朴素贝叶斯

桃扇骨 2024-04-18 11:02 85阅读 0赞

# 1.介绍 #

文本分类实验  
http://qwone.com/~jason/20Newsgroups/下载数据

# 2.代码 #

鸢尾花  
Iris\_GaussianNB.py

#!/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    from sklearn.preprocessing import StandardScaler
    from sklearn.naive_bayes import GaussianNB, MultinomialNB
    from sklearn.pipeline import Pipeline
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.model_selection import train_test_split # 分数据集
    
    
    def iris_type(s):
        it = {
        'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}
        return it[s]
    
    
    if __name__ == "__main__":
        data = np.loadtxt('..\8.Regression\8.iris.data', dtype=float, delimiter=',', converters={
        4: iris_type})
        print data
        x, y = np.split(data, (4,), axis=1)
        x = x[:, :2]
        print x
        print y
    
    	# train_test_split(x,y,train_size=0.8)
        gnb = Pipeline([
            ('sc', StandardScaler()), # 标准化
            ('clf', GaussianNB())]) #假定服从高斯分布，朴素贝叶斯
        gnb.fit(x, y.ravel()) # 转成向量
        
        # gnb = MultinomialNB().fit(x, y.ravel())
        
        # gnb = KNeighborsClassifier(n_neighbors=5).fit(x, y.ravel()) # K近邻，设置层数
    
        # 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
    
        # 无意义，只是为了凑另外两个维度
        # x3 = np.ones(x1.size) * np.average(x[:, 2])
        # x4 = np.ones(x1.size) * np.average(x[:, 3])
        # x_test = np.stack((x1.flat, x2.flat, x3, x4), 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 = gnb.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.title(u'GaussianNB对鸢尾花数据的分类结果', fontsize=18)
        plt.grid(True)
        plt.show()
    
        # 训练集上的预测结果
        y_hat = gnb.predict(x)
        y = y.reshape(-1)
        result = y_hat == y
        print y_hat
        print result
        acc = np.mean(result)
        print '准确度: %.2f%%' % (100 * acc)

MultinomialNB\_intro.py

#!/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    from sklearn.naive_bayes import GaussianNB, MultinomialNB
    
    
    if __name__ == "__main__":
        np.random.seed(0) # 随机数据一样
        M = 20 # 20样本
        N = 5 # 五维
        x = np.random.randint(2, size=(M, N))     # 只能取0和1的MN矩阵，[low, high)
        x = np.array(list(set([tuple(t) for t in x]))) # 把数据提出变成元组，放入集合，再转成列表，形成新的数据----去重，20行除去重复的成为17行
        M = len(x) # 提取行数
        y = np.arange(M) # 行数作为y的标记值，每一行就是一个类别 
        print '样本个数：%d，特征数目：%d' % x.shape
        print '样本：\n', x
        mnb = MultinomialNB(alpha=1)    # 朴素贝叶斯；动手：换成GaussianNB()试试预测结果？
        # mnb = GaussianNB()
        mnb.fit(x, y) # 训练
        y_hat = mnb.predict(x) # 预测，看y和y_hat是否符合
        print '预测类别：', y_hat
        print '准确率：%.2f%%' % (100*np.mean(y_hat == y))
        print '系统得分：', mnb.score(x, y)
        # from sklearn import metrics
        # print metrics.accuracy_score(y, y_hat)
        err = y_hat != y
        for i, e in enumerate(err): # 枚举错误的值，y是实际的，y_hat是被认为的
            if e:
                print y[i], '：\t', x[i], '被认为与', x[y_hat[i]], '一个类别'

六模型文本处理  
text\_classification.py

#!/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    from sklearn.naive_bayes import MultinomialNB, BernoulliNB
    from sklearn.datasets import fetch_20newsgroups # 函数中包含了数据
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import RidgeClassifier
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.svm import SVC
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import GridSearchCV
    from sklearn import metrics
    from time import time
    from pprint import pprint
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    
    
    def test_clf(clf): # 某个分类器
        print u'分类器：', clf
        alpha_can = np.logspace(-3, 2, 10) # 等比数列，取10个不同的参数值
        model = GridSearchCV(clf, param_grid={
        'alpha': alpha_can}, cv=5) # 一个分类器做五次交叉验证（网格搜索），共50次
        m = alpha_can.size # 用于计算时间
        if hasattr(clf, 'alpha'): # NB的超参数
            model.set_params(param_grid={
        'alpha': alpha_can})
            m = alpha_can.size
        if hasattr(clf, 'n_neighbors'): # k-近邻的超参数
            neighbors_can = np.arange(1, 15)
            model.set_params(param_grid={
        'n_neighbors': neighbors_can})
            m = neighbors_can.size
        if hasattr(clf, 'C'): # SVM超参数
            C_can = np.logspace(1, 3, 3) # 时间长不多取
            gamma_can = np.logspace(-3, 0, 3)
            model.set_params(param_grid={
        'C':C_can, 'gamma':gamma_can})
            m = C_can.size * gamma_can.size
        if hasattr(clf, 'max_depth'): # 随机森林超参数
            max_depth_can = np.arange(4, 10)
            model.set_params(param_grid={
        'max_depth': max_depth_can})
            m = max_depth_can.size
        t_start = time()
        model.fit(x_train, y_train) # 训练，得到最佳参数
        t_end = time()
        t_train = (t_end - t_start) / (5*m) # 除以词次数
        print u'5折交叉验证的训练时间为：%.3f秒/(5*%d)=%.3f秒' % ((t_end - t_start), m, t_train)
        print u'最优超参数为：', model.best_params_ # 打印最佳超参数
        t_start = time()
        y_hat = model.predict(x_test)
        t_end = time()
        t_test = t_end - t_start
        print u'测试时间：%.3f秒' % t_test
        acc = metrics.accuracy_score(y_test, y_hat)
        print u'测试集准确率：%.2f%%' % (100 * acc)
        name = str(clf).split('(')[0] # 转成字符串
        index = name.find('Classifier')
        if index != -1: # 如果没找到
            name = name[:index]     # 去掉末尾的Classifier
        if name == 'SVC': # SVC换成SVM
            name = 'SVM'
        return t_train, t_test, 1-acc, name # 返回这个分类器的最终结果
    
    
    if __name__ == "__main__":
        print u'开始下载/加载数据...'
        t_start = time()
        # remove = ('headers', 'footers', 'quotes') # 删除标题等等
        remove = ()
        categories = 'alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space' # 关注的四个类别
        # categories = None     # 若分类所有类别，请注意内存是否够用
        data_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=0, remove=remove)
        data_test = fetch_20newsgroups(subset='test', categories=categories, shuffle=True, random_state=0, remove=remove)
        t_end = time() # 显示下载时间
        print u'下载/加载数据完成，耗时%.3f秒' % (t_end - t_start)
        print u'数据类型：', type(data_train)
        print u'训练集包含的文本数目：', len(data_train.data)
        print u'测试集包含的文本数目：', len(data_test.data)
        print u'训练集和测试集使用的%d个类别的名称：' % len(categories) # 4
        categories = data_train.target_names
        pprint(categories) # 效果稍好
        y_train = data_train.target # 类别
        y_test = data_test.target
        # 查看数据
        print u' -- 前10个文本 -- '
        for i in np.arange(10):
            print u'文本%d(属于类别 - %s)：' % (i+1, categories[y_train[i]]) # 显示类别
            print data_train.data[i] # 训练数据文本
            print '\n\n'
        # 提取词频做特征，非必须    
        vectorizer = TfidfVectorizer(input='content', stop_words='english', max_df=0.5, sublinear_tf=True) # TF-TDF模型提取特征，设置停止词，出现频率
        x_train = vectorizer.fit_transform(data_train.data)  # 做特征转换；x_train是稀疏的，scipy.sparse.csr.csr_matrix
        x_test = vectorizer.transform(data_test.data) # 测试数据特征转换
        
        print u'训练集样本个数：%d，特征个数：%d' % x_train.shape # 样本个数即文本数，特征个数即词数
        print u'停止词:\n',
        pprint(vectorizer.get_stop_words()) # 停止词（非重要）
        feature_names = np.asarray(vectorizer.get_feature_names())
    
        print u'\n\n===================\n分类器的比较：\n'
        # 运行时间，一次时间，正确率
        clfs = (MultinomialNB(),                # 0.87(0.017), 0.002, 90.39%
                BernoulliNB(),                  # 1.592(0.032), 0.010, 88.54%
                KNeighborsClassifier(),         # 19.737(0.282), 0.208, 86.03%
                RidgeClassifier(),              # 25.6(0.512), 0.003, 89.73%
                RandomForestClassifier(n_estimators=200),   # 59.319(1.977), 0.248, 77.01%
                SVC()                           # 236.59(5.258), 1.574, 90.10%，允许错误的SVM
                )
        result = []
        for clf in clfs: # 做遍历
            a = test_clf(clf)
            result.append(a) # 放到result中
            print '\n'
        result = np.array(result)
        time_train, time_test, err, names = result.T
        x = np.arange(len(time_train))
        mpl.rcParams['font.sans-serif'] = [u'simHei']
        mpl.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(10, 7), facecolor='w')
        ax = plt.axes()
        b1 = ax.bar(x, err, width=0.25, color='#77E0A0')
        ax_t = ax.twinx()
        b2 = ax_t.bar(x+0.25, time_train, width=0.25, color='#FFA0A0')
        b3 = ax_t.bar(x+0.5, time_test, width=0.25, color='#FF8080')
        plt.xticks(x+0.5, names, fontsize=10)
        leg = plt.legend([b1[0], b2[0], b3[0]], (u'错误率', u'训练时间', u'测试时间'), loc='upper left', shadow=True)
        # for lt in leg.get_texts():
        #     lt.set_fontsize(14)
        plt.title(u'新闻组文本数据不同分类器间的比较', fontsize=18)
        plt.xlabel(u'分类器名称')
        plt.grid(True)
        plt.tight_layout(2)
        plt.show()