色块显示参考
import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.patches as mpatches from sklearn.linear_model import LogisticRegression import pandas as pd path = 'iris.data' col = ['sepal length','sepal width','petal length','petal width','class'] data = pd.read_csv(path, header=None,names=col) # print(data.head()) #将字符标签转成数字标签 data['class'] = pd.Categorical(data['class']).codes label = data['class'] features = data[['sepal length','sepal width']] # 构建模型 logisreg = LogisticRegression() logisreg.fit(features,data['class']) print(logisreg.intercept_,logisreg.coef_) #模型的权值和偏置 y_hat = logisreg.predict(features)# 训练样本的预测值，是数字标签，实际相当于LR做多分类 print(u'准确度：%.2f%%'%(100*np.mean(y_hat==label.ravel()))) # 划分特征值所在的区域为一个一个的方格 x_min,x_max=features['sepal length'].min()-0.1,features['sepal length'].max()+0.1 y_min,y_max=features['sepal width'].min()-0.1,features['sepal width'].max()+0.1 xx,yy = np.meshgrid(np.linspace(x_min,x_max,200),np.linspace(y_min,y_max,200)) grid_test = np.stack((xx.flat,yy.flat),axis=1) # 计算分隔点的类别 y_predict = logisreg.predict(grid_test) # 设置样本点和背景的颜色 cm_pt = mpl.colors.ListedColormap(['#22B14C','#ED1C24','#A0A0FF'])# 点的颜色 cm_bg = mpl.colors.ListedColormap(['#B0E0E6','#FFC0CB','#B5E61D'])# 背景颜色 # 设置坐标轴显示范围 plt.xlim(x_min-0.1,x_max+0.1) plt.ylim(y_min-0.1,y_max+0.1) print(y_predict) # 划分区域的类别 plt.pcolormesh(xx,yy,y_predict.reshape(xx.shape),cmap=cm_bg) # 显示训练样本 plt.scatter(features['sepal length'],features['sepal width'], c=data['class'],cmap=cm_pt,edgecolors='k', s=40,marker='o') # edgecolors 散点边界的颜色 # s散点的大小 # marker 散点的形状 圆形，三角形，五角星，... # 区域块的图例 patchs = [mpatches.Patch(color='#B0E0E6', label='Iris-setosa'), mpatches.Patch(color='#FFC0CB', label='Iris-versicolor'), mpatches.Patch(color='#B5E61D', label='Iris-virginica')] plt.legend(handles=patchs, fancybox=True, framealpha=0.8) #显示中文 plt.rcParams['font.sans-serif']=['SimHei'] plt.xlabel(u'萼片的长度') plt.ylabel(u'萼片的宽度') plt.show()色块显示的是模型分块的结果，图片中的点是样本点
plt.scatter()参数
