快速入门Python机器学习（16）

2022-09-23 20:01:38 浏览数 (1)

8.4 SVR

8.4.1 SVR类参数、属性和方法

类

代码语言：javascript复制
class sklearn.svm.SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=- 1)

参数

属性

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。惩罚是l2惩罚的平方。

kernel

{'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'

指定要在算法中使用的内核类型。它必须是'linear''poly',' sigmoid','precomputed'或'callable'之一。如果没有给出，则使用'rbf'。如果给定了一个可调用函数，它将用于预计算内核矩阵。

gamma

{'scale', 'auto' or float, default='scale'

'rbf','poly'和'sigmoid'的核系数。如果gamma='scale '(默认值)被传递，那么它使用1 / (n_features * X.var())作为gamma的值，如果'auto'，则使用1 / n_features。

属性

属性

解释

class_weight_

ndarray of shape (n_classes,)

coef_

ndarray of shape (1, n_features)

dual_coef_

ndarray of shape (1, n_SV)

fit_status_

int

intercept_

ndarray of shape (1,)

n_support_

ndarray of shape (n_classes,), dtype=int32

shape_fit_

tuple of int of shape (n_dimensions_of_X,)

support_

ndarray of shape (n_SV,)

support_vectors_

ndarray of shape (n_SV, n_features)

方法

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中的样本执行回归。

score(X, y[, sample_weight])

返回预测的确定系数R2。

set_params(**params)

设置此估计器的参数。

8.4.2 分析make_regression无噪音

代码语言：javascript复制
def SVR_for_make_regression():
       myutil = util()
       X,y = make_regression(n_samples=100,n_features=1,n_informative=2,random_state=8)
       X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=8,test_size=0.3)
       clf = SVR().fit(X,y)
       title = "make_regression SVR ()回归线（无噪音）"
       myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
       myutil.draw_line(X[:,0],y,clf,title)
       myutil.plot_learning_curve(SVR(),X,y,title)
       myutil.show_pic(title)

输出

代码语言：javascript复制
make_regression LinearRegression()回归线（无噪音）:
33.56%
make_regression LinearRegression()回归线（无噪音）:
41.08%

结果非常糟糕

8.4.3 分析make_regression有噪音

代码语言：javascript复制
def SVR_for_make_regression_add_noise():
       myutil = util()
       X,y = make_regression(n_samples=100,n_features=1,n_informative=2,noise=50,random_state=8)
       X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=8,test_size=0.3)
       clf = SVR().fit(X,y)
       title = "make_regression SVR ()回归线（有噪音）"
       myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
       myutil.draw_line(X[:,0],y,clf,title)
       myutil.plot_learning_curve(SVR(),X,y,title)
       myutil.show_pic(title)

输出

代码语言：javascript复制
make_regression LinearRegression()回归线（有噪音）:
18.74%
make_regression LinearRegression()回归线（有噪音）:
18.98%

结果更加糟糕

8.4.4 SVR分析波士顿房价数据

代码语言：javascript复制
def SVR_for_boston():
       myutil = util()
       boston = datasets.load_boston()
       X,y = boston.data,boston.target
       X_train,X_test,y_train,y_test = train_test_split(X, y, random_state =8)
       for kernel in ['linear','rbf','sigmoid','poly']:
              svr = SVR(kernel=kernel)
              svr.fit(X_train,y_train)
              title = "SVR kernel=:" kernel "(预处理前)"
              myutil.print_scores(svr,X_train,y_train,X_test,y_test,title)
       scaler = StandardScaler()
       scaler.fit(X_train)
       X_train_scaler = scaler.transform(X_train)
       X_test_scaler = scaler.transform(X_test)
       for kernel in ['linear','rbf','sigmoid','poly']:
              svr = SVR(kernel=kernel)
              svr.fit(X_train_scaler,y_train)
              title = "SVR kernel=:" kernel "(预处理后)"
              myutil.print_scores(svr,X_train_scaler,y_train,X_test_scaler,y_test,title)

输出

代码语言：javascript复制
SVR kernel=:linear(预处理前):
70.88%
SVR kernel=:linear(预处理前):
69.64%
SVR kernel=:rbf(预处理前):
19.20%
SVR kernel=:rbf(预处理前):
22.23%
SVR kernel=:sigmoid(预处理前):
5.94%
SVR kernel=:sigmoid(预处理前):
7.53%
SVR kernel=:poly(预处理前):
19.50%
SVR kernel=:poly(预处理前):
20.70%
SVR kernel=:linear(预处理后):
70.56%
SVR kernel=:linear(预处理后):
69.84%
SVR kernel=:rbf(预处理后):
66.50%
SVR kernel=:rbf(预处理后):
69.46%
SVR kernel=:sigmoid(预处理后):
56.44%
SVR kernel=:sigmoid(预处理后):
63.41%
SVR kernel=:poly(预处理后):
68.60%
SVR kernel=:poly(预处理后):
62.33%

kernel

linear

rbf

sigmoid

poly

预处理前

70.88%/69.64%

19.20%/22.23%

5.94%/7.53%

19.50%/20.70%

预处理后

70.56%/69.84%

66.50%/69.46%

56.44%/63.41%

68.60%/62.33%

可见，除了linear，预处理后的得分远远高于处理前。scaler = StandardScaler()我们以前介绍过。

代码语言：javascript复制
def SVR_for_boston_for_gamma():
       myutil = util()
       boston = datasets.load_boston()
       X,y = boston.data,boston.target
       X_train,X_test,y_train,y_test = train_test_split(X, y, random_state =8)
       scaler = StandardScaler()
       scaler.fit(X_train)
       X_train_scaler = scaler.transform(X_train)
       X_test_scaler = scaler.transform(X_test)
       for kernel in ['linear','rbf','sigmoid','poly']:
                for gamma in ['scale', 'auto',0.1,0.01,0.001]:
                        svr = SVR(kernel=kernel)
                        svr.fit(X_train_scaler,y_train)
                        title = "SVR kernel=:" kernel ",gamma=" str(gamma) 
                        myutil.print_scores(svr,X_train_scaler,y_train,X_test_scaler,y_test,title)
                        #myutil.plot_learning_curve(SVR(kernel=kernel),X,y,title)
                        #myutil.show_pic(title)输出

由于画图很花时间，这里不画了

输出

代码语言：javascript复制
SVR kernel=:linear,gamma=scale:
70.56%
SVR kernel=:linear,gamma=scale:
69.84%
SVR kernel=:linear,gamma=auto:
70.56%
SVR kernel=:linear,gamma=auto:
69.84%
SVR kernel=:linear,gamma=0.1:
70.56%
SVR kernel=:linear,gamma=0.1:
69.84%
SVR kernel=:linear,gamma=0.01:
70.56%
SVR kernel=:linear,gamma=0.01:
69.84%
SVR kernel=:linear,gamma=0.001:
70.56%
SVR kernel=:linear,gamma=0.01:
69.84%
SVR kernel=:linear,gamma=0.001:
70.56%
SVR kernel=:linear,gamma=0.001:
69.84%
SVR kernel=:rbf,gamma=scale:
66.50%
SVR kernel=:rbf,gamma=scale:
69.46%
SVR kernel=:rbf,gamma=auto:
66.50%
SVR kernel=:rbf,gamma=auto:
69.46%
SVR kernel=:rbf,gamma=0.1:
64.18%
SVR kernel=:rbf,gamma=0.1:
66.96%
SVR kernel=:rbf,gamma=0.01:
56.81%
SVR kernel=:rbf,gamma=0.01:
60.37%
SVR kernel=:rbf,gamma=0.001:
22.75%
SVR kernel=:rbf,gamma=0.001:
24.48%
SVR kernel=:sigmoid,gamma=scale:
56.44%
SVR kernel=:sigmoid,gamma=scale:
63.41%
SVR kernel=:sigmoid,gamma=auto:
56.44%
SVR kernel=:sigmoid,gamma=auto:
63.41%
SVR kernel=:sigmoid,gamma=0.1:
35.92%
SVR kernel=:sigmoid,gamma=0.1:
39.85%
SVR kernel=:sigmoid,gamma=0.01:
49.05%
SVR kernel=:sigmoid,gamma=0.01:
52.26%
SVR kernel=:sigmoid,gamma=0.001:
13.84%
SVR kernel=:sigmoid,gamma=0.001:
14.82%
SVR kernel=:poly,gamma=scale:
68.60%
SVR kernel=:poly,gamma=scale:
62.33%
SVR kernel=:poly,gamma=auto:
68.60%
SVR kernel=:poly,gamma=auto:
62.33%
SVR kernel=:poly,gamma=0.1:
76.02%
SVR kernel=:poly,gamma=0.1:
63.72%
SVR kernel=:poly,gamma=0.01:
1.59%
SVR kernel=:poly,gamma=0.01:
1.03%
SVR kernel=:poly,gamma=0.001:
-2.36%
SVR kernel=:poly,gamma=0.001:
-2.66%

8.4.5 SVR分析糖尿病数据

代码语言：javascript复制
#分析糖尿病数据
def SVR_for_diabetes_for_gamma():
       myutil = util()
       diabetes = datasets.load_diabetes()
       X,y = diabetes.data,diabetes.target
       X_train,X_test,y_train,y_test = train_test_split(X, y, random_state =8)
       for kernel in ['linear','rbf','sigmoid','poly']:
              for gamma in ['scale', 'auto',0.1,0.01,0.001]:
                     svr = SVR(kernel=kernel,gamma=gamma)
                     svr.fit(X_train,y_train)
                     title = "SVR kernel=:" kernel ",gamma=" str(gamma)
                     myutil.print_scores(svr,X_train,y_train,X_test,y_test,title)

输出

代码语言：javascript复制
SVR kernel=:linear,gamma=scale:
-0.89%
SVR kernel=:linear,gamma=scale:
0.35%
SVR kernel=:linear,gamma=auto:
-0.89%
SVR kernel=:linear,gamma=auto:
0.35%
SVR kernel=:linear,gamma=0.1:
-0.89%
SVR kernel=:linear,gamma=0.1:
0.35%
SVR kernel=:linear,gamma=0.01:
-0.89%
SVR kernel=:linear,gamma=0.01:
0.35%
SVR kernel=:linear,gamma=0.001:
-0.89%
SVR kernel=:linear,gamma=0.001:
0.35%
SVR kernel=:rbf,gamma=scale:
18.30%
SVR kernel=:rbf,gamma=scale:
15.14%
SVR kernel=:rbf,gamma=auto:
-2.94%
SVR kernel=:rbf,gamma=auto:
-1.77%
SVR kernel=:rbf,gamma=0.1:
-2.94%
SVR kernel=:rbf,gamma=0.1:
-1.77%
SVR kernel=:rbf,gamma=0.01:
-3.18%
SVR kernel=:rbf,gamma=0.01:
-2.07%
SVR kernel=:rbf,gamma=0.001:
-3.20%
SVR kernel=:rbf,gamma=0.001:
-2.10%
SVR kernel=:sigmoid,gamma=scale:
37.86%
SVR kernel=:sigmoid,gamma=scale:
38.36%
SVR kernel=:sigmoid,gamma=auto:
-3.07%
SVR kernel=:sigmoid,gamma=auto:
-1.94%
SVR kernel=:sigmoid,gamma=0.1:
-3.07%
SVR kernel=:sigmoid,gamma=0.1:
-1.94%
SVR kernel=:sigmoid,gamma=0.01:
-3.19%
SVR kernel=:sigmoid,gamma=0.01:
-2.09%
SVR kernel=:sigmoid,gamma=0.001:
-3.20%
SVR kernel=:sigmoid,gamma=0.001:
-2.10%
SVR kernel=:poly,gamma=scale:
23.87%
SVR kernel=:poly,gamma=scale:
31.86%
SVR kernel=:poly,gamma=auto:
-3.20%
SVR kernel=:poly,gamma=auto:
-2.10%
SVR kernel=:poly,gamma=0.1:
-3.20%
SVR kernel=:poly,gamma=0.1:
-2.10%
SVR kernel=:poly,gamma=0.01:
-3.20%
SVR kernel=:poly,gamma=0.01:
-2.10%
SVR kernel=:poly,gamma=0.001:
-3.20%
SVR kernel=:poly,gamma=0.001:
-2.10%

分数很低，即使进行预处理，也没有用

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