8.2.4 SVC分析红酒数据
#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
*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
#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
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）
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
#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
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
#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
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
#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
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
