SVM的章節已經講完，具體內容請參考：《01 SVM - 大綱》

《14 SVM - 程式碼案例一 - 鳶尾花資料SVM分類》
《15 SVM - 程式碼案例二 - 鳶尾花資料不同分類器效果比較》
《16 SVM - 程式碼案例三 - 不同SVM核函式效果比較》

常規操作：

1、標頭檔案引入SVM相關的包
2、防止中文亂碼
3、讀取資料
4、資料分割訓練集和測試集 6:4

import time
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

## 設定屬性防止中文亂碼
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False

## 讀取資料
# 'sepal length', 'sepal width', 'petal length', 'petal width'
iris_feature = u'花萼長度', u'花萼寬度', u'花瓣長度', u'花瓣寬度'
path = './datas/iris.data'  # 資料檔案路徑
data = pd.read_csv(path, header=None)
x, y = data[list(range(4))], data[4]
y = pd.Categorical(y).codes
x = x[[0, 1]]

## 資料分割
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=28, train_size=0.6)
 

資料SVM分類器構建:

svm1 = SVC(C=0.1, kernel='rbf')
svm2 = SVC(C=1, kernel='rbf')
svm3 = SVC(C=10, kernel='rbf')
svm4 = SVC(C=100, kernel='rbf')
svm5 = SVC(C=500, kernel='rbf')
svm6 = SVC(C=100000, kernel='rbf')

C越大，泛化能力越差，會出現過擬合的問題
C越小，泛化能力越好，但是容易出現欠擬合的問題

模型訓練:

t0=time.time()
svm1.fit(x_train, y_train)
t1=time.time()
svm2.fit(x_train, y_train)
t2=time.time()
svm3.fit(x_train, y_train)
t3=time.time()
svm4.fit(x_train, y_train)
t4=time.time()
svm5.fit(x_train, y_train)
t5=time.time()
svm6.fit(x_train, y_train)
t6=time.time()
 

效果評估：

svm1_score1 = accuracy_score(y_train, svm1.predict(x_train))
svm1_score2 = accuracy_score(y_test, svm1.predict(x_test))

svm2_score1 = accuracy_score(y_train, svm2.predict(x_train))
svm2_score2 = accuracy_score(y_test, svm2.predict(x_test))

svm3_score1 = accuracy_score(y_train, svm3.predict(x_train))
svm3_score2 = accuracy_score(y_test, svm3.predict(x_test))

svm4_score1 = accuracy_score(y_train, svm4.predict(x_train))
svm4_score2 = accuracy_score(y_test, svm4.predict(x_test))

svm5_score1 = accuracy_score(y_train, svm5.predict(x_train))
svm5_score2 = accuracy_score(y_test, svm5.predict(x_test))

svm6_score1 = accuracy_score(y_train, svm6.predict(x_train))
svm6_score2 = accuracy_score(y_test, svm6.predict(x_test))
 

畫圖 - 鳶尾花資料SVM分類器C值不同效果比較:

x_tmp = [0,1,2,3, 4, 5]
t_score = [t1 - t0, t2-t1, t3-t2, t4-t3, t5-t4, t6-t5]
y_score1 = [svm1_score1, svm2_score1, svm3_score1, svm4_score1, svm5_score1, svm6_score1]
y_score2 = [svm1_score2, svm2_score2, svm3_score2, svm4_score2, svm5_score2, svm6_score2]

plt.figure(facecolor='w', figsize=(12,6))

1、模型預測準確率：

plt.subplot(121)
plt.plot(x_tmp, y_score1, 'r-', lw=2, label=u'訓練集準確率')
plt.plot(x_tmp, y_score2, 'g-', lw=2, label=u'測試集準確率')
plt.xlim(-0.3, 3.3)
plt.ylim(np.min((np.min(y_score1), np.min(y_score2)))*0.9, 
    np.max((np.max(y_score1), np.max(y_score2)))*1.1)
plt.legend(loc = 'lower left')
plt.title(u'模型預測準確率', fontsize=13)
plt.xticks(x_tmp, [u'C=0.1', u'C=1', u'C=10', u'C=100', u'C=500', u'C=10000'], rotation=0)
plt.grid(b=True)

2、模型訓練耗時：

plt.subplot(122)
plt.plot(x_tmp, t_score, 'b-', lw=2, label=u'模型訓練時間')
plt.title(u'模型訓練耗時', fontsize=13)
plt.xticks(x_tmp, [u'C=0.1', u'C=1', u'C=10', u'C=100', u'C=500', u'C=10000'], rotation=0)
plt.grid(b=True)

plt.suptitle(u'鳶尾花資料SVM分類器C值不同效果比較', fontsize=16)
plt.show()

預測結果畫圖：

畫圖比較 - 鳶尾花資料SVM分類器不同C引數效果比較

N = 500
x1_min, x2_min = x.min()
x1_max, x2_max = x.max()

t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, N)
x1, x2 = np.meshgrid(t1, t2)  # 生成網格取樣點
grid_show = np.dstack((x1.flat, x2.flat))[0] # 測試點

獲取各個不同演算法的測試值:

svm1_grid_hat = svm1.predict(grid_show)
svm1_grid_hat = svm1_grid_hat.reshape(x1.shape)  # 使之與輸入的形狀相同

svm2_grid_hat = svm2.predict(grid_show)
svm2_grid_hat = svm2_grid_hat.reshape(x1.shape)  # 使之與輸入的形狀相同

svm3_grid_hat = svm3.predict(grid_show)
svm3_grid_hat = svm3_grid_hat.reshape(x1.shape)  # 使之與輸入的形狀相同

svm4_grid_hat = svm4.predict(grid_show)
svm4_grid_hat = svm4_grid_hat.reshape(x1.shape)  # 使之與輸入的形狀相同

svm5_grid_hat = svm5.predict(grid_show)
svm5_grid_hat = svm5_grid_hat.reshape(x1.shape)  # 使之與輸入的形狀相同

svm6_grid_hat = svm6.predict(grid_show)
svm6_grid_hat = svm6_grid_hat.reshape(x1.shape)  # 使之與輸入的形狀相同

畫圖:

cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
plt.figure(facecolor='w', figsize=(14,7))

1、C=0.1

plt.subplot(231)
## 區域圖
plt.pcolormesh(x1, x2, svm1_grid_hat, cmap=cm_light)
## 所以樣本點
plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 樣本
## 測試資料集
plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中測試集樣本
## lable列表
plt.xlabel(iris_feature[0], fontsize=13)
plt.ylabel(iris_feature[1], fontsize=13)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title(u'C=0.1', fontsize=15)
plt.grid(b=True, ls=':')
plt.tight_layout(pad=1.5)

2、C=1

plt.subplot(232)
## 區域圖
plt.pcolormesh(x1, x2, svm2_grid_hat, cmap=cm_light)
## 所以樣本點
plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 樣本
## 測試資料集
plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中測試集樣本
## lable列表
plt.xlabel(iris_feature[0], fontsize=13)
plt.ylabel(iris_feature[1], fontsize=13)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title(u'C=1', fontsize=15)
plt.grid(b=True, ls=':')
plt.tight_layout(pad=1.5)

3、C=10

plt.subplot(233)
## 區域圖
plt.pcolormesh(x1, x2, svm3_grid_hat, cmap=cm_light)
## 所以樣本點
plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 樣本
## 測試資料集
plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中測試集樣本
## lable列表
plt.xlabel(iris_feature[0], fontsize=13)
plt.ylabel(iris_feature[1], fontsize=13)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title(u'C=10', fontsize=15)
plt.grid(b=True, ls=':')
plt.tight_layout(pad=1.5)

4、C=100

plt.subplot(234)
## 區域圖
plt.pcolormesh(x1, x2, svm4_grid_hat, cmap=cm_light)
## 所以樣本點
plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 樣本
## 測試資料集
plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中測試集樣本
## lable列表
plt.xlabel(iris_feature[0], fontsize=13)
plt.ylabel(iris_feature[1], fontsize=13)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title(u'C=100', fontsize=15)
plt.grid(b=True, ls=':')
plt.tight_layout(pad=1.5)

5、C=500

plt.subplot(235)
## 區域圖
plt.pcolormesh(x1, x2, svm5_grid_hat, cmap=cm_light)
## 所以樣本點
plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 樣本
## 測試資料集
plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中測試集樣本
## lable列表
plt.xlabel(iris_feature[0], fontsize=13)
plt.ylabel(iris_feature[1], fontsize=13)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title(u'C=500', fontsize=15)
plt.grid(b=True, ls=':')
plt.tight_layout(pad=1.5)

6、C=10000

plt.subplot(236)
## 區域圖
plt.pcolormesh(x1, x2, svm6_grid_hat, cmap=cm_light)
## 所以樣本點
plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 樣本
## 測試資料集
plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中測試集樣本
## lable列表
plt.xlabel(iris_feature[0], fontsize=13)
plt.ylabel(iris_feature[1], fontsize=13)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title(u'C=10000', fontsize=15)
plt.grid(b=True, ls=':')
plt.tight_layout(pad=1.5)

plt.suptitle(u'鳶尾花資料SVM分類器不同C引數效果比較', fontsize=16)
plt.show()

結論：
C越大，泛化能力越差，會出現過擬合的問題
C越小，泛化能力越好，但是容易出現欠擬合的問題