本文共 2309 字，大约阅读时间需要 7 分钟。

#!/usr/bin/python# -*- coding:utf-8 -*-import numpy as npimport pandas as pdfrom sklearn.linear_model import LogisticRegressionfrom sklearn.preprocessing import StandardScaler, PolynomialFeaturesfrom sklearn.pipeline import Pipelineimport matplotlib.pyplot as pltimport matplotlib as mplimport matplotlib.patches as mpatchesif __name__ == "__main__":    path = 'iris.data'  # 数据文件路径    data = pd.read_csv(path, header=None)    data[4] = pd.Categorical(data[4]).codes    print("data[4]",data[4])    x, y = np.split(data.values, (4,), axis=1)    x = x[:, :2]    print("x=",x)    lr = Pipeline([('sc', StandardScaler()),#去均值和方差归一化。且是针对每一个特征维度来做的，而不是针对样本                   ('poly', PolynomialFeatures(degree=2)),                   ('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(u'准确度：%.2f%%' % (100 * np.mean(y_hat == 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(), x[:, 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    mpl.rcParams['font.sans-serif'] = [u'simHei']    mpl.rcParams['axes.unicode_minus'] = False    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.figure(facecolor='w')    plt.pcolormesh(x1, x2, y_hat, cmap=cm_light)  # 预测值的显示    plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', s=50, cmap=cm_dark)  # 样本的显示    plt.xlabel(u'花萼长度', fontsize=14)    plt.ylabel(u'花萼宽度', fontsize=14)    plt.xlim(x1_min, x1_max)    plt.ylim(x2_min, x2_max)    plt.grid()    patchs = [mpatches.Patch(color='#77E0A0', label='Iris-setosa'),              mpatches.Patch(color='#FF8080', label='Iris-versicolor'),              mpatches.Patch(color='#A0A0FF', label='Iris-virginica')]    plt.legend(handles=patchs, fancybox=True, framealpha=0.8)    plt.title(u'鸢尾花Logistic回归分类效果 - 标准化', fontsize=17)    plt.show()