Iris数据跑回归模型

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

if __name__ == '__main__':
    path = 'iris.data'

    data = pd.read_csv('iris.data', header=None)
    iris_types = data[4].unique()
    for i, type in enumerate(iris_types):
        data.set_value(data[4] == type, 4, i)
    x, y = np.split(data.values, (4,), axis=1)
    x = x.astype(np.float)
    y = y.astype(np.int)
    print('x = \n', x)
    print('y = \n', y)
    # 仅使用前两列特征
    x = x[:, :2]
    lr = Pipeline([('sc', StandardScaler()), ('clf', LogisticRegression())])
    lr.fit(x, y.ravel())
    y_hat = lr.predict(x)
    y_hat_prob = lr.predict_proba(x)
    np.set_printoptions(suppress=True)
    print('y_hat = \n', y_hat)
    print('y_hat_prob = \n', y_hat_prob)
    print('accuracy：%.2f%%' % (100*np.mean(y_hat == y.ravel())))
    # 画图
    # 横纵各采样多少个值
    N, M = 1000, 1000     
    # 第0列的范围
    x1_min, x1_max = x[:, 0].min(), x[:, 0].max()   
    # 第1列的范围
    x2_min, x2_max = x[:, 1].min(), x[:, 1].max()   
    t1 = np.linspace(x1_min, x1_max, N)
    t2 = np.linspace(x2_min, x2_max, M)
    # 生成网格采样点
    x1, x2 = np.meshgrid(t1, t2)           
    # 测试点         
    x_test = np.stack((x1.flat, x2.flat), axis=1)   

    cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
    cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
    # 预测值
    y_hat = lr.predict(x_test)                  
    # 使之与输入的形状相同
    y_hat = y_hat.reshape(x1.shape)                 
    plt.figure(facecolor='w')
    # 预测值的显示
    plt.pcolormesh(x1, x2, y_hat, cmap=cm_light)     
    # 样本的显示
    plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolors='k', s=50, cmap=cm_dark)    
    plt.xlabel('calyx length', fontsize=14)
    plt.ylabel('calyx width', fontsize=14)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.grid()
    plt.title('iris flower data in Logistic classifier - standardscaler', fontsize=17)
    plt.show()