【实验练习】基于SVM的实现鸢尾花(Iris)数据集分类 (Python实现)

题目

采用SVM方法实现鸢尾花(Iris)数据集分类

数据集

iris.name是关于数据集的属性说明；

iris.data是实际的数据集，它包含三类数据，每类数据有50条数据。

要求

训练集：选取Iris数据集中80%的数据，即120个数据，每类含有40个数据。

测试集：采用除训练集外的30个数据。

具体SVM方法：自由根据情况来选择。

评价指标：选取分类相关的评价指标来衡量分类结果。

目录结构

    |----Exp3\
    |    |----Data\
    |    |    |----iris.data
    |    |    |----iris.names
    |    |----main.py
    |    |----Plot.py
    |    |----Result\
    |    |    |----iris-cla.png
    |    |    |----iris-petal-cla.png
    |    |    |----iris-sepal-cal.png
    |    |    |----iris-sepal-petal-cla.png

main.py 

# -*- coding: utf-8 -*- #
"""
@Project    ：MachineLearningLesson
@File       ：main.py 
@Author     ：ZAY
@Time       ：2023/6/4 15:44
@Annotation : " "
"""

import os
import torch
import sklearn
import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score,auc,roc_curve,precision_recall_curve,f1_score, precision_score, recall_score
# Exp.Exp3为本项目存储路径，根据自己实际存储地址进行更改
from Exp.Exp3.Plot import plotSepalShow,plotPetalShow,plotSPShow 

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 将标签由文字映射为数字
def Iris_label(s):
    it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
    return it[s]

def Iris_Sepal_Cla(data_x, label_y):
    data_x = data_x[:, 0:2]
    train_data, test_data, train_label, test_label = sklearn.model_selection.train_test_split(data_x, label_y,
                                                                                              random_state = 1,
                                                                                              train_size = 0.8,
                                                                                              test_size = 0.2)
    # 训练 SVM 分类器 https://blog.csdn.net/TeFuirnever/article/details/99646257
    classifier = svm.SVC(C = 0.5, kernel = 'rbf', gamma = 10, decision_function_shape = 'ovr')  # rbf
    classifier.fit(train_data, train_label.ravel())

    train_label_pre = classifier.predict(train_data)
    test_label_pre = classifier.predict(test_data)
    print('花萼训练集acc:', accuracy_score(train_label, train_label_pre))
    print('花萼测试集acc:', accuracy_score(test_label, test_label_pre))

    # 查看内部决策函数（返回的是样本到超平面的距离）
    train_decision_function = classifier.decision_function(train_data)
    predict_result = classifier.predict(train_data)

    # print('train_decision_function:', train_decision_function)
    # print('predict_result:', predict_result)

    plotSepalShow(test_data, test_label, data_x, label_y, classifier)

def Iris_Petal_Cla(data_x, label_y):
    # 基于SVM鸢尾花瓣长宽度二特征分类
    data_x = data_x[:, 2:4]

    train_data, test_data, train_label, test_label = sklearn.model_selection.train_test_split(data_x, label_y,
                                                                                              random_state = 1,
                                                                                              train_size = 0.8,
                                                                                              test_size = 0.2)
    classifier = svm.SVC(C = 0.5, kernel = 'rbf', gamma = 10, decision_function_shape = 'ovr')  # rbf
    classifier.fit(train_data, train_label.ravel())

    train_label_pre = classifier.predict(train_data)
    test_label_pre = classifier.predict(test_data)
    print('花瓣训练集acc:', accuracy_score(train_label, train_label_pre))
    print('花瓣测试集acc:', accuracy_score(test_label, test_label_pre))

    plotPetalShow(test_data, test_label, data_x, label_y, classifier)


if __name__ == "__main__":

    txt_path = './/Result//SVM.txt'

    data = np.loadtxt("./Data/iris.data", dtype = float, delimiter = ',', converters = {4: Iris_label})
    # 基于SVM鸢尾花萼长宽度二特征分类
    data_x, label_y = np.split(data, indices_or_sections = (4,), axis = 1)  # x为数据，y为标签

    Iris_Sepal_Cla(data_x, label_y)

    Iris_Petal_Cla(data_x, label_y)

 plotSepalShow.py plotPetalShow.py

# -*- coding: utf-8 -*- #
"""
@Project    ：MachineLearningLesson
@File       ：plot.py 
@Author     ：ZAY
@Time       ：2023/6/5 21:41
@Annotation : " "
"""

# 确定坐标轴范围
import matplotlib
import matplotlib.pyplot as plt
import numpy as np


def plotSepalShow(test_data, test_label, data_x, label_y, classifier):
    x1_min, x1_max = data_x[:, 0].min(), data_x[:, 0].max()  # 第0维特征的范围
    x2_min, x2_max = data_x[:, 1].min(), data_x[:, 1].max()  # 第1维特征的范围
    x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]  # 生成网络采样点
    # print(x1.shape) # (200, 200)
    # print(x1.flat) # flat属性可以使得像遍历以一维数组的方法来遍历多维数组
    grid_test = np.stack((x1.flat, x2.flat), axis = 1)  # 测试点
    # print(grid_test.shape) # (40000, 2)
    # 指定默认字体
    matplotlib.rcParams['font.sans-serif'] = ['SimHei']

    # 设置颜色
    cm_light = matplotlib.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    cm_dark = matplotlib.colors.ListedColormap(['g', 'r', 'b'])

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

    plt.pcolormesh(x1, x2, grid_hat, cmap = cm_light)  # 预测值的显示
    plt.scatter(data_x[:, 0], data_x[:, 1], c = label_y[:, 0], s = 30, cmap = cm_dark)  # 样本
    plt.scatter(test_data[:, 0], test_data[:, 1], c = test_label[:, 0], s = 30, edgecolors = 'k', zorder = 2,
                cmap = cm_dark)  # 圈中测试集样本点
    plt.xlabel('花萼长度', fontsize = 13)
    plt.ylabel('花萼宽度', fontsize = 13)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.title('基于SVM鸢尾花萼长宽度二特征分类')
    plt.savefig('./Result/iris-sepal-cal.png')
    plt.show()

def plotPetalShow(test_data, test_label, data_x, label_y, classifier):
    x1_min, x1_max = data_x[:, 0].min(), data_x[:, 0].max()  # 第2维特征的范围
    x2_min, x2_max = data_x[:, 1].min(), data_x[:, 1].max()  # 第3维特征的范围
    x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]  # 生成网络采样点
    # print(x1.shape) # (200, 200)
    # print(x1.flat) # flat属性可以使得像遍历以一维数组的方法来遍历多维数组
    grid_test = np.stack((x1.flat, x2.flat), axis = 1)  # 测试点
    # print(grid_test.shape) # (40000, 2)
    # 指定默认字体
    matplotlib.rcParams['font.sans-serif'] = ['SimHei']

    # 设置颜色
    cm_light = matplotlib.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    cm_dark = matplotlib.colors.ListedColormap(['g', 'r', 'b'])

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

    plt.pcolormesh(x1, x2, grid_hat, cmap = cm_light)  # 预测值的显示
    plt.scatter(data_x[:, 0], data_x[:, 1], c = label_y[:, 0], s = 30, cmap = cm_dark)  # 样本
    plt.scatter(test_data[:, 0], test_data[:, 1], c = test_label[:, 0], s = 30, edgecolors = 'k', zorder = 2,
                cmap = cm_dark)  # 圈中测试集样本点
    plt.xlabel('花瓣长度', fontsize = 13)
    plt.ylabel('花瓣宽度', fontsize = 13)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.title('基于SVM鸢尾花瓣长宽度二特征分类')
    plt.savefig('./Result/iris-petal-cla.png')
    plt.show()

测试结果

进一步优化：

def Iris_Sepal_Petal_Cla(data_x, label_y):
    data_x = np.stack((data_x[:, 0], data_x[:, 2], data_x[:, 3]), axis=1)

    train_data, test_data, train_label, test_label = sklearn.model_selection.train_test_split(data_x, label_y,
                                                                                              random_state = 1,
                                                                                              train_size = 0.8,
                                                                                              test_size = 0.2)
    classifier = svm.SVC(C = 0.5, kernel = 'linear', gamma = 10, decision_function_shape = 'ovr')  # rbf
    classifier.fit(train_data, train_label.ravel())

    train_label_pre = classifier.predict(train_data)
    test_label_pre = classifier.predict(test_data)
    print('花萼和花瓣训练集acc:', accuracy_score(train_label, train_label_pre))
    print('花萼和花瓣测试集acc:', accuracy_score(test_label, test_label_pre))

    plotSPShow(classifier, data_x, label_y)

def plotSPShow(clf, x, y):
    iris_feature = 'sepal length', 'sepal width', 'petal lenght', 'petal width'
    # 开始画图
    x0_min, x0_max = x[:, 0].min(), x[:, 0].max()
    x1_min, x1_max = x[:, 1].min(), x[:, 1].max()  # 第0列的范围
    x2_min, x2_max = x[:, 2].min(), x[:, 2].max()  # 第1列的范围
    x0, x1, x2 = np.mgrid[x0_min:x0_max:50j, x1_min:x1_max:50j, x2_min:x2_max:50j]  # 生成网格采样点,3D
    grid_test = np.stack((x0.flat, x1.flat, x2.flat), axis=1)  # stack():沿着新的轴加入一系列数组， flat的作用是将数组分解成可连续访问的元素,目的就是把他拉直后合并，并且不改变数组
    print('grid_test:\n', grid_test)

    grid_hat = clf.predict(grid_test)  # 预测分类值 得到【0,0.。。。2,2,2】
    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 = matplotlib.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    cm_dark = matplotlib.colors.ListedColormap(['g', 'r', 'b'])

    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')

    # s：marker标记的大小
    # c: 颜色  可为单个，可为序列
    # depthshade: 是否为散点标记着色以呈现深度外观。对 scatter() 的每次调用都将独立执行其深度着色。
    # marker：样式
    # alpha为点的透明度，在0~1之间

    ax.scatter(xs=x1, ys=x2, zs=x0, zdir='z', s=10, c=grid_hat, depthshade=True, cmap=cm_light,alpha=0.01)
    ax.scatter(xs=x[:,1], ys=x[:,2], zs=x[:,0], zdir='z', s=30, c=np.squeeze(y), depthshade=True, cmap=cm_dark, marker="^")
    plt.title('基于SVM鸢尾花萼长度和花瓣长宽度三特征分类')
    plt.savefig('./Result/iris-sepal-petal-cla.png')
    plt.show()


if __name__ == "__main__":

    txt_path = './/Result//SVM.txt'

    data = np.loadtxt("./Data/iris.data", dtype = float, delimiter = ',', converters = {4: Iris_label})
    # 基于SVM鸢尾花萼长宽度二特征分类
    data_x, label_y = np.split(data, indices_or_sections = (4,), axis = 1)  # x为数据，y为标签

    Iris_Sepal_Petal_Cla(data_x, label_y)

Plot.py 将以上绘图.py代码汇总

# -*- coding: utf-8 -*- #
"""
@Project    ：MachineLearningLesson
@File       ：plot.py 
@Author     ：ZAY
@Time       ：2023/6/5 21:41
@Annotation : " "
"""

# 确定坐标轴范围
import matplotlib
import matplotlib.pyplot as plt
import numpy as np


def plotSepalShow(test_data, test_label, data_x, label_y, classifier):
    x1_min, x1_max = data_x[:, 0].min(), data_x[:, 0].max()  # 第0维特征的范围
    x2_min, x2_max = data_x[:, 1].min(), data_x[:, 1].max()  # 第1维特征的范围
    x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]  # 生成网络采样点
    # print(x1.shape) # (200, 200)
    # print(x1.flat) # flat属性可以使得像遍历以一维数组的方法来遍历多维数组
    grid_test = np.stack((x1.flat, x2.flat), axis = 1)  # 测试点
    # print(grid_test.shape) # (40000, 2)
    # 指定默认字体
    matplotlib.rcParams['font.sans-serif'] = ['SimHei']

    # 设置颜色
    cm_light = matplotlib.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    cm_dark = matplotlib.colors.ListedColormap(['g', 'r', 'b'])

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

    plt.pcolormesh(x1, x2, grid_hat, cmap = cm_light)  # 预测值的显示
    plt.scatter(data_x[:, 0], data_x[:, 1], c = label_y[:, 0], s = 30, cmap = cm_dark)  # 样本
    plt.scatter(test_data[:, 0], test_data[:, 1], c = test_label[:, 0], s = 30, edgecolors = 'k', zorder = 2,
                cmap = cm_dark)  # 圈中测试集样本点
    plt.xlabel('花萼长度', fontsize = 13)
    plt.ylabel('花萼宽度', fontsize = 13)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.title('基于SVM鸢尾花萼长宽度二特征分类')
    plt.savefig('./Result/iris-sepal-cal.png')
    plt.show()

def plotPetalShow(test_data, test_label, data_x, label_y, classifier):
    x1_min, x1_max = data_x[:, 0].min(), data_x[:, 0].max()  # 第2维特征的范围
    x2_min, x2_max = data_x[:, 1].min(), data_x[:, 1].max()  # 第3维特征的范围
    x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]  # 生成网络采样点
    # print(x1.shape) # (200, 200)
    # print(x1.flat) # flat属性可以使得像遍历以一维数组的方法来遍历多维数组
    grid_test = np.stack((x1.flat, x2.flat), axis = 1)  # 测试点
    # print(grid_test.shape) # (40000, 2)
    # 指定默认字体
    matplotlib.rcParams['font.sans-serif'] = ['SimHei']

    # 设置颜色
    cm_light = matplotlib.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    cm_dark = matplotlib.colors.ListedColormap(['g', 'r', 'b'])

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

    plt.pcolormesh(x1, x2, grid_hat, cmap = cm_light)  # 预测值的显示
    plt.scatter(data_x[:, 0], data_x[:, 1], c = label_y[:, 0], s = 30, cmap = cm_dark)  # 样本
    plt.scatter(test_data[:, 0], test_data[:, 1], c = test_label[:, 0], s = 30, edgecolors = 'k', zorder = 2,
                cmap = cm_dark)  # 圈中测试集样本点
    plt.xlabel('花瓣长度', fontsize = 13)
    plt.ylabel('花瓣宽度', fontsize = 13)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.title('基于SVM鸢尾花瓣长宽度二特征分类')
    plt.savefig('./Result/iris-petal-cla.png')
    plt.show()

def plotSPShow(clf, x, y):
    iris_feature = 'sepal length', 'sepal width', 'petal lenght', 'petal width'
    # 开始画图
    x0_min, x0_max = x[:, 0].min(), x[:, 0].max()
    x1_min, x1_max = x[:, 1].min(), x[:, 1].max()  # 第0列的范围
    x2_min, x2_max = x[:, 2].min(), x[:, 2].max()  # 第1列的范围
    x0, x1, x2 = np.mgrid[x0_min:x0_max:50j, x1_min:x1_max:50j, x2_min:x2_max:50j]  # 生成网格采样点,3D
    grid_test = np.stack((x0.flat, x1.flat, x2.flat), axis=1)  # stack():沿着新的轴加入一系列数组， flat的作用是将数组分解成可连续访问的元素,目的就是把他拉直后合并，并且不改变数组
    print('grid_test:\n', grid_test)

    grid_hat = clf.predict(grid_test)  # 预测分类值 得到【0,0.。。。2,2,2】
    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 = matplotlib.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    cm_dark = matplotlib.colors.ListedColormap(['g', 'r', 'b'])

    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')

    # s：marker标记的大小
    # c: 颜色  可为单个，可为序列
    # depthshade: 是否为散点标记着色以呈现深度外观。对 scatter() 的每次调用都将独立执行其深度着色。
    # marker：样式
    # alpha为点的透明度，在0~1之间

    ax.scatter(xs=x1, ys=x2, zs=x0, zdir='z', s=10, c=grid_hat, depthshade=True, cmap=cm_light,alpha=0.01)
    ax.scatter(xs=x[:,1], ys=x[:,2], zs=x[:,0], zdir='z', s=30, c=np.squeeze(y), depthshade=True, cmap=cm_dark, marker="^")
    plt.title('基于SVM鸢尾花萼长度和花瓣长宽度三特征分类')
    plt.savefig('./Result/iris-sepal-petal-cla.png')
    plt.show()

测试结果

实验数据和完整代码请私信