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
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
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
代码中的数据集：https://download.csdn.net/download/qq_41185868/13757777
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
Unnamed: 0 content 0 0 牵动人心的雄安新区规划细节内容和出台时间表敲定。日前，北京商报记者从业内获悉，京津冀协同发...
1 1 去年以来，多个城市先后发布了多项楼市调控政策。在限购、限贷甚至限售的政策“组合拳”下，房地产...
2 2 在今年中国国际自行车展上，上海凤凰自行车总裁王朝阳表示，共享单车的到来把我们打懵了，影响更是...
3 3 25家上市银行迎来了一年一度的“分红季”，21世纪经济报道记者根据公开信息梳理发现，25家银...
4 4 说起卷饼，大家其实并不陌生，这个来自中原的传统美食，发展至今也衍生出各种各样的种类，卷边的制...
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)
Unnamed: 0 content 0 0 牵动人心的雄安新区规划细节内容和出台时间表敲定。日前，北京商报记者从业内获悉，京津冀协同发...
1 1 去年以来，多个城市先后发布了多项楼市调控政策。在限购、限贷甚至限售的政策“组合拳”下，房地产...
2 2 在今年中国国际自行车展上，上海凤凰自行车总裁王朝阳表示，共享单车的到来把我们打懵了，影响更是...
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']
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
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
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
Unnamed: 0 content 0 0 牵动人心的雄安新区规划细节内容和出台时间表敲定。日前，北京商报记者从业内获悉，京津冀协同发...
1 1 去年以来，多个城市先后发布了多项楼市调控政策。在限购、限贷甚至限售的政策“组合拳”下，房地产...
2 2 在今年中国国际自行车展上，上海凤凰自行车总裁王朝阳表示，共享单车的到来把我们打懵了，影响更是...
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']
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
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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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, 2799, 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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
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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
content 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，...
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
test_data_frame： 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...
738 738 骗子往往都很会讲故事，比如以下这些硅谷骗局：验血公司Theranos，号称只要从指尖抽几滴血...
511 511 专访 Whyd 创始人 孟崨在学校，他是最调皮，却又成绩最好的学生，让老师头疼不已。在公司，...
725 725 据介绍，喜马拉雅FM会员月费为18元，年度会员188元，价格与视频网站会员价格相仿。在会员福...
... ... ...
805 805 每经记者 王海慜 每经编辑 叶峰今日盘中，昨日领涨的中小创出现休整，而昨日暂时休整的一批龙头...
448 448 中国人买什么都喜欢大的，房子要买面积大的、手机要买屏大的，买车自然也是要挑选空间大的。抛开拉...
782 782 中证网讯 （记者 徐金忠）6月7日，国能电动汽车瑞典有限公司（NEVS）亮相CES亚洲消费电...
1264 1264 目前日系豪华品牌讴歌已经开启了国产之路，在推出CDX车型后，讴歌在国内的知名度一度飙升。CD...
1195 1195 近日有爆料称，乐视位于北京达美中心的办公地因未及时缴纳办公地费用已被停止物业一切服务；物业公...
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]
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(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 赞 (0)