【Logistic回归】鸢尾花分类（二）预测&分类

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.pipeline import Pipeline
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import warnings
warnings.filterwarnings("ignore")

if __name__ == "__main__":
    f_Iris = open("iris.data")
    data = pd.read_csv(f_Iris, header=None)
    data[4] = pd.Categorical(data[4]).codes
    x, y = np.split(data.values, (4,), axis=1)
    # 第0、4列数据
    x = x[:, [0,3]]
    lr = Pipeline([('sc', StandardScaler()),
                   ('poly', PolynomialFeatures(degree=10)),
                   ('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('准确度：%.2f%%' % (100*np.mean(y_hat == y.ravel())))
    # 画图
    N, M = 200, 200     # 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'] = ['simHei']
    mpl.rcParams['axes.unicode_minus'] = False
    cm_light = mpl.colors.ListedColormap(['#FFEC8B', '#A8A8A8', '#1E90FF'])
    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.flat, edgecolors='k', s=50, cmap=cm_dark)    # 样本的显示
    plt.xlabel(u'花萼长度', fontsize=14)
    plt.ylabel(u'花瓣宽度', fontsize=14)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.grid()
    patchs = [mpatches.Patch(color='#FFEC8B', label='Iris-setosa'),
              mpatches.Patch(color='#A8A8A8', label='Iris-versicolor'),
              mpatches.Patch(color='#1E90FF', 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', fontsize=17)
    plt.show()