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
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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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, 3094, 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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, 3651, 1536, 0, 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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]
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[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
