python实现svm对鸢尾花进行分类（附带鸢尾花数据）

鸢尾花分类
鸢尾花数据链接：
http://bj.bcebos.com/v1/ai-studio-online/93e8a07d6624465c943f60a0b4ec5fd959d44b5e5453410a8b2452ed3720c32f?responseContentDisposition=attachment%3B%20filename%3Diris.data&authorization=bce-auth-v1%2F0ef6765c1e494918bc0d4c3ca3e5c6d1%2F2018-12-12T14%3A57%3A54Z%2F-1%2F%2F2cbe86672d5f2d44278cc3f76789307590c5aeffa85569803fd6e7d625b43ca2
方法一

import numpy as np
from sklearn import model_selection as mo
from sklearn import svm
import matplotlib.pyplot as plt
from matplotlib import colors
import matplotlib as mpl

def iris_type(s):
    # 数据转为整型，数据集标签类别由string转为int
    it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
    return it[s]

data = np.loadtxt(r'D:\PycharmProjects\untitled\鸢尾花\iris.data', dtype=float, delimiter=',', converters={4:iris_type})
'''
def loadtxt(fname, dtype=float, comments='#', delimiter=None,
            converters=None, skiprows=0, usecols=None, unpack=False,
            ndmin=0, encoding='bytes', max_rows=None):
'''
x, y = np.split(data, (4, ), axis=1)
x_train, x_test, y_train, y_test = mo.train_test_split(x, y, random_state=1, test_size=0.3)
'''
train_data：被划分的样本特征集
train_target：被划分的样本标签
test_size：如果是浮点数，在0-1之间，表示样本占比；如果是整数的话就是样本的数量
random_state：是随机数的种子。
随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。比如你每次都填1，其他参数一样的情况下你得到的随机数组是一样的。但填0或不填，每次都会不一样。
随机数的产生取决于种子，随机数和种子之间的关系遵从以下两个规则：
种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。
'''
clf = svm.SVC(C=0.5, kernel='linear', decision_function_shape='ovr')
clf.fit(x_train, y_train, sample_weight=None)
print(x_train.shape)
#print(x_train)
#print(y_train)
#print(x_test)
#print(y_test)
acc = clf.predict(x_train) == y_train.flat
print('Accuracy:%f' % (np.mean(acc)))
x1 = x[:, :2]
x_train, x_test, y_train, y_test = mo.train_test_split(x1, y,random_state=1, test_size=0.3)
clf.fit(x_train, y_train, sample_weight=None)
x1_min, x1_max = x1[:, 0].min(), x1[:, 0].max()
x2_min, x2_max = x1[:, 1].min(), x1[:, 1].max()
x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]
g_test = np.stack((x1.flat, x2.flat), axis=1)
print(g_test.shape)
g_map = clf.predict(g_test).reshape(x1.shape)
y = clf.predict(x_test)
cm_light = colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
cm_dack = colors.ListedColormap(['r', 'g', 'b'])
plt.pcolormesh(x1, x2, g_map, cmap=cm_light)
plt.scatter(x_test[:, 0], x_test[:, 1],c=np.squeeze(y.flat), s=50, cmap=cm_dack)
plt.plot()
plt.grid()
plt.show()

方法二

import numpy as np
from matplotlib import colors
from sklearn import svm
from sklearn.svm import SVC
from sklearn import model_selection
import matplotlib.pyplot as plt
import matplotlib as mpl

def load_data():
    # 导入数据
    data = np.loadtxt(r'D:\PycharmProjects\untitled\鸢尾花\iris.data', dtype=float, delimiter=',', converters={4: iris_type})

    return data


def iris_type(s):
    # 数据转为整型，数据集标签类别由string转为int
    it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
    return it[s]


    # 定义分类器
def classifier():
    clf = svm.SVC(C=0.5,  # 误差项惩罚系数
                  kernel='linear',  # 线性核 kenrel="rbf":高斯核
                  decision_function_shape='ovr')  # 决策函数

    return clf


def train(clf, x_train, y_train):
    # x_train：训练数据集
    # y_train：训练数据集标签
    # 训练开始
    clf.fit(x_train, y_train.ravel(),sample_weight=None)  # 同flnumpy.ravelatten将矩阵拉平


def show_accuracy(a, b, tip):
    acc = a.ravel() == b.ravel()
    print(a)
    print(b)
    print(acc)
    print('%s Accuracy:%.3f' % (tip, np.mean(acc)))


def print_accuracy(clf, x_train, y_train, x_test, y_test):
    #print(x_train)
    show_accuracy(clf.predict(x_train), y_train, 'traing data')
    show_accuracy(clf.predict(x_test), y_test, 'testing data')
    #print(x_train)
    #print(y_train.ravel())
    #print(clf.predict(x_train))


def draw(clf, x):  # 写完一个函数要运行，否则报错：函数未定义
    '''
    print(x.shape)
    (150, 2)
    '''
    iris_feature = 'sepal length', 'sepal width', 'petal lenght', 'petal width'
    x1_min, x1_max = x[:, 0].min(), x[:, 0].max()  # 第0列的范围
    x2_min, x2_max = x[:, 1].min(), x[:, 1].max()  # 第1列的范围

    x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]  # 生成网格采样点
    grid_test = np.stack((x1.flat, x2.flat), axis=1)  # 测试点
    '''
    print(grid_test.shape)
    (40000, 2)
    '''
    #print('grid_test:\n', grid_test)
    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.。。。2,2,2】
    '''
    print(grid_hat.shape)
    (40000,)
    '''
    #print('grid_hat:\n', grid_hat)
    grid_hat = grid_hat.reshape(x1.shape)  # reshape grid_hat和x1形状一致
    # 若3*3矩阵e，则e.shape()为3*3,表示3行3列
    cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    '''
    x3 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]  # 生成网格采样点
    print(x3.shape)
    (2, 200, 200)
    print(x1.shape)
    print(x2.shape)
    print(grid_hat.shape)
    (200, 200)
    (200, 200)
    简单理解为x1+x2=x3
    (200, 200)
    '''

    cm_dark = mpl.colors.ListedColormap(['g', 'b', 'r'])
    print(grid_hat.shape)
    plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)  # pcolormesh(x,y,z,cmap)这里参数代入
    # x1，x2，grid_hat，cmap=cm_light绘制的是背景。
    plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolor='k', s=50, cmap=cm_dark)  # 样本点
    plt.scatter(x_test[:, 0], x_test[:, 1], s=120, facecolor='none', zorder=10)  # 测试点
    plt.xlabel(iris_feature[0], fontsize=20)
    plt.ylabel(iris_feature[1], fontsize=20)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.title('svm in iris data classification', fontsize=30)
    plt.grid()
    plt.show()


# 训练四个特征：
data = load_data()
x, y = np.split(data, (4,), axis=1)  # x为前四列，y为第五列，x为训练数据，y为数据标签
# data=(150,5),x=(150,4),y=(150,1)

# x_train,x_test,y_train,y_test = 训练数据，测试数据，训练数据标签，测试数据标签
x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, random_state=1,
                                                                    test_size=0.3)  # 数据集划分成70%30%测试集
clf = classifier()  # 声明svm分类器对象
train(clf, x_train, y_train)  # 启动分类器进行模型训练

print_accuracy(clf, x_train, y_train, x_test, y_test)

# 训练两个特征（用于画图展示）
data = load_data()
#print(np.shape(data))
x,y = np.split(data,(4,),axis=1) # x为前四列，y为第五列，x为训练数据，y为数据标签
#print(np.shape(x))
#print(np.shape(y))
x=x[:,:2] # 只要前两个特征，此时只训练前两个特征，用于画图
#print(np.shape(x))
x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=1,test_size=0.3)
clf = classifier()
train(clf,x_train,y_train)
print_accuracy(clf,x_train,y_train,x_test,y_test)
draw(clf,x)