ML之NB：基于news新闻文本数据集利用纯统计法、kNN、朴素贝叶斯(高斯/多元伯努利/多项式)、线性判别分析LDA、感知器等算法实现文本分类预测daiding

深藏阁楼爱情的钟 2022-10-22 10:56 17阅读 0赞

ML之NB：基于news新闻文本数据集利用纯统计法、kNN、朴素贝叶斯(高斯/多元伯努利/多项式)、线性判别分析LDA、感知器等算法实现文本分类预测

**目录**

基于news新闻文本数据集利用纯统计法、kNN、朴素贝叶斯(高斯/多元伯努利/多项式)、线性判别分析LDA、感知器等算法实现文本分类预测

设计思路

输出结果

核心代码

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

**相关文章**  
[ML之NB：基于news新闻文本数据集利用纯统计法、kNN、朴素贝叶斯(高斯/多元伯努利/多项式)、线性判别分析LDA、感知器等算法实现文本分类预测][ML_NB_news_kNN_LDA]  
[ML之NB：基于news新闻文本数据集利用纯统计法、kNN、朴素贝叶斯(高斯/多元伯努利/多项式)、线性判别分析LDA、感知器等算法实现文本分类预测实现][ML_NB_news_kNN_LDA 1]

# 基于news新闻文本数据集利用纯统计法、kNN、朴素贝叶斯(高斯/多元伯努利/多项式)、线性判别分析LDA、感知器等算法实现文本分类预测 #

## 设计思路 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

## 输出结果 ##

**代码中的数据集**：[https://download.csdn.net/download/qq\_41185868/13757777][https_download.csdn.net_download_qq_41185868_13757777]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 4][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 5][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 6][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 7][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 8][]

F:\Program Files\Python\Python36\lib\site-packages\gensim\utils.py:1209: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
      warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 1293 entries, 0 to 1292
    Data columns (total 6 columns):
     #   Column      Non-Null Count  Dtype 
    ---  ------      --------------  ----- 
     0   Unnamed: 0  1293 non-null   int64 
     1   content     1292 non-null   object
     2   id          1293 non-null   int64 
     3   tags        1293 non-null   object
     4   time        1293 non-null   object
     5   title       1293 non-null   object
    dtypes: int64(2), object(4)
    memory usage: 60.7+ KB
    None
    
                        id                                  tags  \
    0  6428905748545732865   ['财经', '白洋淀', '城市规划', '徐匡迪', '太行山']   
    1  6428954136200855810   ['财经', '碧桂园', '万科集团', '投资', '广州恒大']   
    2  6420576443738784002    ['财经', '自行车', '凤凰', '王朝阳', '汽车展览']   
    3  6429007290541031681  ['财经', '银行', '工商银行', '兴业银行', '交通银行']   
    4  6397481672254619905     ['财经', '小吃', '装修', '市场营销', '手工艺']   
    
                      time                   title  
    0  2017-06-07 22:52:55  雄安新区规划“骨架”敲定，方案有望9月底出炉  
    1  2017-06-08 08:01:13       “红五月”不红 房企资金链压力攀升  
    2  2017-05-16 12:03:00      凤凰自行车总裁：共享单车把我们打懵了  
    3  2017-06-08 07:00:00    25家银行分红季派出3536亿“大红包”  
    4  2017-03-15 07:03:22      五万以下的小本餐饮项目，卷饼赚钱最稳  
    chinese_pattern re.compile('[\\u4e00-\\u9fff]+')
    Building prefix dict from F:\File_Jupyter\实用代码\naive_bayes（简单贝叶斯）\jieba_dict\dict.txt.big ...
    Loading model from cache C:\Users\niu\AppData\Local\Temp\jieba.ue3752d4e13420d2dc6b66831a5a4ab13.cache
    Loading model cost 1.326 seconds.
    Prefix dict has been built succesfully.
    dictionary
    <class 'gensim.corpora.dictionary.Dictionary'> Dictionary(46351 unique tokens: ['一个', '一个个', '一举一动', '一些', '一体']...)
    <class 'method'> <bound method Dictionary.doc2bow of <gensim.corpora.dictionary.Dictionary object at 0x000001BDC62291D0>>
    F:\Program Files\Python\Python36\lib\site-packages\numpy\core\_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
      return array(a, dtype, copy=False, order=order)
    
    
                                                  corpus  \
    0  [(0, 6), (1, 1), (2, 1), (3, 3), (4, 2), (5, 2...   
    1  [(0, 1), (3, 3), (13, 1), (17, 1), (41, 1), (5...   
    2  [(15, 1), (53, 1), (167, 1), (262, 1), (396, 1...   
    
                                                   tfidf  
    0  [(0, 0.005554342859788116), (1, 0.007470250835...  
    1  [(0, 0.002081356679198299), (3, 0.012288034179...  
    2  [(15, 0.057457146244872616), (53, 0.0543395377...  
    after abs 4.7683716e-07
    foo： (1293, 1293)
    dis2TSNE_Visual:  (1293, 2)
    {'养生': 0, '科技': 1, '财经': 2, '游戏': 3, '育儿': 4, '汽车': 5}
    data_frame.keyword_index： 1    379
    2    287
    5    283
    4    148
    3    141
    0     55
    Name: keyword_index, dtype: int64
    
                        id                                 tags  \
    0  6428905748545732865  ['财经', '白洋淀', '城市规划', '徐匡迪', '太行山']   
    1  6428954136200855810  ['财经', '碧桂园', '万科集团', '投资', '广州恒大']   
    2  6420576443738784002   ['财经', '自行车', '凤凰', '王朝阳', '汽车展览']   
    
    
                                               doc_words  \
    0  [牵动人心, 雄安, 新区, 规划, 细节, 内容, 出台, 时间表, 敲定, 日前, 北京...   
    1  [去年, 以来, 多个, 城市, 先后, 发布, 多项, 楼市, 调控, 政策, 限购, 限...   
    2  [今年, 中国, 国际, 自行车, 展上, 上海, 凤凰, 自行车, 总裁, 王, 朝阳, ...   
    
                                                  corpus  \
    0  [(0, 6), (1, 1), (2, 1), (3, 3), (4, 2), (5, 2...   
    1  [(0, 1), (3, 3), (13, 1), (17, 1), (41, 1), (5...   
    2  [(15, 1), (53, 1), (167, 1), (262, 1), (396, 1...   
    
                                                   tfidf   visual01   visual02  \
    0  [(0, 0.005554342859788116), (1, 0.007470250835... -65.903542 -14.433964   
    1  [(0, 0.002081356679198299), (3, 0.012288034179... -29.659267 -14.811647   
    2  [(15, 0.057457146244872616), (53, 0.0543395377... -22.118195 -48.148167   
    
       keyword_index  
    0              2  
    1              2  
    2              2  
    Childcare，label_category_ID_pos.tfidf)[:20]： ['孩子', '家长', '教育', '学习', '男孩子', '成绩', '爸爸', '分享', '帮助', '方法', '小学', '数学', '交流', '男孩', '妈妈', '成长', '父母', '懂', '免费', '翼航']
    Childcare，label_category_ID_neg.tfidf)[:20]： []
    train_index MatrixSimilarity<646 docs, 46329 features>
    hot_words shape： 6 300
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1503, 5096, 1506, 4584, 23019, 493, 494, 498, 5108, 18935, 1529, 6138, 7163, 10238, 5119}, 5: {0, 14849, 512, 3, 11266, 14853, 2053, 23047, 1527, 2569, 15370, 14861, 13, 19471, 2577, 11793, 14867, 18423, 533, 15384, 14875, 15388, 11807, 15396, 4132, 1574, 14890, 14893, 14896, 14897, 1586, 51, 1590, 14911, 1088, 15429, 14406, 23111, 16968, 14921, 14925, 16461, 14929, 15442, 8789, 14934, 2647, 3161, 7770, 91, 14940, 9308, 14937, 14943, 608, 6755, 1124, 13924, 14950, 5219, 14947, 9325, 3697, 14961, 11893, 14968, 12408, 15485, 637, 5247, 1668, 1157, 23172, 647, 15492, 15498, 5773, 19087, 13969, 9362, 15506, 1681, 148, 11926, 1176, 2713, 155, 1180, 15517, 1692, 20124, 10401, 19105, 675, 674, 19109, 167, 1704, 11946, 15019, 12458, 1709, 682, 9091, 2224, 15025, 20656, 176, 180, 7858, 12982, 15031, 15543, 41136, 14013, 2239, 1729, 708, 9413, 21700, 712, 15562, 15051, 2765, 15057, 15061, 9942, 15063, 21718, 22747, 15068, 15069, 32475, 13535, 15583, 15074, 227, 19683, 2789, 1766, 13542, 13036, 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    word_bagNum shape： 6 50
    {0: [1536, 7681, 17410, 17411, 17415, 6664, 17420, 15886, 4623, 17935, 4625, 5139, 4631, 17916, 17437, 544, 16422, 5671, 1065, 4650, 4651, 4653, 4690, 16943, 4657, 17458, 15921, 51, 7222, 17464, 17465, 10299, 15932, 64, 6209, 66, 17474, 4680, 8264, 8266, 40008, 6730, 8273, 6738, 5203, 5206, 18005, 15958, 597, 15960], 1: [0, 3, 11785, 2569, 32779, 9227, 526, 21519, 530, 4116, 533, 11805, 2590, 2591, 3105, 7203, 1571, 8740, 1574, 12836, 1062, 1577, 2553, 4654, 1071, 2094, 30257, 51, 30260, 53, 28213, 24633, 1082, 1087, 68, 8779, 78, 12367, 11859, 2647, 91, 13916, 13917, 15455, 608, 9825, 1634, 12387, 13412, 613], 2: [0, 3, 520, 1547, 12300, 2062, 3599, 1040, 26641, 18, 25616, 2577, 13846, 2583, 4121, 25114, 1051, 1052, 25629, 1054, 1567, 2591, 3105, 3616, 4126, 1060, 4125, 1062, 1063, 26663, 1577, 13863, 1066, 1580, 45, 1071, 51, 3123, 53, 2614, 3125, 1082, 2622, 66, 2627, 11843, 1093, 1606, 1605, 3651], 3: [1536, 0, 10242, 3, 37889, 1029, 10248, 2569, 9740, 9745, 10770, 17938, 2577, 10257, 9238, 3094, 9752, 9751, 9754, 30235, 9243, 18425, 9246, 2590, 24096, 9249, 9250, 9251, 4643, 10272, 9252, 5666, 3616, 3625, 4133, 4136, 1071, 9264, 4657, 51, 9267, 22583, 10808, 40504, 10304, 6210, 3650, 37444, 68, 9284], 4: [0, 5121, 4098, 3, 3078, 7175, 1543, 1545, 22027, 5131, 14, 4623, 4625, 22547, 533, 2588, 2590, 1570, 4643, 2597, 5669, 5159, 6183, 2602, 45, 6702, 18937, 5168, 5169, 48, 4657, 3063, 51, 1590, 12343, 5686, 5689, 2105, 1586, 5175, 5694, 6721, 68, 2630, 29767, 29778, 4692, 2133, 5204, 6740], 5: [0, 14849, 512, 3, 11266, 14853, 2053, 23047, 1527, 2569, 15370, 14861, 13, 19471, 2577, 11793, 14867, 18423, 533, 15384, 14875, 15388, 11807, 15396, 4132, 1574, 14890, 14893, 14896, 14897, 1586, 51, 1590, 14911, 1088, 15429, 14406, 23111, 16968, 14921, 14925, 16461, 14929, 15442, 8789, 14934, 2647, 3161, 7770, 91]}
    after all_words, word_bag shape： 6 300
    {0: [1536, 7681, 17410, 17411, 17415, 6664, 17420, 15886, 4623, 17935, 4625, 5139, 4631, 17916, 17437, 544, 16422, 5671, 1065, 4650, 4651, 4653, 4690, 16943, 4657, 17458, 15921, 51, 7222, 17464, 17465, 10299, 15932, 64, 6209, 66, 17474, 4680, 8264, 8266, 40008, 6730, 8273, 6738, 5203, 5206, 18005, 15958, 597, 15960, 0, 3, 11785, 2569, 32779, 9227, 526, 21519, 530, 4116, 533, 11805, 2590, 2591, 3105, 7203, 1571, 8740, 1574, 12836, 1062, 1577, 2553, 4654, 1071, 2094, 30257, 51, 30260, 53, 28213, 24633, 1082, 1087, 68, 8779, 78, 12367, 11859, 2647, 91, 13916, 13917, 15455, 608, 9825, 1634, 12387, 13412, 613, 0, 3, 520, 1547, 12300, 2062, 3599, 1040, 26641, 18, 25616, 2577, 13846, 2583, 4121, 25114, 1051, 1052, 25629, 1054, 1567, 2591, 3105, 3616, 4126, 1060, 4125, 1062, 1063, 26663, 1577, 13863, 1066, 1580, 45, 1071, 51, 3123, 53, 2614, 3125, 1082, 2622, 66, 2627, 11843, 1093, 1606, 1605, 3651, 1536, 0, 10242, 3, 37889, 1029, 10248, 2569, 9740, 9745, 10770, 17938, 2577, 10257, 9238, 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15429, 14406, 23111, 16968, 14921, 14925, 16461, 14929, 15442, 8789, 14934, 2647, 3161, 7770, 91], 5: [1536, 7681, 17410, 17411, 17415, 6664, 17420, 15886, 4623, 17935, 4625, 5139, 4631, 17916, 17437, 544, 16422, 5671, 1065, 4650, 4651, 4653, 4690, 16943, 4657, 17458, 15921, 51, 7222, 17464, 17465, 10299, 15932, 64, 6209, 66, 17474, 4680, 8264, 8266, 40008, 6730, 8273, 6738, 5203, 5206, 18005, 15958, 597, 15960, 0, 3, 11785, 2569, 32779, 9227, 526, 21519, 530, 4116, 533, 11805, 2590, 2591, 3105, 7203, 1571, 8740, 1574, 12836, 1062, 1577, 2553, 4654, 1071, 2094, 30257, 51, 30260, 53, 28213, 24633, 1082, 1087, 68, 8779, 78, 12367, 11859, 2647, 91, 13916, 13917, 15455, 608, 9825, 1634, 12387, 13412, 613, 0, 3, 520, 1547, 12300, 2062, 3599, 1040, 26641, 18, 25616, 2577, 13846, 2583, 4121, 25114, 1051, 1052, 25629, 1054, 1567, 2591, 3105, 3616, 4126, 1060, 4125, 1062, 1063, 26663, 1577, 13863, 1066, 1580, 45, 1071, 51, 3123, 53, 2614, 3125, 1082, 2622, 66, 2627, 11843, 1093, 1606, 1605, 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    features_data_frame.shape： (6, 255)
    0 30
    1 185
    2 139
    3 66
    4 69
    5 157
    class_Proportion： 
     [0.04643962848297214, 0.28637770897832815, 0.21517027863777088, 0.1021671826625387, 0.10681114551083591, 0.24303405572755418]
    test_data_frame.head(2) 
          Unnamed: 0                                            content  \
    854         854  据Mobileexpose报道，华硕已经正式向媒体发出邀请，定于6月14日在台湾举办记者会，...   
    101         101   6月6日，王者荣耀猴三棍重做引起王者峡谷一阵轩然大波，毕竟这个强势的猴子已经陪伴我们好几个...   
    
                          id                                   tags  \
    854  6429089676803440897  ['科技', '华硕', '华硕ZenFone', '台湾', '手机']   
    101  6429098400347586818       ['游戏', '猴子', '王者荣耀', '黄忠', '游戏']   
    
                        time                     title  \
    854  2017-06-07 10:11:00        华硕ZenFone AR宣布本月发售   
    101  2017-06-07 10:39:20  猴子重做之后是加强还是削弱？狂到站对面泉水拿双杀   
    
                                                 doc_words  \
    854  [报道, 华硕, 已经, 正式, 媒体, 发出, 邀请, 定于, 月, 日, 台湾, 举办,...   
    101  [月, 日, 王者, 荣耀, 猴三棍, 重, 做, 引起, 王者, 峡谷, 一阵, 轩然大波...   
    
                                                    corpus  \
    854  [(142, 1), (362, 1), (472, 1), (475, 1), (494,...   
    101  [(0, 2), (68, 3), (133, 1), (184, 1), (226, 1)...   
    
                                                     tfidf   visual01   visual02  \
    854  [(142, 0.13953435619531032), (362, 0.046441336...  21.684397 -30.567736   
    101  [(0, 0.012838015508020575), (68, 0.04742284222...  67.188065  21.183245   
    
         keyword_index  
    854              1  
    101              3  
    print the first sample 
     Unnamed: 0                                                     854
    会，...
    id                                             6429089676803440897
    tags                         ['科技', '华硕', '华硕ZenFone', '台湾', '手机']
    time                                           2017-06-07 10:11:00
    title                                           华硕ZenFone AR宣布本月发售
    doc_words        [报道, 华硕, 已经, 正式, 媒体, 发出, 邀请, 定于, 月, 日, 台湾, 举办,...
    corpus           [(142, 1), (362, 1), (472, 1), (475, 1), (494,...
    tfidf            [(142, 0.13953435619531032), (362, 0.046441336...
    visual01                                                   21.6844
    visual02                                                  -30.5677
    keyword_index                                                    1
    Name: 854, dtype: object
    test_data_frame.iloc[0].corpus:  [(142, 1), (362, 1), (472, 1), (475, 1), (494, 1), (530, 1), (872, 1), (909, 1), (1254, 1), (1312, 1), (1878, 1), (2577, 1), (2783, 1), (2979, 1), (3697, 1), (5508, 1), (9052, 1), (12204, 1), (12256, 1), (12591, 1), (12936, 1), (12991, 1), (13128, 1), (13194, 1), (13244, 1), (13317, 1), (31670, 1), (31683, 1), (33417, 1)]
    [1.45708072e-43 1.78656934e-66 7.12148875e-63 1.71090490e-53
     4.71385662e-54 2.08405934e-64]
    [-35.34436300647761, -16.431856044032266, -20.267559000416433, -22.405433968586664, -27.97121661401147, -18.05089965903481]
    F:\File_Jupyter\实用代码\naive_bayes（简单贝叶斯）\TextClassPrediction_kNN_NB_LDA_P.py:346: SettingWithCopyWarning: 
    A value is trying to be set on a copy of a slice from a DataFrame.
    Try using .loc[row_indexer,col_indexer] = value instead
    
    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
      test_data_frame['predicted_class'] = test_data_frame['corpus'].apply(predict_text_ByMax)       #预测所有测试文档   predict all test documents
    
                           id                                   tags  \
    854   6429089676803440897  ['科技', '华硕', '华硕ZenFone', '台湾', '手机']   
    101   6429098400347586818       ['游戏', '猴子', '王者荣耀', '黄忠', '游戏']   
    738   6413133652368982274     ['科技', '厨卫电器', '榨汁机', '小家电', '硅谷']   
    511   6428827159980867842     ['科技', '智能家居', '音箱', '苹果公司', '法国']   
    725   6428841852455354625                  ['科技', '喜马拉雅山', '科技']   
    ...                   ...                                    ...   
    805   6429151552733069569                           ['财经', '财经']   
    448   6415852634885341441    ['汽车', 'SUV', '国产车', '概念车', '汽车用品']   
    782   6428858665063383297   ['科技', '新能源汽车', '电动汽车', '新能源', '经济']   
    1264  6427822755417194753    ['汽车', '日本汽车', '讴歌汽车', 'SUV', '空调']   
    1195  6429093420292210945                     ['科技', '乐视', '科技']   
    
                         time                        title  \
    854   2017-06-07 10:11:00           华硕ZenFone AR宣布本月发售   
    101   2017-06-07 10:39:20     猴子重做之后是加强还是削弱？狂到站对面泉水拿双杀   
    738   2017-04-26 10:41:39                绝！他用一台榨汁机骗了8亿   
    511   2017-06-08 11:06:00    他的智能音箱一上市，苹果公司就推出了HomePod   
    725   2017-06-07 18:37:00  喜马拉雅FM推出“付费会员”，当天召集超221万名会员   
    ...                   ...                          ...   
    805   2017-06-08 14:30:00          盘中近20家龙头白马股集体创下历史新高   
    448   2017-05-03 18:37:20      别瞎找了！10万左右尺寸最大的SUV都在这里了   
    782   2017-06-07 19:12:00      倡导移动出行新概念 NEVS两款概念量产车亮相   
    1264  2017-06-08 09:54:40        居然还有一款车，最低配和中高配看不出差别？   
    1195  2017-06-08 10:45:00     乐视被爆未及时缴物业费，员工或将被阻止进大楼办公   
    
                                                  doc_words  \
    854   [报道, 华硕, 已经, 正式, 媒体, 发出, 邀请, 定于, 月, 日, 台湾, 举办,...   
    101   [月, 日, 王者, 荣耀, 猴三棍, 重, 做, 引起, 王者, 峡谷, 一阵, 轩然大波...   
    738   [骗子, 往往, 很会, 讲故事, 以下, 硅谷, 骗局, 验血, 公司, 号称, 指尖, ...   
    511   [专访, 创始人, 孟, 崨, 学校, 最, 调皮, 却, 成绩, 最好, 学生, 老师, ...   
    725   [据介绍, 喜马拉雅, 会员, 月费, 元, 年度, 会员, 元, 价格, 视频, 网站, ...   
    ...                                                 ...   
    805   [每经, 记者, 王海, 慜, 每经, 编辑, 叶峰, 今日, 盘中, 昨日, 领涨, 中小...   
    448   [中国, 人买, 喜欢, 房子, 买, 面积, 手机, 买, 屏大, 买车, 自然, 挑选,...   
    782   [中证网, 讯, 记者, 徐金忠, 月, 日, 国, 电动汽车, 瑞典, 有限公司, 亮相,...   
    1264  [目前, 日系, 豪华, 品牌, 讴歌, 已经, 开启, 国产, 路, 推出, 车型, 后,...   
    1195  [近日, 爆料, 称, 乐视, 位于, 北京, 达美, 中心, 办公地, 因未, 及时, 缴...   
    
                                                     corpus  \
    854   [(142, 1), (362, 1), (472, 1), (475, 1), (494,...   
    101   [(0, 2), (68, 3), (133, 1), (184, 1), (226, 1)...   
    738   [(0, 2), (45, 1), (48, 1), (133, 2), (155, 1),...   
    511   [(0, 10), (13, 2), (14, 2), (20, 1), (45, 1), ...   
    725   [(30, 1), (102, 1), (142, 1), (154, 1), (189, ...   
    ...                                                 ...   
    805   [(113, 1), (167, 1), (169, 1), (214, 1), (258,...   
    448   [(4, 2), (8, 1), (14, 1), (51, 6), (53, 2), (6...   
    782   [(15, 2), (30, 1), (53, 7), (93, 1), (143, 1),...   
    1264  [(0, 1), (20, 1), (51, 1), (176, 1), (225, 1),...   
    1195  [(57, 1), (111, 1), (191, 1), (361, 1), (476, ...   
    
                                                      tfidf   visual01   visual02  \
    854   [(142, 0.13953435619531032), (362, 0.046441336...  21.684397 -30.567736   
    101   [(0, 0.012838015508020575), (68, 0.04742284222...  67.188065  21.183245   
    738   [(0, 0.008984009118453712), (45, 0.01791359767... -22.855194 -11.270862   
    511   [(0, 0.04361196171462796), (13, 0.028607388065... -22.198786  12.217076   
    725   [(30, 0.05815947983270004), (102, 0.0450585853...  26.268911  21.240065   
    ...                                                 ...        ...        ...   
    805   [(113, 0.030899018921031703), (167, 0.02103003... -66.232071   0.221611   
    448   [(4, 0.04071064284477513), (8, 0.0235138776022...  41.836094 -44.539528   
    782   [(15, 0.03392075672049564), (30, 0.03003603467... -26.810091 -29.602842   
    1264  [(0, 0.009883726180653873), (20, 0.04080153677...  36.279522 -52.474297   
    1195  [(57, 0.09668298763559263), (111, 0.1255406499...  -6.373239  16.101738   
    
          keyword_index  predicted_class  
    854               1                1  
    101               3                3  
    738               1                1  
    511               1                2  
    725               1                1  
    ...             ...              ...  
    805               2                2  
    448               5                5  
    782               1                1  
    1264              5                5  
    1195              1                1  
    
    [647 rows x 13 columns]
    SModel_CS_acc_score： 0.7047913446676971
    300
    label_category_ID 2
    一个
    一些
    概念
    经营
    补贴
    股市
    增持
    成本
    乳业
    万吨
    train_data_frame.corpus[0] 
     [(0, 6), (1, 1), (2, 1), (3, 3), (4, 2), (5, 2), (6, 1), (7, 1), (8, 2), (9, 1), (10, 3), (11, 1), (12, 2), (13, 2), (14, 2), (15, 1), (16, 1), (17, 2), (18, 1), (19, 1), (20, 2), (21, 1), (22, 2), (23, 2), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1), (29, 2), (30, 3), (31, 4), (32, 3), (33, 1), (34, 1), (35, 1), (36, 7), (37, 1), (38, 1), (39, 2), (40, 3), (41, 1), (42, 1), (43, 1), (44, 1), (45, 2), (46, 1), (47, 1), (48, 1), (49, 2), (50, 4), (51, 21), (52, 3), (53, 7), (54, 1), (55, 2), (56, 1), (57, 4), (58, 2), (59, 1), (60, 5), (61, 1), (62, 1), (63, 1), (64, 2), (65, 1), (66, 3), (67, 1), (68, 2), (69, 2), (70, 1), (71, 1), (72, 1), (73, 1), (74, 2), (75, 1), (76, 1), (77, 1), (78, 1), (79, 2), (80, 1), (81, 1), (82, 1), (83, 4), (84, 7), (85, 2), (86, 3), (87, 1), (88, 9), (89, 1), (90, 1), (91, 8), (92, 3), (93, 1), (94, 4), (95, 1), (96, 2), (97, 1), (98, 7), (99, 1), (100, 2), (101, 1), (102, 1), (103, 1), (104, 1), (105, 1), (106, 1), (107, 1), (108, 1), (109, 2), (110, 1), (111, 2), (112, 1), (113, 1), (114, 1), (115, 1), (116, 1), (117, 1), (118, 1), (119, 1), (120, 1), (121, 2), (122, 1), (123, 1), (124, 1), (125, 1), (126, 5), (127, 1), (128, 4), (129, 1), (130, 1), (131, 1), (132, 2), (133, 2), (134, 1), (135, 5), (136, 1), (137, 1), (138, 3), (139, 1), (140, 1), (141, 1), (142, 1), (143, 1), (144, 1), (145, 2), (146, 1), (147, 1), (148, 2), (149, 4), (150, 1), (151, 1), (152, 2), (153, 2), (154, 1), (155, 3), (156, 1), (157, 1), (158, 1), (159, 1), (160, 1), (161, 2), (162, 1), (163, 1), (164, 1), (165, 2), (166, 1), (167, 3), (168, 1), (169, 1), (170, 3), (171, 3), (172, 1), (173, 2), (174, 1), (175, 1), (176, 2), (177, 5), (178, 1), (179, 1), (180, 1), (181, 1), (182, 1), (183, 1), (184, 4), (185, 1), (186, 1), (187, 1), (188, 1), (189, 3), (190, 1), (191, 14), (192, 2), (193, 2), (194, 2), (195, 1), (196, 3), (197, 1), (198, 1), (199, 11), (200, 6), (201, 1), (202, 1), (203, 2), (204, 1), (205, 8), (206, 2), (207, 2), (208, 2), (209, 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(508, 1), (509, 1), (510, 1), (511, 1), (512, 1), (513, 1), (514, 2), (515, 1), (516, 3), (517, 4), (518, 1), (519, 1), (520, 1), (521, 1), (522, 1), (523, 1), (524, 1), (525, 1), (526, 2), (527, 2), (528, 1), (529, 1), (530, 1), (531, 1), (532, 1), (533, 1), (534, 1), (535, 2), (536, 5), (537, 2), (538, 1), (539, 1), (540, 1), (541, 7), (542, 1), (543, 1), (544, 1), (545, 2), (546, 1), (547, 3), (548, 2), (549, 1), (550, 1), (551, 2), (552, 1), (553, 2), (554, 1), (555, 1), (556, 2), (557, 1), (558, 2), (559, 5), (560, 2), (561, 1), (562, 1), (563, 1), (564, 1), (565, 1), (566, 1), (567, 7), (568, 2), (569, 1), (570, 2), (571, 1), (572, 1), (573, 1), (574, 4), (575, 1), (576, 2), (577, 2), (578, 1), (579, 2), (580, 1), (581, 1), (582, 1), (583, 2), (584, 1), (585, 1), (586, 1), (587, 4), (588, 1), (589, 4), (590, 2), (591, 1), (592, 1), (593, 1), (594, 2), (595, 1), (596, 1), (597, 1), (598, 1), (599, 1), (600, 1), (601, 1), (602, 1), (603, 1), (604, 1), (605, 1), (606, 1), (607, 1), (608, 2), (609, 1), (610, 2), (611, 1), (612, 1), (613, 11), (614, 1), (615, 1), (616, 3), (617, 1), (618, 1), (619, 1), (620, 1), (621, 1), (622, 1), (623, 1), (624, 32), (625, 2), (626, 1), (627, 8), (628, 1), (629, 3), (630, 3), (631, 1), (632, 1), (633, 4), (634, 1), (635, 1), (636, 2), (637, 1), (638, 3), (639, 2), (640, 1), (641, 1), (642, 1), (643, 3), (644, 5), (645, 4), (646, 1), (647, 1), (648, 3), (649, 1), (650, 1), (651, 1), (652, 1), (653, 1), (654, 1), (655, 2), (656, 1), (657, 7), (658, 1), (659, 2), (660, 1), (661, 2), (662, 1), (663, 1), (664, 1), (665, 1), (666, 1), (667, 1), (668, 4), (669, 1), (670, 1), (671, 3), (672, 1), (673, 1), (674, 2), (675, 1), (676, 1), (677, 1), (678, 1), (679, 1), (680, 2), (681, 2), (682, 1), (683, 1), (684, 1), (685, 3), (686, 1), (687, 1), (688, 1), (689, 1), (690, 4), (691, 1), (692, 2), (693, 3), (694, 1), (695, 2), (696, 1), (697, 1), (698, 2), (699, 1), (700, 1), (701, 4), (702, 1), (703, 1), (704, 2), (705, 1), (706, 1), (707, 1), (708, 1), (709, 2), (710, 1), (711, 3), (712, 1), (713, 1), (714, 4), (715, 1), (716, 1), (717, 1), (718, 2), (719, 1), (720, 1), (721, 2), (722, 1), (723, 1), (724, 4), (725, 1), (726, 1), (727, 1), (728, 1), (729, 2), (730, 12), (731, 2), (732, 1), (733, 2), (734, 3), (735, 1), (736, 26), (737, 1), (738, 5), (739, 1), (740, 2), (741, 5), (742, 2), (743, 3), (744, 3), (745, 2), (746, 1), (747, 3), (748, 2), (749, 2), (750, 2), (751, 1), (752, 1), (753, 2), (754, 1), (755, 1), (756, 1), (757, 1), (758, 1), (759, 4), (760, 1), (761, 1), (762, 1), (763, 1), (764, 1), (765, 2), (766, 1), (767, 1), (768, 1), (769, 2), (770, 8), (771, 2), (772, 4), (773, 1), (774, 8), (775, 3), (776, 1), (777, 1), (778, 3), (779, 1), (780, 1), (781, 1), (782, 5), (783, 2), (784, 2), (785, 1), (786, 4), (787, 1), (788, 1), (789, 1), (790, 1), (791, 1), (792, 1), (793, 4), (794, 1), (795, 1), (796, 1), (797, 5), (798, 3), (799, 5), (800, 3), (801, 1), (802, 1), (803, 1), (804, 1), (805, 2), (806, 2), (807, 2), (808, 1), (809, 1), (810, 1), (811, 1), (812, 1), (813, 1), (814, 1), (815, 3), (816, 1), (817, 2), (818, 1), (819, 1), (820, 11), (821, 1), (822, 1), (823, 2), (824, 3), (825, 1), (826, 1), (827, 1), (828, 1), (829, 1), (830, 3), (831, 4), (832, 46), (833, 1), (834, 1), (835, 2), (836, 2), (837, 1), (838, 1), (839, 2), (840, 2), (841, 1), (842, 1), (843, 2), (844, 2), (845, 2), (846, 1), (847, 1), (848, 2), (849, 1), (850, 1), (851, 1), (852, 3), (853, 1), (854, 1), (855, 6), (856, 1), (857, 1), (858, 1)]
    [33. 74. 73. 31. 47. 48.]
    <class 'numpy.ndarray'>
    SModel_acc_score： 0.8114374034003091
    kNNC_acc_score： 0.8160741885625966
    GNBC_acc_score： 0.6352395672333848
    MNBC_acc_score： 0.6352395672333848
    BNBC_acc_score： 0.29675425038639874
    LDAC_acc_score： 0.8238021638330757
    PerceptronC_acc_score： 0.8222565687789799

## 核心代码 ##

class GaussianNB Found at: sklearn.naive_bayes
    
    class GaussianNB(_BaseNB):
        """
        Gaussian Naive Bayes (GaussianNB)
        
        Can perform online updates to model parameters via :meth:`partial_fit`.
        For details on algorithm used to update feature means and variance online,
        see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
        
        http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf
        
        Read more in the :ref:`User Guide <gaussian_naive_bayes>`.
        
        Parameters
        ----------
        priors : array-like of shape (n_classes,)
        Prior probabilities of the classes. If specified the priors are not
        adjusted according to the data.
        
        var_smoothing : float, default=1e-9
        Portion of the largest variance of all features that is added to
        variances for calculation stability.
        
        .. versionadded:: 0.20
        
        Attributes
        ----------
        class_count_ : ndarray of shape (n_classes,)
        number of training samples observed in each class.
        
        class_prior_ : ndarray of shape (n_classes,)
        probability of each class.
        
        classes_ : ndarray of shape (n_classes,)
        class labels known to the classifier
        
        epsilon_ : float
        absolute additive value to variances
        
        sigma_ : ndarray of shape (n_classes, n_features)
        variance of each feature per class
        
        theta_ : ndarray of shape (n_classes, n_features)
        mean of each feature per class
        
        Examples
        --------
        >>> import numpy as np
        >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
        >>> Y = np.array([1, 1, 1, 2, 2, 2])
        >>> from sklearn.naive_bayes import GaussianNB
        >>> clf = GaussianNB()
        >>> clf.fit(X, Y)
        GaussianNB()
        >>> print(clf.predict([[-0.8, -1]]))
        [1]
        >>> clf_pf = GaussianNB()
        >>> clf_pf.partial_fit(X, Y, np.unique(Y))
        GaussianNB()
        >>> print(clf_pf.predict([[-0.8, -1]]))
        [1]
        """
        @_deprecate_positional_args
        def __init__(self, *, priors=None, var_smoothing=1e-9):
            self.priors = priors
            self.var_smoothing = var_smoothing
        
        def fit(self, X, y, sample_weight=None):
            """Fit Gaussian Naive Bayes according to X, y
    
            Parameters
            ----------
            X : array-like of shape (n_samples, n_features)
                Training vectors, where n_samples is the number of samples
                and n_features is the number of features.
    
            y : array-like of shape (n_samples,)
                Target values.
    
            sample_weight : array-like of shape (n_samples,), default=None
                Weights applied to individual samples (1. for unweighted).
    
                .. versionadded:: 0.17
                   Gaussian Naive Bayes supports fitting with *sample_weight*.
    
            Returns
            -------
            self : object
            """
            X, y = self._validate_data(X, y)
            y = column_or_1d(y, warn=True)
            return self._partial_fit(X, y, np.unique(y), _refit=True, 
                sample_weight=sample_weight)
        
        def _check_X(self, X):
            return check_array(X)
        
        @staticmethod
        def _update_mean_variance(n_past, mu, var, X, sample_weight=None):
            """Compute online update of Gaussian mean and variance.
    
            Given starting sample count, mean, and variance, a new set of
            points X, and optionally sample weights, return the updated mean and
            variance. (NB - each dimension (column) in X is treated as independent
            -- you get variance, not covariance).
    
            Can take scalar mean and variance, or vector mean and variance to
            simultaneously update a number of independent Gaussians.
    
            See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and 
             LeVeque:
    
            http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf
    
            Parameters
            ----------
            n_past : int
                Number of samples represented in old mean and variance. If sample
                weights were given, this should contain the sum of sample
                weights represented in old mean and variance.
    
            mu : array-like of shape (number of Gaussians,)
                Means for Gaussians in original set.
    
            var : array-like of shape (number of Gaussians,)
                Variances for Gaussians in original set.
    
            sample_weight : array-like of shape (n_samples,), default=None
                Weights applied to individual samples (1. for unweighted).
    
            Returns
            -------
            total_mu : array-like of shape (number of Gaussians,)
                Updated mean for each Gaussian over the combined set.
    
            total_var : array-like of shape (number of Gaussians,)
                Updated variance for each Gaussian over the combined set.
            """
            if X.shape[0] == 0:
                return mu, var
            # Compute (potentially weighted) mean and variance of new datapoints
            if sample_weight is not None:
                n_new = float(sample_weight.sum())
                new_mu = np.average(X, axis=0, weights=sample_weight)
                new_var = np.average((X - new_mu) ** 2, axis=0, 
                 weights=sample_weight)
            else:
                n_new = X.shape[0]
                new_var = np.var(X, axis=0)
                new_mu = np.mean(X, axis=0)
            if n_past == 0:
                return new_mu, new_var
            n_total = float(n_past + n_new)
            # Combine mean of old and new data, taking into consideration
            # (weighted) number of observations
            total_mu = (n_new * new_mu + n_past * mu) / n_total
            # Combine variance of old and new data, taking into consideration
            # (weighted) number of observations. This is achieved by combining
            # the sum-of-squared-differences (ssd)
            old_ssd = n_past * var
            new_ssd = n_new * new_var
            total_ssd = old_ssd + new_ssd + (n_new * n_past / n_total) * (mu - 
             new_mu) ** 2
            total_var = total_ssd / n_total
            return total_mu, total_var
        
        def partial_fit(self, X, y, classes=None, sample_weight=None):
            """Incremental fit on a batch of samples.
    
            This method is expected to be called several times consecutively
            on different chunks of a dataset so as to implement out-of-core
            or online learning.
    
            This is especially useful when the whole dataset is too big to fit in
            memory at once.
    
            This method has some performance and numerical stability overhead,
            hence it is better to call partial_fit on chunks of data that are
            as large as possible (as long as fitting in the memory budget) to
            hide the overhead.
    
            Parameters
            ----------
            X : array-like of shape (n_samples, n_features)
                Training vectors, where n_samples is the number of samples and
                n_features is the number of features.
    
            y : array-like of shape (n_samples,)
                Target values.
    
            classes : array-like of shape (n_classes,), default=None
                List of all the classes that can possibly appear in the y vector.
    
                Must be provided at the first call to partial_fit, can be omitted
                in subsequent calls.
    
            sample_weight : array-like of shape (n_samples,), default=None
                Weights applied to individual samples (1. for unweighted).
    
                .. versionadded:: 0.17
    
            Returns
            -------
            self : object
            """
            return self._partial_fit(X, y, classes, _refit=False, 
                sample_weight=sample_weight)
        
        def _partial_fit(self, X, y, classes=None, _refit=False, 
            sample_weight=None):
            """Actual implementation of Gaussian NB fitting.
    
            Parameters
            ----------
            X : array-like of shape (n_samples, n_features)
                Training vectors, where n_samples is the number of samples and
                n_features is the number of features.
    
            y : array-like of shape (n_samples,)
                Target values.
    
            classes : array-like of shape (n_classes,), default=None
                List of all the classes that can possibly appear in the y vector.
    
                Must be provided at the first call to partial_fit, can be omitted
                in subsequent calls.
    
            _refit : bool, default=False
                If true, act as though this were the first time we called
                _partial_fit (ie, throw away any past fitting and start over).
    
            sample_weight : array-like of shape (n_samples,), default=None
                Weights applied to individual samples (1. for unweighted).
    
            Returns
            -------
            self : object
            """
            X, y = check_X_y(X, y)
            if sample_weight is not None:
                sample_weight = _check_sample_weight(sample_weight, X)
            # If the ratio of data variance between dimensions is too small, it
            # will cause numerical errors. To address this, we artificially
            # boost the variance by epsilon, a small fraction of the standard
            # deviation of the largest dimension.
            self.epsilon_ = self.var_smoothing * np.var(X, axis=0).max()
            if _refit:
                self.classes_ = None
            if _check_partial_fit_first_call(self, classes):
                # This is the first call to partial_fit:
                # initialize various cumulative counters
                n_features = X.shape[1]
                n_classes = len(self.classes_)
                self.theta_ = np.zeros((n_classes, n_features))
                self.sigma_ = np.zeros((n_classes, n_features))
                self.class_count_ = np.zeros(n_classes, dtype=np.float64)
                # Initialise the class prior
                # Take into account the priors
                if self.priors is not None:
                    priors = np.asarray(self.priors)
                    # Check that the provide prior match the number of classes
                    if len(priors) != n_classes:
                        raise ValueError('Number of priors must match number of'
                            ' classes.')
                    # Check that the sum is 1
                    if not np.isclose(priors.sum(), 1.0):
                        raise ValueError('The sum of the priors should be 1.') # Check that 
                         the prior are non-negative
                    if (priors < 0).any():
                        raise ValueError('Priors must be non-negative.')
                    self.class_prior_ = priors
                else:
                    self.class_prior_ = np.zeros(len(self.classes_), 
                        dtype=np.float64) # Initialize the priors to zeros for each class
            else:
                if X.shape[1] != self.theta_.shape[1]:
                    msg = "Number of features %d does not match previous data %d."
                    raise ValueError(msg % (X.shape[1], self.theta_.shape[1]))
                # Put epsilon back in each time
                ::]self.epsilon_
            self.sigma_[ -= 
            classes = self.classes_
            unique_y = np.unique(y)
            unique_y_in_classes = np.in1d(unique_y, classes)
            if not np.all(unique_y_in_classes):
                raise ValueError("The target label(s) %s in y do not exist in the "
                    "initial classes %s" % 
                    (unique_y[~unique_y_in_classes], classes))
            for y_i in unique_y:
                i = classes.searchsorted(y_i)
                X_i = X[y == y_i:]
                if sample_weight is not None:
                    sw_i = sample_weight[y == y_i]
                    N_i = sw_i.sum()
                else:
                    sw_i = None
                    N_i = X_i.shape[0]
                new_theta, new_sigma = self._update_mean_variance(
                    self.class_count_[i], self.theta_[i:], self.sigma_[i:], 
                    X_i, sw_i)
                self.theta_[i:] = new_theta
                self.sigma_[i:] = new_sigma
                self.class_count_[i] += N_i
            
            self.sigma_[::] += self.epsilon_
            # Update if only no priors is provided
            if self.priors is None:
                # Empirical prior, with sample_weight taken into account
                self.class_prior_ = self.class_count_ / self.class_count_.sum()
            return self
        
        def _joint_log_likelihood(self, X):
            joint_log_likelihood = []
            for i in range(np.size(self.classes_)):
                jointi = np.log(self.class_prior_[i])
                n_ij = -0.5 * np.sum(np.log(2. * np.pi * self.sigma_[i:]))
                n_ij -= 0.5 * np.sum(((X - self.theta_[i:]) ** 2) / 
                    (self.sigma_[i:]), 1)
                joint_log_likelihood.append(jointi + n_ij)
            
            joint_log_likelihood = np.array(joint_log_likelihood).T
            return joint_log_likelihood
    
    
    
    class MultinomialNB Found at: sklearn.naive_bayes
    
    class MultinomialNB(_BaseDiscreteNB):
        """
        Naive Bayes classifier for multinomial models
        
        The multinomial Naive Bayes classifier is suitable for classification with
        discrete features (e.g., word counts for text classification). The
        multinomial distribution normally requires integer feature counts. However,
        in practice, fractional counts such as tf-idf may also work.
        
        Read more in the :ref:`User Guide <multinomial_naive_bayes>`.
        
        Parameters
        ----------
        alpha : float, default=1.0
        Additive (Laplace/Lidstone) smoothing parameter
        (0 for no smoothing).
        
        fit_prior : bool, default=True
        Whether to learn class prior probabilities or not.
        If false, a uniform prior will be used.
        
        class_prior : array-like of shape (n_classes,), default=None
        Prior probabilities of the classes. If specified the priors are not
        adjusted according to the data.
        
        Attributes
        ----------
        class_count_ : ndarray of shape (n_classes,)
        Number of samples encountered for each class during fitting. This
        value is weighted by the sample weight when provided.
        
        class_log_prior_ : ndarray of shape (n_classes, )
        Smoothed empirical log probability for each class.
        
        classes_ : ndarray of shape (n_classes,)
        Class labels known to the classifier
        
        coef_ : ndarray of shape (n_classes, n_features)
        Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
        as a linear model.
        
        feature_count_ : ndarray of shape (n_classes, n_features)
        Number of samples encountered for each (class, feature)
        during fitting. This value is weighted by the sample weight when
        provided.
        
        feature_log_prob_ : ndarray of shape (n_classes, n_features)
        Empirical log probability of features
        given a class, ``P(x_i|y)``.
        
        intercept_ : ndarray of shape (n_classes, )
        Mirrors ``class_log_prior_`` for interpreting MultinomialNB
        as a linear model.
        
        n_features_ : int
        Number of features of each sample.
        
        Examples
        --------
        >>> import numpy as np
        >>> rng = np.random.RandomState(1)
        >>> X = rng.randint(5, size=(6, 100))
        >>> y = np.array([1, 2, 3, 4, 5, 6])
        >>> from sklearn.naive_bayes import MultinomialNB
        >>> clf = MultinomialNB()
        >>> clf.fit(X, y)
        MultinomialNB()
        >>> print(clf.predict(X[2:3]))
        [3]
        
        Notes
        -----
        For the rationale behind the names `coef_` and `intercept_`, i.e.
        naive Bayes as a linear classifier, see J. Rennie et al. (2003),
        Tackling the poor assumptions of naive Bayes text classifiers, ICML.
        
        References
        ----------
        C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
        Information Retrieval. Cambridge University Press, pp. 234-265.
        https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-
         classification-1.html
        """
        @_deprecate_positional_args
        def __init__(self, *, alpha=1.0, fit_prior=True, class_prior=None):
            self.alpha = alpha
            self.fit_prior = fit_prior
            self.class_prior = class_prior
        
        def _more_tags(self):
            return {'requires_positive_X':True}
        
        def _count(self, X, Y):
            """Count and smooth feature occurrences."""
            check_non_negative(X, "MultinomialNB (input X)")
            self.feature_count_ += safe_sparse_dot(Y.T, X)
            self.class_count_ += Y.sum(axis=0)
        
        def _update_feature_log_prob(self, alpha):
            """Apply smoothing to raw counts and recompute log probabilities"""
            smoothed_fc = self.feature_count_ + alpha
            smoothed_cc = smoothed_fc.sum(axis=1)
            self.feature_log_prob_ = np.log(smoothed_fc) - np.log(smoothed_cc.
             reshape(-1, 1))
        
        def _joint_log_likelihood(self, X):
            """Calculate the posterior log probability of the samples X"""
            return safe_sparse_dot(X, self.feature_log_prob_.T) + self.class_log_prior_
    
    
    
    
    
    class BernoulliNB Found at: sklearn.naive_bayes
    
    class BernoulliNB(_BaseDiscreteNB):
        """Naive Bayes classifier for multivariate Bernoulli models.
        
        Like MultinomialNB, this classifier is suitable for discrete data. The
        difference is that while MultinomialNB works with occurrence counts,
        BernoulliNB is designed for binary/boolean features.
        
        Read more in the :ref:`User Guide <bernoulli_naive_bayes>`.
        
        Parameters
        ----------
        alpha : float, default=1.0
        Additive (Laplace/Lidstone) smoothing parameter
        (0 for no smoothing).
        
        binarize : float or None, default=0.0
        Threshold for binarizing (mapping to booleans) of sample features.
        If None, input is presumed to already consist of binary vectors.
        
        fit_prior : bool, default=True
        Whether to learn class prior probabilities or not.
        If false, a uniform prior will be used.
        
        class_prior : array-like of shape (n_classes,), default=None
        Prior probabilities of the classes. If specified the priors are not
        adjusted according to the data.
        
        Attributes
        ----------
        class_count_ : ndarray of shape (n_classes)
        Number of samples encountered for each class during fitting. This
        value is weighted by the sample weight when provided.
        
        class_log_prior_ : ndarray of shape (n_classes)
        Log probability of each class (smoothed).
        
        classes_ : ndarray of shape (n_classes,)
        Class labels known to the classifier
        
        feature_count_ : ndarray of shape (n_classes, n_features)
        Number of samples encountered for each (class, feature)
        during fitting. This value is weighted by the sample weight when
        provided.
        
        feature_log_prob_ : ndarray of shape (n_classes, n_features)
        Empirical log probability of features given a class, P(x_i|y).
        
        n_features_ : int
        Number of features of each sample.
        
        Examples
        --------
        >>> import numpy as np
        >>> rng = np.random.RandomState(1)
        >>> X = rng.randint(5, size=(6, 100))
        >>> Y = np.array([1, 2, 3, 4, 4, 5])
        >>> from sklearn.naive_bayes import BernoulliNB
        >>> clf = BernoulliNB()
        >>> clf.fit(X, Y)
        BernoulliNB()
        >>> print(clf.predict(X[2:3]))
        [3]
        
        References
        ----------
        C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
        Information Retrieval. Cambridge University Press, pp. 234-265.
        https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-
         model-1.html
        
        A. McCallum and K. Nigam (1998). A comparison of event models 
         for naive
        Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning 
         for
        Text Categorization, pp. 41-48.
        
        V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering 
         with
        naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam 
         (CEAS).
        """
        @_deprecate_positional_args
        def __init__(self, *, alpha=1.0, binarize=.0, fit_prior=True, 
            class_prior=None):
            self.alpha = alpha
            self.binarize = binarize
            self.fit_prior = fit_prior
            self.class_prior = class_prior
        
        def _check_X(self, X):
            X = super()._check_X(X)
            if self.binarize is not None:
                X = binarize(X, threshold=self.binarize)
            return X
        
        def _check_X_y(self, X, y):
            X, y = super()._check_X_y(X, y)
            if self.binarize is not None:
                X = binarize(X, threshold=self.binarize)
            return X, y
        
        def _count(self, X, Y):
            """Count and smooth feature occurrences."""
            self.feature_count_ += safe_sparse_dot(Y.T, X)
            self.class_count_ += Y.sum(axis=0)
        
        def _update_feature_log_prob(self, alpha):
            """Apply smoothing to raw counts and recompute log 
             probabilities"""
            smoothed_fc = self.feature_count_ + alpha
            smoothed_cc = self.class_count_ + alpha * 2
            self.feature_log_prob_ = np.log(smoothed_fc) - np.log
             (smoothed_cc.reshape(-1, 1))
        
        def _joint_log_likelihood(self, X):
            """Calculate the posterior log probability of the samples X"""
            n_classes, n_features = self.feature_log_prob_.shape
            n_samples, n_features_X = X.shape
            if n_features_X != n_features:
                raise ValueError(
                    "Expected input with %d features, got %d instead" % 
                     (n_features, n_features_X))
            neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
            # Compute  neg_prob · (1 - X).T  as  ∑neg_prob - X · neg_prob
            jll = safe_sparse_dot(X, (self.feature_log_prob_ - neg_prob).T)
            jll += self.class_log_prior_ + neg_prob.sum(axis=1)
            return jll

[ML_NB_news_kNN_LDA]: https://yunyaniu.blog.csdn.net/article/details/111565297
[ML_NB_news_kNN_LDA 1]: https://yunyaniu.blog.csdn.net/article/details/111565944
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[https_download.csdn.net_download_qq_41185868_13757777]: https://download.csdn.net/download/qq_41185868/13757777
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