调用分类问题

#!/usr/bin/python
# -*- coding:utf-8 -*-

import numpy as np
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import preprocessing
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

def iris_type(s):
    it = {b‘Iris-setosa‘: 0,b‘Iris-versicolor‘: 1,b‘Iris-virginica‘: 2}
    return it[s]

if __name__ == "__main__":
    path = u‘C:8.iris.data‘  # 数据文件路径

    # # 路径，浮点型数据，逗号分隔，第4列使用函数iris_type单独处理
    data = np.loadtxt(path,dtype=float,delimiter=‘,‘,converters={4: iris_type})
    print("data",data)
    # 将数据的0到3列组成x，第4列得到y
    x,y = np.split(data,(4,),axis=1)

    # 为了可视化，仅使用前两列特征
    x = x[:,:2]

    print("x:",x)
    print("y:",y)
    
    
    #
    # x = StandardScaler().fit_transform(x)
    # lr = LogisticRegression()   # Logistic回归模型
    #     lr.fit(x,y.ravel())        # 根据数据[x,y]，计算回归参数
    #
    # 等价形式
    lr = Pipeline([(‘sc‘,StandardScaler()),(‘clf‘,LogisticRegression()) ])
    lr.fit(x,y.ravel())

    # 画图
    N,M = 500,500     # 横纵各采样多少个值
    x1_min,x1_max = x[:,0].min(),x[:,0].max()   # 第0列的范围
    x2_min,x2_max = x[:,1].min(),1].max()   # 第1列的范围
    t1 = np.linspace(x1_min,x1_max,N)
    t2 = np.linspace(x2_min,x2_max,M)
    x1,x2 = np.meshgrid(t1,t2)                    # 生成网格采样点
    x_test = np.stack((x1.flat,x2.flat),axis=1)   # 测试点

    # 无意义，只是为了凑另外两个维度
    # x3 = np.ones(x1.size) * np.average(x[:,2])
    # x4 = np.ones(x1.size) * np.average(x[:,3])
    # x_test = np.stack((x1.flat,x2.flat,x3,x4),axis=1)  # 测试点

    cm_light = mpl.colors.ListedColormap([‘#77E0A0‘,‘#FF8080‘,‘#A0A0FF‘])
    cm_dark = mpl.colors.ListedColormap([‘g‘,‘r‘,‘b‘])

    y_hat = lr.predict(x_test)                  # 预测值
    y_hat = y_hat.reshape(x1.shape)                 # 使之与输入的形状相同


    plt.pcolormesh(x1,x2,y_hat,cmap=cm_light)     # 预测值的显示

    print("===="*30)
    print(len(x[:,0]))
    plt.scatter(x[:,0],1],c=np.squeeze(y),edgecolors=‘k‘,s=50,cmap=cm_dark)    # 样本的显示


    plt.xlabel(‘petal length‘)
    plt.ylabel(‘petal width‘)
    plt.xlim(x1_min,x1_max)
    plt.ylim(x2_min,x2_max)
    plt.grid()
    # plt.savefig(‘2.png‘)
    plt.show()

    # 训练集上的预测结果
    y_hat = lr.predict(x)
    y = y.reshape(-1)
    result = y_hat == y
    print(y_hat)
    print(result)
    acc = np.mean(result)
    print(‘准确度: %.2f%%‘ % (100 * acc))