附源码|paddle paddle实现鸢尾花分类_%(tip,acc.mean())-优快云博客

本文是基于百度的paddle paddle实现经典数据集莺尾花分类问题，采用的是SVM算法。

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

#*************将字符串转为整型，便于数据加载***********************
def iris_type(s):
    it = {b'Iris-setosa':0, b'Iris-versicolor':1, b'Iris-virginica':2}
    return it[s]

#**************并判断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)))

def print_accuracy(clf,x_train,y_train,x_test,y_test):
    #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
    print('trianing prediction:%.3f' %(clf.score(x_train, y_train)))
    print('test data prediction:%.3f' %(clf.score(x_test, y_test)))
     #原始结果与预测结果进行对比   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 lenght', 'petal width'
    # 开始画图
    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:\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.。。。2,2,2】
    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 = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
    cm_dark = mpl.colors.ListedColormap(['g', 'b', 'r'])
 
    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()

#**********************SVM分类器构建*************************
def classifier():
    clf = svm.SVC(C=0.8, kernel='rbf', gamma=0.1, decision_function_shape='ovo')# 高斯核
    # 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

#***********************训练模型*****************************
def train(clf,x_train,y_train):
    clf.fit(x_train,         #训练集特征向量
            y_train.ravel()) #训练集目标值

#***********************主程序*****************************
# 1.数据准备
# 1.1加载数据
data = np.loadtxt('/home/aistudio/data/data5423/iris.data', #数据文件路径
                  dtype=float,                              #数据类型
                  delimiter=',',                            #数据分隔符
                  converters={4:iris_type})                 #将第5列使用函数iris_type进行转换
# 1.2数据分割
x, y = np.split(data,                                       #数组数据
                (4,),                                       #第5列开始往后为y
                axis=1)                                     #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,              #被划分的样本特征集
                                                               y,              #被划分的样本标签
                                                               random_state=1, #随机数种子
                                                               test_size=0.3)  #测试样本占比
# print(x_train)
# print(y_train)
# 2.定义模型：SVM模型定义
clf = classifier()
# 3.训练模型
train(clf,x_train,y_train)
# 4.模型评估
print_accuracy(clf,x_train,y_train,x_test,y_test)
# 5.模型使用
draw(clf,x)