07-05 支援向量機-找C及gamma.md

07-05 支援向量機-找C及gamma

執行結果:

(1)main.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from time import time


#------------------------------------
# 訓練資料, 依序為:
# 花萼長度(sepal_length), 
# 花萼寬度(sepal_width), 
# 花瓣長度(petal_length), 
# 花瓣寬度(petal_width), 
# 品種(species)
#------------------------------------
data=np.genfromtxt('iris.csv',  dtype=None, delimiter=',')

X=[]
Y=[]

# 訓練資料範圍, 為了繪圖用
a_min=999
a_max=-999
b_min=999
b_max=-999

for (sepal_length, sepal_width, petal_length, petal_width, species) in data:
    s=species.decode('UTF-8')

    #a=sepal_length
    #b=sepal_width
    a=petal_length
    b=petal_width

    if a>a_max: a_max=a
    if a<a_min: a_min=a
    if b>b_max: b_max=b
    if b<b_min: b_min=b
	
    X.append([a, b])		
	
	# 品種共有: setosa, versicolor, virginica
    if s=='virginica':
        Y.append(0)
    else:
        Y.append(1)
				
X=np.array(X)
Y=np.array(Y)

		
#=================================
# 建立分類模型
#=================================
# 找出較好的 C 及 gamma 參數

t0 = time()
param_grid = {'C': [0.1, 0.5, 1, 5, 10, 15, 20, 100, 150, 175, 200, 250, 1000],
              'gamma': [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2, 3, 4, 5, 10], }
clf = GridSearchCV(SVC(kernel='rbf'), param_grid)
clf = clf.fit(X, Y)

print("完成時間 %0.3fs" % (time() - t0))

print("-"*30)
print("由grid search找出的最佳估計:")
print(clf.best_estimator_)

best_C=clf.best_estimator_.C
best_gamma=clf.best_estimator_.gamma

print("-"*30)
print('C=', best_C)
print('gamma=', best_gamma)

clf = svm.SVC(kernel='rbf', gamma=best_gamma, C=best_C)
clf.fit(X, Y)


# 設定圖形尺寸 
plt.figure(figsize=(12, 9))

#---------------------------
# 繪圖
#---------------------------
fig = plt.figure()


#---------------------------
# 畫出 support vectors
#---------------------------
plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=3, facecolors='y', zorder=10)
plt.axis('tight')


#------------------------------------------
# 由訓練資料的標籤畫出不同顏色區域
#------------------------------------------
x_min = a_min - 1.5
x_max = a_max + 1.5
y_min = b_min - 1.5
y_max = b_max + 1.5

XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])

Z = Z.reshape(XX.shape)
plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[-.5, 0, 0.5])

plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)


#---------------------------
# 畫出 training vectors
#---------------------------
label_0 = np.array(Y) == 0
X_0 = X[label_0]

label_1 = np.array(Y) == 1
X_1 = X[label_1]

plt.scatter(X_0[:,0], X_0[:,1], c='b', s=10)
plt.scatter(X_1[:,0], X_1[:,1], c='r', s=10)


#---------------------------
# 儲存圖檔
#---------------------------
fig.savefig('graph.png')            

(2)iris.csv

5.1,3.5,1.4,0.2,setosa
4.9,3,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3,1.4,0.1,setosa
4.3,3,1.1,0.1,setosa
5.8,4,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5,3,1.6,0.2,setosa
5,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.1,1.5,0.1,setosa
4.4,3,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5,3.3,1.4,0.2,setosa
7,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5,2,3.5,1,versicolor
5.9,3,4.2,1.5,versicolor
6,2.2,4,1,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3,4.5,1.5,versicolor
5.8,2.7,4.1,1,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3,5,1.7,versicolor
6,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1,versicolor
5.8,2.7,3.9,1.2,versicolor
6,2.7,5.1,1.6,versicolor
5.4,3,4.5,1.5,versicolor
6,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3,4.1,1.3,versicolor
5.5,2.5,4,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3,4.6,1.4,versicolor
5.8,2.6,4,1.2,versicolor
5,2.3,3.3,1,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3,5.8,2.2,virginica
7.6,3,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3,5.5,2.1,virginica
5.7,2.5,5,2,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6,2.2,5,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2,virginica
7.7,2.8,6.7,2,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6,3,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3,5.2,2.3,virginica
6.3,2.5,5,1.9,virginica
6.5,3,5.2,2,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3,5.1,1.8,virginica