1.基础知识了解
2.百度飞桨实际运行
SVM鸢尾花分类20210512 - 飞桨AI Studio (baidu.com)
- 加载相关包
import numpy as np
from matplotlib import colors
from sklearn import svm
from sklearn import model_selection
import matplotlib.pyplot as plt
import matplotlib as mpl- 加载数据、切分数据集
# ======将字符串转化为整形==============
def iris_type(s):
it = {b'Iris-setosa':0, b'Iris-versicolor':1,b'Iris-virginica':2}
return it[s]
# 1 数据准备
# 1.1 加载数据
data = np.loadtxt('/home/aistudio/data/data2301/iris.data', # 数据文件路径i
dtype=float, # 数据类型
delimiter=',', # 数据分割符
converters={4:iris_type}) # 将第五列使用函数iris_type进行转换
# 1.2 数据分割
x, y = np.split(data, (4, ), axis=1) # 数据分组 第五列开始往后为y 代表纵向分割按列分割
x = x[:, :2]
x_train, x_test, y_train, y_test=model_selection.train_test_split(x, y, random_state=1, test_size=0.2)- 构建SVM分类器，训练函数
# SVM分类器构建
def classifier():
###############################################
###############################################
############# 在此处添加代码 ############
###############################################
###############################################
return clf
# 训练模型
def train(clf, x_train, y_train):
###############################################
###############################################
############# 在此处添加代码 ############
###############################################
###############################################
- 初始化分类器实例，训练模型
# 2 定义模型 SVM模型定义
clf = classifier()
# 3 训练模型
train(clf, x_train, y_train)-
展示训练结果及验证结果
# ======判断a,b是否相等计算acc的均值
def show_accuracy(a, b, tip):
acc = a.ravel() == b.ravel()
print('%s Accuracy:%.3f' %(tip, np.mean(acc)))
# 分别打印训练集和测试集的准确率 score(x_train, y_train)表示输出 x_train,y_train在模型上的准确率
def print_accuracy(clf, x_train, y_train, x_test, y_test):
print('training prediction:%.3f' %(clf.score(x_train, y_train)))
print('test data prediction:%.3f' %(clf.score(x_test, y_test)))
# 原始结果和预测结果进行对比 predict() 表示对x_train样本进行预测,返回样本类别
show_accuracy(clf.predict(x_train), y_train, 'traing data')
show_accuracy(clf.predict(x_test), y_test, 'testing data')
# 计算决策函数的值 表示x到各个分割平面的距离
print('decision_function:\n', clf.decision_function(x_train))
def draw(clf, x):
iris_feature = 'sepal length', 'sepal width', 'petal length', 'petal width'
# 开始画图
x1_min, x1_max = x[:, 0].min(), x[:, 0].max()
x2_min, x2_max = x[:, 1].min(), x[:, 1].max()
# 生成网格采样点
x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]
# 测试点
grid_test = np.stack((x1.flat, x2.flat), axis = 1)
print('grid_test:\n', grid_test)
# 输出样本到决策面的距离
z = clf.decision_function(grid_test)
print('the distance to decision plane:\n', z)
grid_hat = clf.predict(grid_test)
# 预测分类值 得到[0, 0, ..., 2, 2]
print('grid_hat:\n', grid_hat)
# 使得grid_hat 和 x1 形状一致
grid_hat = grid_hat.reshape(x1.shape)
cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'b', 'r'])
plt.pcolormesh(x1, x2, grid_hat, cmap = cm_light)
plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolor='k', s=50, cmap=cm_dark )
plt.scatter(x_test[:, 0], x_test[:, 1], s=120, facecolor='none', zorder=10 )
plt.xlabel(iris_feature[0], fontsize=20) # 注意单词的拼写label
plt.ylabel(iris_feature[1], fontsize=20)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title('Iris data classification via SVM', fontsize=30)
plt.grid()
plt.show()
# 4 模型评估
print('-------- eval ----------')
print_accuracy(clf, x_train, y_train, x_test, y_test)
# 5 模型使用
print('-------- show ----------')
draw(clf, x) 结果：
-------- eval ----------
training prediction:0.808
test data prediction:0.767
traing data Accuracy:0.808
testing data Accuracy:0.767
decision_function:
[[-0.24991711 1.2042151 2.19527349]
[-0.30144975 1.25525744 2.28694265]
[-0.24281146 2.24318221 0.99502737]
[-0.27672959 1.2395788 2.23333857]
[-0.23718563 2.21927504 1.11750062]
[ 2.24124823 -0.20327106 0.82871773]
[-0.24916991 2.25488962 0.92530871]
[ 2.2222485 0.86479883 -0.18955173]
[-0.28036071 1.24228023 2.24154874]
[-0.29229603 1.26471537 2.25517554]
[-0.28446963 1.23293167 2.25928719]
[ 2.24433312 0.82415773 -0.20653214]
[-0.28058919 2.2680431 1.18280403]
[-0.2685366 1.22653818 2.22306948]
[-0.28088362 1.23636902 2.24824728]
[-0.3051288 1.27363886 2.28725744]
[ 2.19125377 -0.19835874 1.03664074]
[ 2.25909278 0.7973515 -0.21992546]
[ 2.23082124 1.05792561 -0.23704919]
[ 0.9071986 2.20602139 -0.18401877]
[ 2.23542016 0.85310906 -0.20593739]
[ 2.17688585 -0.13662868 0.89878446]
[-0.2901959 1.13009006 2.28629999]
[-0.2849149 1.2256961 2.26370915]
[-0.29702633 1.25351358 2.277823 ]
[-0.27672959 1.2395788 2.23333857]
[-0.26773664 1.23366473 2.21155174]
[-0.18376448 1.04634559 2.17207981]
[-0.3034019 1.26567438 2.28710058]
[-0.19335707 2.1789894 1.06048442]
[ 2.26111102 0.82507149 -0.23839539]
[-0.25175432 2.24568274 1.07353366]
[-0.27612009 1.24511631 2.22395753]
[ 2.23082124 1.05792561 -0.23704919]
[ 2.2564785 0.88137735 -0.24525952]
[-0.27392297 1.22235345 2.24092419]
[ 2.27186349 0.81063773 -0.25217964]
[-0.24991711 1.2042151 2.19527349]
[-0.26570402 1.19126129 2.24029108]
[-0.27848257 1.2178274 2.2538024 ]
[-0.22451542 2.21500409 1.06585832]
[-0.27155037 1.18375822 2.2533339 ]
[-0.24054376 1.19871464 2.17582039]
[ 2.26342438 -0.22589317 0.79171647]
[-0.28058919 2.2680431 1.18280403]
[-0.27325118 1.23002938 2.23296907]
[-0.27392297 1.22235345 2.24092419]
[ 0.83829222 2.24377366 -0.21341635]
[-0.24516302 1.14882472 2.2212494 ]
[-0.23166652 2.24053482 0.92047491]
[ 2.22969047 -0.19768814 0.85619186]
[ 2.22880454 0.99577113 -0.22838164]
[ 2.27145869 -0.24964429 0.80531071]
[-0.27155037 1.18375822 2.2533339 ]
[ 2.26483527 0.94178326 -0.26172128]
[-0.26110752 2.23705292 1.1785139 ]
[-0.27982727 1.24751212 2.23370536]
[-0.22879722 1.19272468 2.14998616]
[ 2.23358198 0.83241849 -0.19030886]
[ 2.22452335 0.89510197 -0.20533704]
[-0.2457942 2.23080526 1.1192022 ]
[ 2.22880454 0.99577113 -0.22838164]
[-0.29975002 1.26103019 2.28055184]
[-0.26301911 1.22280275 2.21100325]
[-0.30016925 1.25327954 2.28493414]
[-0.2813963 1.22963701 2.2540346 ]
[-0.28697192 2.26788659 1.2256914 ]
[-0.22353839 1.09045989 2.20818498]
[-0.28117478 1.14500651 2.27402976]
[-0.18956974 2.19344513 0.97988104]
[ 2.25743255 -0.25828463 1.01583138]
[-0.2457942 2.23080526 1.1192022 ]
[ 2.17277768 1.22898718 -0.25528063]
[-0.24124254 2.24831388 0.92286901]
[-0.2849149 1.2256961 2.26370915]
[ 2.24579933 0.84272184 -0.21897044]
[-0.28890998 1.24952476 2.25968873]
[ 2.25299223 0.81668128 -0.21944995]
[ 2.26111102 0.82507149 -0.23839539]
[-0.23642368 1.10779426 2.22078495]
[-0.20799903 2.21040083 0.9835351 ]
[-0.27904302 1.20814609 2.25888125]
[ 2.23719183 0.87970197 -0.21848687]
[ 2.25804076 0.78683693 -0.20770513]
[-0.20036305 1.13877998 2.14747696]
[ 2.2575743 0.91742515 -0.25144563]
[-0.2457942 2.23080526 1.1192022 ]
[ 2.24054953 0.9647293 -0.23738931]
[-0.27392297 1.22235345 2.24092419]
[ 1.04178458 2.22068685 -0.22589065]
[ 2.26302243 0.86771692 -0.25169177]
[-0.25967114 1.18457321 2.23184401]
[ 2.27008204 0.91974964 -0.26603261]
[-0.16478644 2.17106379 0.9763103 ]
[ 2.25967478 1.03492895 -0.26153197]
[-0.24124254 2.24831388 0.92286901]
[-0.220911 2.26253025 0.78819329]
[ 2.24433312 0.82415773 -0.20653214]
[ 2.21629138 1.08000401 -0.22797453]
[ 0.94499808 2.23194749 -0.22546394]
[ 2.2787295 0.77880195 -0.25266172]
[-0.22879722 1.19272468 2.14998616]
[-0.25647454 1.21879654 2.1959717 ]
[ 2.24579933 0.84272184 -0.21897044]
[-0.27848257 1.2178274 2.2538024 ]
[-0.21088734 2.19937515 1.06319809]
[-0.28656383 2.27063398 1.2147421 ]
[-0.28535213 1.21733665 2.26763273]
[-0.2457942 2.23080526 1.1192022 ]
[ 2.18136055 0.8932065 -0.13975588]
[ 2.19696244 1.09880525 -0.21701131]
[-0.27114143 2.24778105 1.1980246 ]
[-0.26207613 1.23041878 2.19666289]
[-0.29382184 1.2442528 2.27479662]
[-0.24432781 2.23739126 1.07102463]
[-0.27256402 1.23671218 2.2235153 ]
[-0.26483213 1.20360155 2.23222183]
[-0.28211449 2.25818853 1.22483139]
[-0.27848257 1.2178274 2.2538024 ]
[ 2.22880454 0.99577113 -0.22838164]]
-------- show ----------
grid_test:
[[4.3 2. ]
[4.3 2.0120603]
[4.3 2.0241206]
...
[7.9 4.3758794]
[7.9 4.3879397]
[7.9 4.4 ]]
the distance to decision plane:
[[ 1.15418548 2.24935988 -0.26432263]
[ 1.15805875 2.2485129 -0.26434377]
[ 1.16176809 2.24764867 -0.2643649 ]
...
[-0.28260705 0.82993354 2.28954779]
[-0.28228765 0.82682418 2.28953928]
[-0.2819642 0.82383103 2.28953076]]
grid_hat:
[1. 1. 1. ... 2. 2. 2.]
