ML-SVM案例学习案例一:对鸢尾花数据进行SVM分类(附源码)

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文章目录


前言

【ML-SVM案例学习】会有十种SVM案例,供大家用来学习。本章实现SVM鸢尾花数据的分类任务。


一、完整源码分步实现

1.引入库

代码如下(示例):

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings

from sklearn import svm  # svm导入
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.exceptions import ChangedBehaviorWarning

2.读入数据

代码如下(示例):

## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
warnings.filterwarnings('ignore', category=ChangedBehaviorWarning)

## 读取数据
# 'sepal length', 'sepal width', 'petal length', 'petal width'
iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
path = './datas/iris.data'  # 数据文件路径
data = pd.read_csv(path, header=None)
data.head(5)

数据如下:

3.编码数据

x, y = data[list(range(4))], data[4]
y = pd.Categorical(y).codes  # 把文本数据进行编码,比如a b c编码为 0 1 2; 可以通过pd.Categorical(y).categories获取index对应的原始值
x = x[[0, 1]] # 获取第一列和第二列
print('经过编码后的数据y:', y[:5])

经过编码后的数据y: [0 0 0 0 0]

4.数据分割

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.8)
# svm.SVC API说明:
# 功能:使用SVM分类器进行模型构建
# 参数说明:
# C: 误差项的惩罚系数,默认为1.0;一般为大于0的一个数字,
#    C越大表示在训练过程中对于总误差的关注度越高,也就是说当C越大的时候,
#    对于训练集的表现会越好,但是有可能引发过度拟合的问题(overfiting)
# kernel
#    指定SVM内部函数的类型,可选值:linear、poly、rbf、sigmoid、precomputed(基本不用,有前提要求,要求特征属性数目和样本数目一样);默认是rbf;
# degree
#    当使用多项式函数作为svm内部的函数的时候,给定多项式的项数,默认为3
# gamma
#    当SVM内部使用poly、rbf、sigmoid的时候,核函数的系数值,当默认值为auto的时候,实际系数为1/n_features
# coef0: 
#    当核函数为poly或者sigmoid的时候,给定的独立系数,默认为0
# probability
#    是否启用概率估计,默认不启动,不太建议启动
# shrinking
#    是否开启收缩启发式计算,默认为True
# tol: 
#    模型构建收敛参数,当模型的的误差变化率小于该值的时候,结束模型构建过程,默认值:1e-3
# cache_size:
#    在模型构建过程中,缓存数据的最大内存大小,默认为空,单位MB
# class_weight:
#    给定各个类别的权重,默认为空
# max_iter:
#    最大迭代次数,默认-1表示不限制
# decision_function_shape: 
#    决策函数,可选值:ovo和ovr,默认为None;推荐使用ovr;(1.7以上版本才有)

5.数据SVM分类器构建

clf = svm.SVC(C=1, kernel='rbf',gamma=0.1)
# gamma值越大,训练集的拟合就越好,但是会造成过拟合,导致测试集拟合变差
# gamma值越小,模型的泛化能力越好,训练集和测试集的拟合相近,但是会导致训练集出现欠拟合问题,
# 从而,准确率变低,导致测试集准确率也变低。
## 模型训练
clf.fit(x_train, y_train)

输出:
SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=‘ovr’, degree=3, gamma=0.1, kernel=‘rbf’,
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)

6.计算模型的准确率/精度

print ('训练集的score:', clf.score(x_train, y_train)) 
print ('训练集准确率:', accuracy_score(y_train, clf.predict(x_train)))
print ('测试集的score:',  clf.score(x_test, y_test))
print ('测试集准确率:', accuracy_score(y_test, clf.predict(x_test)))

输出:
训练集的score: 0.85
训练集准确率: 0.85
测试集的score: 0.7333333333333333
测试集准确率: 0.7333333333333333

7.计算决策函数的结构值以及预测值

#  (decision_function计算的是样本x到各个分割平面的距离<也就是决策函数的值>)
print ('decision_function:\\n', clf.decision_function(x_train))
print ('predict:\\n', clf.predict(x_train))

decision_function:

decision_function:
 [[-0.25039727  1.0886331   2.16176417]
 [ 1.03478736  2.11650098 -0.15128834]
 [ 2.23214438  1.00598335 -0.23812773]
 [-0.19163546  2.1175139   1.07412155]
 [-0.32152579  1.14496276  2.17656303]
 [ 1.02173467  2.16988825 -0.19162293]
 [ 2.14580325  0.95677746 -0.10258071]
 [-0.23566638  2.17796366  1.05770273]
 [-0.13008471  2.12075927  1.00932543]
 [-0.19844194  2.1995431   0.99889884]
 [-0.36343522  1.08701831  2.27641692]
 [ 2.30535715  1.04393285 -0.34929   ]
 [-0.35915878  1.06384614  2.29531264]
 [ 2.29333629  0.99860275 -0.29193904]
 [ 2.21795456  0.97111601 -0.18907056]
 [ 0.92054508  2.2724345  -0.19297958]
 [-0.2997012   1.10328323  2.19641797]
 [-0.2730624   1.03890272  2.23415968]
 [-0.33839217  2.26132199  1.07707018]
 [-0.44273262  1.17653689  2.26619573]
 [-0.15877661  2.21746358  0.94131303]
 [-0.44724083  1.02472152  2.42251931]
 [-0.17202518  1.05287918  2.119146  ]
 [-0.14988387  2.23343312  0.91645074]
 [-0.31861821  1.16774019  2.15087802]
 [-0.29622421  1.14950193  2.14672228]
 [ 1.0664275   2.1904298  -0.2568573 ]
 [-0.35991183  1.20227659  2.15763525]
 [-0.35330602  1.04124945  2.31205657]
 [-0.2997012   1.10328323  2.19641797]
 [-0.05522314  2.03779287  1.01743027]
 [ 2.25203496  1.06973396 -0.32176891]
 [-0.17449621  2.18085941  0.9936368 ]
 [-0.11021164  2.18046075  0.92975089]
 [-0.05865155  2.14084287  0.91780868]
 [-0.12662311  2.21612151  0.9105016 ]
 [-0.19163546  2.1175139   1.07412155]
 [-0.38070881  1.0296007   2.35110811]
 [ 2.24957743  0.96861839 -0.21819582]
 [ 2.35477694  1.05478502 -0.40956196]
 [-0.34332437  1.16288782  2.18043655]
 [-0.06527735  2.12119172  0.94408563]
 [ 2.14185505  1.03254567 -0.17440072]
 [ 2.27389225  0.85571723 -0.12960948]
 [-0.35915878  1.06384614  2.29531264]
 [ 2.30724951  1.05732668 -0.3645762 ]
 [-0.13008471  2.12075927  1.00932543]
 [ 1.00329378  2.20214884 -0.20544262]
 [ 2.37889994  0.99914274 -0.37804268]
 [-0.38865303  2.25320429  1.13544874]
 [-0.29145938  0.96854255  2.32291684]
 [-0.09164014  2.14161983  0.95002031]
 [ 2.22623117  1.08968182 -0.31591299]
 [-0.4096892   1.06746523  2.34222397]
 [-0.33660296  1.0467762   2.28982676]
 [-0.2997012   1.10328323  2.19641797]
 [-0.32152579  1.14496276  2.17656303]
 [ 2.33278328  0.94341849 -0.27620177]
 [ 2.32663406  1.00960575 -0.33623981]
 [-0.25094655  1.06568299  2.18526357]
 [-0.2730624   1.03890272  2.23415968]
 [ 2.13304331  1.19108118 -0.32412449]
 [-0.11663626  1.03526731  2.08136896]
 [ 2.19635991  1.09554303 -0.29190293]
 [-0.19042462  2.21791314  0.97251148]
 [-0.35915878  1.06384614  2.29531264]
 [ 2.37987847  1.02502782 -0.40490629]
 [ 2.31697854  0.97865204 -0.29563057]
 [-0.42101983  1.06048387  2.36053596]
 [ 2.26321395  1.00248244 -0.26569639]
 [ 2.3322641   1.06231608 -0.39458018]
 [ 2.2645061   0.93262533 -0.19713143]
 [-0.17206568  2.24979256  0.92227312]
 [-0.31794906  1.05203355  2.2659155 ]
 [-0.44593685  1.03180134  2.41413551]
 [ 2.26321395  1.00248244 -0.26569639]
 [ 2.22247594  1.07534695 -0.29782289]
 [ 2.20680036  1.02662003 -0.23342039]
 [-0.11748127  2.16161947  0.9558618 ]
 [-0.32277435  1.09831759  2.22445676]
 [ 2.21795026  1.05994599 -0.27789625]
 [ 2.21270515  1.04364305 -0.2563482 ]
 [-0.2986835   1.12654041  2.17214309]
 [ 2.14185505  1.03254567 -0.17440072]
 [-0.5         1.07338601  2.42661399]
 [ 1.0415998   2.20742886 -0.24902865]
 [-0.30569708  0.92274296  2.38295412]
 [-0.32111039  1.07499685  2.24611354]
 [ 2.36439692  0.89257767 -0.25697458]
 [-0.1613555   2.11948124  1.04187426]
 [ 2.161655    0.92086513 -0.08252013]
 [-0.47608835  1.04954709  2.42654126]
 [ 2.33278328  0.94341849 -0.27620177]
 [ 2.30535715  1.04393285 -0.34929   ]
 [-0.47075253  1.07424442  2.39650811]
 [ 2.24367895  1.03936622 -0.28304517]
 [-0.14575094  1.03325696  2.11249398]
 [-0.11748127  2.16161947  0.9558618 ]
 [-0.17449621  2.18085941  0.9936368 ]
 [-0.16701198  2.19987473  0.96713725]
 [-0.22523374  1.06936924  2.1558645 ]
 [-0.34404723  1.09287868  2.25116855]
 [-0.35991183  1.20227659  2.15763525]
 [-0.34404723  1.09287868  2.25116855]
 [ 2.16544172  1.10090524 -0.26634696]
 [-0.14988387  2.23343312  0.91645074]
 [-0.32111039  1.07499685  2.24611354]
 [-0.17449621  2.18085941  0.9936368 ]
 [ 2.23827935  1.02296045 -0.2612398 ]
 [-0.34541291  1.11637043  2.22904248]
 [ 0.96788879  2.12033521 -0.088224  ]
 [-0.07704422  2.07965201  0.99739221]
 [-0.3958175   1.23359604  2.16222145]
 [ 2.13504156  1.01391343 -0.14895499]
 [ 2.31059852  0.96260146 -0.27319998]
 [ 2.22247594  1.07534695 -0.29782289]
 [-0.27283046  1.13075432  2.14207614]
 [-0.17449621  2.18085941  0.9936368 ]
 [-0.29717239  0.92710063  2.37007176]
 [ 2.33180515  1.03788212 -0.36968728]]
predict:
 [2 1 0 1 2 1 0 1 1 1 2 0 2 0 0 1 2 2 1 2 1 2 2 1 2 2 1 2 2 2 1 0 1 1 1 1 1
 2 0 0 2 1 0 0 2 0 1 1 0 1 2 1 0 2 2 2 2 0 0 2 2 0 2 0 1 2 0 0 2 0 0 0 1 2
 2 0 0 0 1 2 0 0 2 0 2 1 2 2 0 1 0 2 0 0 2 0 2 1 1 1 2 2 2 2 0 1 2 1 0 2 1
 1 2 0 0 0 2 1 2 0]

8.画图

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)  # 生成网格采样点
grid_show = np.dstack((x1.flat, x2.flat))[0] # 测试点


grid_hat = clf.predict(grid_show)       # 预测分类值
grid_hat = grid_hat.reshape(x1.shape)  # 使之与输入的形状相同

cm_light = mpl.colors.ListedColormap(['#00FFCC', '#FFA0A0', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])

plt.figure(facecolor='w')
## 区域图
plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)
## 所有样本点
plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)  # 样本
## 测试数据集
plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)  # 圈中测试集样本
## 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'鸢尾花SVM特征分类', fontsize=16)
plt.grid(b=True, ls=':')
plt.tight_layout(pad=1.5)
plt.show()

绘图显示


总结

提示:这里对文章进行总结:
以上就是今天要讲的内容,本文仅仅简单介绍了鸢尾花数据进行SVM分类,下一章将介绍02_案例二:鸢尾花数据不同分类器效果比较

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