.14%
SVC_乳腺癌数据,kernel=rbf,gamma=0.1:
100.00%
SVC_乳腺癌数据,kernel=rbf,gamma=0.1:
56.14%
*SVC_乳腺癌数据,kernel=rbf,gamma=0.01:
100.00%
SVC_乳腺癌数据,kernel=rbf,gamma=0.01:
55.26%
SVC_乳腺癌数据,kernel=rbf,gamma=0.001:
98.02%
SVC_乳腺癌数据,kernel=rbf,gamma=0.001:
89.47%
SVC_乳腺癌数据,kernel=sigmoid,gamma=scale:
48.79%
SVC_乳腺癌数据,kernel=sigmoid,gamma=scale:
38.60%
SVC_乳腺癌数据,kernel=sigmoid,gamma=auto:
64.40%
SVC_乳腺癌数据,kernel=sigmoid,gamma=auto:
56.14%
SVC_乳腺癌数据,kernel=sigmoid,gamma=0.1:
64.40%
SVC_乳腺癌数据,kernel=sigmoid,gamma=0.1:
56.14%
SVC_乳腺癌数据,kernel=sigmoid,gamma=0.01:
64.40%
SVC_乳腺癌数据,kernel=sigmoid,gamma=0.01:
56.14%
SVC_乳腺癌数据,kernel=sigmoid,gamma=0.001:
64.40%
SVC_乳腺癌数据,kernel=sigmoid,gamma=0.001:
56.14%
SVC_乳腺癌数据,kernel=poly,gamma=scale:
90.77%
SVC_乳腺癌数据,kernel=poly,gamma=scale:
91.23%
SVC_乳腺癌数据,kernel=poly,gamma=auto:
48.35%
SVC_乳腺癌数据,kernel=poly,gamma=auto:
51.75%
SVC_乳腺癌数据,kernel=poly,gamma=0.1:
35.38%
SVC_乳腺癌数据,kernel=poly,gamma=0.1:
34.21%
SVC_乳腺癌数据,kernel=poly,gamma=0.01:
76.70%
SVC_乳腺癌数据,kernel=poly,gamma=0.01:
79.82%
SVC_乳腺癌数据,kernel=poly,gamma=0.001:
40.44%
SVC_乳腺癌数据,kernel=poly,gamma=0.001:
42.11%

图（省略）

8.3 LinearSVC

8.3.1 LinearSVC类参数、属性和方法

类

class sklearn.svm.LinearSVC(penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)

参数

参数

类型

解释

C

float, default=1.0

正则化参数。正则化的强度与C成反比，必须是严格正的。

属性

属性

类型

coef_

ndarray of shape (1, n_features) if n_classes == 2 else (n_classes, n_features)分配给特征的权重（原始问题中的系数）。这仅在线性内核的情况下可用。

intercept_

ndarray of shape (1,) if n_classes == 2 else (n_classes,)coef_是从遵循liblinear的内部内存布局的raw_coef_派生的只读属性。

classes_

ndarray of shape (n_classes,)决策函数中的常数。唯一类标签。

n_iter_

int在所有类中运行的最大迭代次数

方法

decision_function(X)

计算X中样本的决策函数。

densify()

将系数矩阵转换为密集数组格式。

decision_function(X)

预测样本的置信度得分。

fit(X, y[, sample_weight])

根据给定的训练数据拟合SVM模型。

get_params([deep])

获取此估计器的参数。

predict(X)

对X中的样品进行分类。

score(X, y[, sample_weight])

返回给定测试数据和标签的平均精度。

set_params(**params)

设置此估计器的参数。

sparsify()

将系数矩阵转换为稀疏格式。

8.3.2 LinearSVC原理（分析make_blobs数据）

代码语言：javascript复制
def LinearSVC_Theory():
        myutil = util()
        X , y = make_blobs(n_samples=50,random_state=6,centers=2)
        clf = LinearSVC()
        clf.fit(X,y)
        plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.Paired,s=30)
        ax = plt.gca()
        xlim = ax.get_xlim()
        ylim = ax.get_ylim()
        xx = np.linspace(xlim[0],xlim[1],30)
        yy = np.linspace(ylim[0],ylim[1],30)
        YY , XX = np.meshgrid(yy,xx)
        xy = np.vstack([XX.ravel(),YY.ravel()]).T
        Z = clf.decision_function(xy). reshape(XX.shape)
        ax.contour(XX,YY,Z,colors='k',levels=[-1,0,1],alpha=0.5,linestyles=['--','-','--']) #绘制等高线
        title= u"LinearSVC原理"
        myutil.show_pic(title)

8.3.3 LinearSVC分析鸢尾花数据

代码语言：javascript复制
#LinearSVC分析鸢尾花数据
def LinearSVC_for_load_iris():
       warnings.filterwarnings("ignore")
       myutil = util()
       X,y = datasets.load_iris().data,datasets.load_iris().target
       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
       clf = LinearSVC()
       clf.fit(X_train, y_train)
       title = "LinearSVC_鸢尾花数据"
       myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
       myutil.plot_learning_curve(LinearSVC(),X,y,title)
       myutil.show_pic(title)

输出

代码语言：javascript复制
LinearSVC_鸢尾花数据:
95.83%
LinearSVC_鸢尾花数据:
96.67%

8.3.4 LinearSVC分析红酒数据

代码语言：javascript复制
#LinearSVC分析红酒数据
def LinearSVC_for_load_wine():
       warnings.filterwarnings("ignore")
       myutil = util()
       X,y = datasets.load_wine().data,datasets.load_wine().target
       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
       clf = LinearSVC()
       clf.fit(X_train, y_train)
       title = "LinearSVC_红酒数据"
       myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
       myutil.plot_learning_curve(LinearSVC(),X,y,title)
       myutil.show_pic(title)
                 X = datasets.load_wine().data[:,:2]
                 clf = LinearSVC().fit(X,y)
                  myutil.draw_scatter_for_clf(X,y,clf,title)

输出

代码语言：javascript复制
LinearSVC_红酒数据:
90.14%
LinearSVC_红酒数据:
86.11%

8.3.5 LinearSVC分析乳腺癌数据

代码语言：javascript复制
#LinearSVC分析乳腺癌数据
def LinearSVC_for_load_breast_cancer():
       warnings.filterwarnings("ignore")
       myutil = util()
       X,y = datasets.load_breast_cancer().data,datasets.load_breast_cancer().target
       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
       clf = LinearSVC()
       clf.fit(X_train, y_train)
       title = "LinearSVC_乳腺癌数据"
       myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
       myutil.plot_learning_curve(LinearSVC(),X,y,title)
       myutil.show_pic(title)
       X = datasets.load_breast_cancer().data[:,:2]
       clf = LinearSVC().fit(X,y)
       myutil.draw_scatter_for_clf(X,y,clf,title)

输出

代码语言：javascript复制
LinearSVC_乳腺癌数据:
93.19%
LinearSVC_乳腺癌数据:
92.11%

Linear Kernel与LinearSVC差不多，边界都是线性的；

RBF和polynomial Kernel 边界都不是线性的

kernel

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