02 贝叶斯算法 - 案例一 - 鸢尾花数据分类

浅浅的花香味﹌ 2022-02-14 11:31 589阅读 0赞

[01 贝叶斯算法 - 朴素贝叶斯][01 _ -]

#### 常规操作： ####

import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures
    from sklearn.naive_bayes import GaussianNB, MultinomialNB#高斯贝叶斯和多项式朴素贝叶斯
    from sklearn.pipeline import Pipeline
    from sklearn.metrics import accuracy_score
    from sklearn.model_selection import train_test_split
    from sklearn.neighbors import KNeighborsClassifier
    
    ## 设置属性防止中文乱码
    mpl.rcParams['font.sans-serif'] = [u'SimHei']
    mpl.rcParams['axes.unicode_minus'] = False
    
    # 花萼长度、花萼宽度，花瓣长度，花瓣宽度
    iris_feature_E = 'sepal length', 'sepal width', 'petal length', 'petal width'
    iris_feature_C = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
    iris_class = 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'
    features = [2,3]
    
    ## 读取数据
    path = './datas/iris.data'  # 数据文件路径
    data = pd.read_csv(path, header=None)
    x = data[list(range(4))]
    x = x[features]
    y = pd.Categorical(data[4]).codes ## 直接将数据特征转换为0，1,2
    print ("总样本数目：%d；特征属性数目：%d" % x.shape)

总样本数目：150；特征属性数目：2

--------------------

#### 数据分割，形成模型训练数据和测试数据 ####

x_train1, x_test1, y_train1, y_test1 = train_test_split(x, y, train_size=0.8, random_state=14)
    x_train, x_test, y_train, y_test = x_train1, x_test1, y_train1, y_test1
    print ("训练数据集样本数目：%d, 测试数据集样本数目：%d" % (x_train.shape[0], x_test.shape[0]))

训练数据集样本数目：120, 测试数据集样本数目：30

--------------------

#### 高斯贝叶斯模型构建 ####

clf = Pipeline([
            ('sc', StandardScaler()),#标准化，把它转化成了高斯分布
            ('poly', PolynomialFeatures(degree=1)),
            ('clf', GaussianNB())]) # MultinomialNB多项式贝叶斯算法中要求特征属性的取值不能为负数
    ## 训练模型
    clf.fit(x_train, y_train)

Pipeline(memory=None,

steps=[('sc', StandardScaler(copy=True, with_mean=True, with_std=True)), ('poly', PolynomialFeatures(degree=1, include_bias=True, interaction_only=False)), ('clf', GaussianNB(priors=None))])

--------------------

#### 计算预测值并计算准确率 ####

y_train_hat = clf.predict(x_train)
    print ('训练集准确度: %.2f%%' % (100 * accuracy_score(y_train, y_train_hat)))
    y_test_hat = clf.predict(x_test)
    print ('测试集准确度：%.2f%%' % (100 * accuracy_score(y_test, y_test_hat)))

训练集准确度: 95.83%  
测试集准确度：96.67%

--------------------

#### 产生区域图 ####

N, M = 500, 500     # 横纵各采样多少个值
    x1_min1, x2_min1 = x_train.min()
    x1_max1, x2_max1 = x_train.max()
    x1_min2, x2_min2 = x_test.min()
    x1_max2, x2_max2 = x_test.max()
    x1_min = np.min((x1_min1, x1_min2))
    x1_max = np.max((x1_max1, x1_max2))
    x2_min = np.min((x2_min1, x2_min2))
    x2_max = np.max((x2_max1, x2_max2))
    
    t1 = np.linspace(x1_min, x1_max, N)
    t2 = np.linspace(x2_min, x2_max, N)
    x1, x2 = np.meshgrid(t1, t2)  # 生成网格采样点
    x_show = np.dstack((x1.flat, x2.flat))[0] # 测试点
    
    cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
    cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
    y_show_hat = clf.predict(x_show)      # 预测值
    y_show_hat = y_show_hat.reshape(x1.shape)

--------------------

## 画图 ##

plt.figure(facecolor='w')
    plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light)     # 预测值的显示
    plt.scatter(x_train[features[0]], x_train[features[1]], c=y_train, edgecolors='k', s=50, cmap=cm_dark)
    plt.scatter(x_test[features[0]], x_test[features[1]], c=y_test, marker='^', edgecolors='k', s=120, cmap=cm_dark)
    plt.xlabel(iris_feature_C[features[0]], fontsize=13)
    plt.ylabel(iris_feature_C[features[1]], fontsize=13)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.title(u'GaussianNB对鸢尾花数据的分类结果, 正确率:%.3f%%' % (100 * accuracy_score(y_test, y_test_hat)), fontsize=18)
    plt.grid(True)
    plt.show()

![1240][]

[03 贝叶斯算法 - 案例二 - 新闻数据分类][03 _ - _ -]

[01 _ -]: https://www.jianshu.com/p/7a869cd83b1f
[1240]: /images/20220214/7899d0e5d7dc4742ac5045a2d628a695.png
[03 _ - _ -]: https://www.jianshu.com/p/ce117898f699