机器学习之朴素贝叶斯分类算法[亲测有效]一、数学知识相关1.独立事件–前提2.条件概率3.全概率公式4.贝叶斯公式5.朴素贝叶斯公式其中:P(A)叫做A事件的先验概率,即一般情况下,认为A发生的概率。P(B|A)叫做似然度,是A假设条件成立的情况下发生B的概率。P(A|B)叫做后验概率,在B发生的情况下发生A的概率,也就是要求的概率。P(B)叫做标准化常量,即在一般情况下,认为B…
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一、数学知识相关
2.条件概率
3.全概率公式
4.贝叶斯公式
5.朴素贝叶斯公式
其中:
- P(A)叫做A事件的先验概率,即一般情况下,认为A发生的概率。
- P(B|A)叫做似然度,是A假设条件成立的情况下发生B的概率。
- P(A|B)叫做后验概率,在B发生的情况下发生A的概率,也就是要求的概率。P(B)叫做标准化常量,即在一般情况下,认为B发生的概率。
朴素贝叶斯分类算法:朴素贝叶斯(Naive Bayes ,NB)算法是基于贝叶斯定理与特征条件独立假设的分类方法,该算法是有监督的学习算法,解决的是分类问题,是将一个未知样本分到几个预先已知类别的过程。朴素贝叶斯的思想就是根据某些个先验概率计算Y变量属于某个类别的后验概率,也就是根据先前事件的有关数据估计未来某个事件发生的概率。
二、理解朴素贝叶斯
假设现在有一堆邮件,正常邮件的比例是80%,垃圾邮件的比例是20%,这堆邮件中,5%的邮件中出现Viagra单词,如果有一封邮件,这封邮件中包含Viagra单词,求这封邮件是垃圾邮件的概率。
显然不能使用5%*20%=1%得到这封邮件是垃圾邮件的概率,因为垃圾邮件中有可能出现Viagra也有可能不会出现Viagra单词。那么根据贝叶斯公式可得包含Viagra单词的邮件是垃圾邮件的概率为:
通过同样的计算可以得到,含有Viagra单词但不是垃圾邮件的概率为0.2。那么可以认为这封邮件是垃圾邮件的概率比较大。这里的Viagra可以理解为邮件中的一个特征。那么当一封邮件有额外更多的特征时,贝叶斯如何扩展?
假设所有历史邮件中只出现了4个单词,也就是4个特征,根据历史邮件统计的单词频率表如下:
假设现在给定一封邮件中有Viagra和Unsubscribe(取消订阅)两个单词,求这封邮件是垃圾邮件的概率、不是垃圾邮件的概率?
利用贝叶斯公式,我们可以得到:
三、拉普拉斯估计
根据以上例子,假设有一封邮件这4个单词都出现了,求这封邮件是垃圾邮件的概率:
由于的概率为0/20,会导致整个结果是垃圾邮件的概率为0,那么就否定了其他单词出现的权重。
拉普拉斯估计本质上是给频率表中的每个单词的计数加上一个较小的数,这样就保证每一类中每个特征发生的概率非零。通常,拉普拉斯估计中加上的数值为1,这样就保证了每一个特征至少在数据中出现一次。
四、Python 贝叶斯案例
import osimport sys# codecs 编码转换模块import codecsif __name__ == '__main__': # 读取文本构建语料库 corpus = [] labels = [] corpus_test = [] labels_test = [] f = codecs.open("./sms_spam.txt", "rb") count = 0 while True: # readline() 方法用于从文件读取整行,包括 "\n" 字符。 line = f.readline().decode("utf-8") # 读取第一行,第一行数据是列头,不统计 if count == 0: count = count + 1 continue if line: count = count + 1 line = line.split(",") label = line[0] sentence = line[1] corpus.append(sentence) if "ham" == label: labels.append(0) elif "spam" == label: labels.append(1) if count > 5550: corpus_test.append(sentence) if "ham" == label: labels_test.append(0) elif "spam" == label: labels_test.append(1) else: break # 文本特征提取:skl # 将文本数据转化成特征向量的过程 # 比较常用的文本特征表示法为词袋法 # # 词袋法: # 不考虑词语出现的顺序,每个出现过的词汇单独作为一列特征 # 这些不重复的特征词汇集合为词表 # 每一个文本都可以在很长的词表上统计出一个很多列的特征向量 # CountVectorizer是将文本向量转换成稀疏表示数值向量(字符频率向量) vectorizer 将文档词块化,只考虑词汇在文本中出现的频率 # 词袋 vectorizer = CountVectorizer() # 每行的词向量,fea_train是一个矩阵 fea_train = vectorizer.fit_transform(corpus) print("vectorizer.get_feature_names is ", vectorizer.get_feature_names()) print("fea_train is ", fea_train.toarray()) # vocabulary=vectorizer.vocabulary_ 只计算上面vectorizer中单词的tf(term frequency 词频) vectorizer2 = CountVectorizer(vocabulary=vectorizer.vocabulary_) fea_test = vectorizer2.fit_transform(corpus_test) # print vectorizer2.get_feature_names() # print fea_test.toarray() # create the Multinomial Naive Bayesian Classifier # alpha = 1 拉普拉斯估计给每个单词个数加1 clf = MultinomialNB(alpha=1) clf.fit(fea_train, labels) pred = clf.predict(fea_test); for p in pred: if p == 0: print("正常邮件") else: print("垃圾邮件")
测试结果:
C:\ProgramData\Anaconda3\python.exe F:/Python/001code/machinelearning/www/lxk/com/bayes.py*************************Naive Bayes*************************vectorizer.get_feature_names is ['00', '008704050406', '0089', '0121', '01223585236', '01223585334', '0125698789', '02', '0207', '02073162414', '02085076972', '021', '03', '04', '0430', '05', '050703', '0578', '06', '07', '07008009200', '07046744435', '07090201529', '07123456789', '0721072', '07732584351', '07734396839', '07742676969', '07753741225', '0776xxxxxxx', '07786200117', '077xxx', '078', '07801543489', '07808', '07808247860', '07808726822', '07815296484', '07821230901', '078498', '07880867867', '0789xxxxxxx', '07946746291', '0796xxxxxx', '07973788240', '07xxxxxxxxx', '08', '0800', '08000407165', '08000776320', '08000839402', '08000930705', '08000938767', '08001950382', '08002888812', '08002986030', '08002986906', '08002988890', '08006344447', '0808', '08081263000', '08081560665', '0825', '083', '0844', '08448350055', '08448714184', '0845', '08452810071', '08452810073', '08452810075over18', '0870', '08700435505150p', '08700621170150p', '08701213186', '08701237397', '08701417012', '0870141701216', '087016248', '087018728737', '08702490080', '08702840625', '08704439680', '08706091795', '0870737910216yrs', '08707500020', '08707509020', '0870753331018', '08707808226', '08708034412', '08708800282', '08709501522', '0871212025016', '08712300220', '087123002209am', '08712317606', '08712400200', '08712400602450p', '08712402050', '08712402578', '08712402779', '08712402902', '08712402972', '08712404000', '08712405020', '08712405022', '08712460324', '0871277810710p', '0871277810810', '0871277810910p', '08714342399', '087147123779am', '08714712379', '08714712388', '08714712394', '08714712412', '08715203028', '08715203649', '08715203652', '08715203656', '08715203677', '08715203685', '08715203694', '08715205273', '08715500022', '08715705022', '08717111821', '08717168528', '08717205546', '0871750', '08717507382', '08717509990', '08717890890', '08717898035', '08718711108', '08718720201', '08718723815', '08718725756', '08718726270', '08718727868', '08718727870', '08718727870150ppm', '08718730666', '08718738001', '08718738002', '08718738034', '08719180219', '08719180248', '08719181259', '08719181503', '08719181513', '08719839835', '08719899217', '08719899229', '08719899230', '09', '09050000332', '09050000460', '09050001295', '09050001808', '09050002311', '09050003091', '09050090044', '09050280520', '09053750005', '09056242159', '09058091854', '09058091870', '09058094454', '09058094455', '09058094507', '09058094565', '09058094583', '09058094594', '09058094597', '09058094599', '09058095107', '09058095201', '09058097189', '09058097218', '09058098002', '09061104276', '09061104283', '09061209465', '09061213237', '09061221061', '09061221066', '09061701444', '09061701461', '09061701851', '09061701939', '09061702893', '09061743386', '09061743806', '09061743810', '09061743811', '09061744553', '09061749602', '09061790121', '09061790125', '09061790126', '09063440451', '09063442151', '09063458130', '0906346330', '09064012103', '09064012160', '09064015307', '09064017295', '09064018838', '09064019014', '09064019788', '09065171142', '09065174042', '09065394514', '09065394973', '09066350750', '09066358152', '09066358361', '09066362231', '09066364311', '09066368327', '09066368753', '09066380611', '09066382422', '09066612661', '09066649731from', '09066660100', '09071512432', '09071512433', '09077818151', '09090204448', '09090900040', '09094100151', '09094646631', '09094646899', '09095350301', '09096102316', '09099725823', '09099726395', '09099726429', '09099726481', '09099726553', '09111030116', '09111032124', '09701213186', '0a', '0quit', '10', '100', '1000', '1000call', '1000s', '100p', '100percent', '1013', '1030', '10am', '10k', '10p', '10ppm', '11', '1120', '1131', '114', '116', '1172', '118p', '11mths', '11pm', '12', '1205', '120p', '121', '1225', '123', '125', '1250', '125gift', '128', '12hours', '12hrs', '12mths', '13', '130', '14', '140', '1405', '140ppm', '145', '1450', '146tf150p', '14tcr', '15', '150', '150p', '150p16', '150pm', '150ppm', '150ppmsg', '151', '15541', '15pm', '16', '165', '169', '177', '18', '180', '18p', '18yrs', '195', '1apple', '1b6a5ecef91ff9', '1cup', '1da', '1lemon', '1million', '1pm', '1st', '1st4terms', '1stchoice', '1stone', '1thing', '1tulsi', '1win150ppmx3', '1winawk', '20', '200', '2000', '2003', '2004', '2005', '2006', '2007', '200p', '2025050', '20m12aq', '20p', '21', '21870000', '21st', '22', '220cm2', '2309', '23f', '23g', '24', '24hrs', '24m', '24th', '25', '250', '250k', '255', '25p', '26', '2667', '26th', '27', '28', '2814032', '28days', '28th', '28thfeb', '29', '2b', '2c', '2day', '2end', '2exit', '2find', '2getha', '2geva', '2go', '2kbsubject', '2lands', '2marrow', '2moro', '2morow', '2morro', '2morrow', '2morrowxxxx', '2mro', '2mrw', '2nd', '2nhite', '2nite', '2optout', '2p', '2price', '2rcv', '2stop', '2stoptx', '2stoptxt', '2u', '2u2', '2waxsto', '2wks', '2wt', '2years', '2yr', '2yrs', '30', '300', '3000', '300603', '300603t', '300p', '3030', '30ish', '30pm', '30s', '30th', '31', '3100', '310303', '31p', '32', '32000', '3230', '326', '33', '330', '350', '3510i', '3650', '36504', '3680', '3750', '37819', '38', '391784', '3d', '3days', '3g', '3hrs', '3lions', '3lp', '3miles', '3mins', '3mobile', '3pound', '3qxj9', '3rd', '3uz', '3wks', '3xx', '40', '400', '400mins', '4041', '40411', '40533', '40gb', '41685', '41782', '420', '42049', '4217', '42478', '42810', '430', '434', '44', '440', '44345', '447797706009', '447801259231', '448712404000', '449050000301', '449071512431', '45', '450', '450pw', '45239', '45pm', '4742', '47per', '48', '4882', '48922', '49', '49557', '4a', '4d', '4eva', '4fil', '4get', '4goten', '4info', '4jx', '4msgs', '4mths', '4t', '4th', '4the', '4txt', '4u', '4utxt', '4w', '4xx26', '4years', '50', '500', '5000', '505060', '50award', '50p', '50perweeksub', '50perwksub', '50pm', '50pmmorefrommobile2bremoved', '50ppm', '50rcvd', '515', '5226', '530', '54', '542', '545', '5ish', '5min', '5mls', '5p', '5pm', '5th', '5wq', '600', '6031', '6089', '60p', '61200', '61610', '62220cncl', '6230', '62468', '62735', '630', '63miles', '645', '65', '650', '6669', '674', '67441233', '68866', '69101', '69200', '69669', '69696', '69698', '69855', '69876', '69888', '69888nyt', '69911', '69969', '69988', '6hrs', '6ish', '6missed', '6months', '6pm', '6th', '6times', '6wu', '6zf', '700', '71', '7250', '7250i', '730', '74355', '75', '750', '7548', '75max', '762', '7634', '7684', '77', '7732584351', '786', '7am', '7cfca1a', '7ish', '7mp', '7oz', '7pm', '7th', '7zs', '80', '800', '8000930705', '80062', '8007', '80082', '80086', '80160', '80182', '80488', '80608', '8077', '80878', '81151', '81303', '81618', '820554ad0a1705572711', '82242', '82277', '82324', '83021', '83039', '83110', '83118', '83222', '83332', '83338', '83355', '83370', '83383', '83435', '83600', '84025', '84128', '84199', '84484', '85', '850', '85023', '85069', '85233', '8552', '85555', '86021', '861', '864233', '86688', '86888', '87021', '87066', '87070', '87077', '87121', '87131', '8714714', '87239', '87575', '8800', '88039', '88066', '88222', '88600', '88800', '8883', '88877', '88888', '89034', '89070', '89080', '89105', '89123', '89555', '89693', '8am', '8ball', '8lb', '8pm', '8th', '900', '9061100010', '910', '930', '945', '946', '95', '97n7qp', '98321561', '99', '9ae', '9am', '9ja', '9pm', '9t', '9th', '____', 'a21', 'a30', 'aah', 'aaniye', 'aaooooright', 'aathi', 'ab', 'abbey', 'abdomen', 'abeg', 'abel', 'aberdeen', 'abi', 'ability', 'abiola', 'abj', 'able', 'abnormally', 'about', 'aboutas', 'above', 'absence', 'absolutely', 'absolutly', 'abstract', 'abt', 'abta', 'abuse', 'ac', 'academic', 'acc', 'accent', 'accenture', 'accept', 'access', 'accessible', 'accidant', 'accident', 'accidentally', 'accommodation', 'accommodationvouchers', 'accomodations', 'accordin', 'accordingly', 'account', 'accounting', 'accounts', 'achan', 'ache', 'achieve', 'acl03530150pm', 'acnt', 'across', 'act', 'acted', 'actin', 'action', 'activate', 'active', 'activities', 'actor', 'actual', 'actually', 'ad', 'adam', 'add', 'added', 'addie', 'adding', 'address', 'adds', 'adewale', 'adi', 'admin', 'administrator', 'admirer', 'adore', 'adoring', 'adp', 'adress', 'adrian', 'ads', 'adsense', 'adult', 'adults', 'advance', 'adventuring', 'advice', 'aeronautics', 'aeroplane', 'afew', 'affair', 'affairs', 'affectionate', 'affidavit', 'afford', 'afghanistan', 'afraid', 'africa', 'african', 'aft', 'after', 'afternon', 'afternoon', 'afterwards', 'aftr', 'ag', 'again', 'against', 'agalla', 'age', 'age16', 'agency', 'agent', 'agents', 'ages', 'agidhane', 'aging', 'ago', 'agree', 'ah', 'aha', 'ahead', 'ahhh', 'ahhhh', 'aids', 'aight', 'ain', 'aint', 'air', 'air1', 'airport', 'airtel', 'aiya', 'aiyah', 'aiyar', 'aiyo', 'ajith', 'aka', 'akon', 'al', 'alaipayuthe', 'albi', 'album', 'alcohol', 'aldrine', 'alert', 'alertfrom', 'alex', 'alfie', 'algarve', 'algebra', 'algorithms', 'ali', 'alian', 'alibi', 'alive', 'all', 'allah', 'allday', 'alle', 'allo', 'allow', 'allowed', 'almost', 'alone', 'along', 'already', 'alright', 'alrite', 'also', 'alter', 'alternative', 'although', 'alto18', 'aluable', 'always', 'alwys', 'am', 'amanda', 'amazing', 'ambrith', 'american', 'ami', 'amk', 'amla', 'ammae', 'amongst', 'amount', 'amplikater', 'amrca', 'amrita', 'ams', 'amt', 'amused', 'amy', 'an', 'ana', 'anal', 'analysis', 'anand', 'and', 'anderson', 'andre', 'andres', 'andros', 'angels', 'angry', 'animal', 'animation', 'anjie', 'anjola', 'anna', 'annie', 'anniversary', 'annoncement', 'announced', 'announcement', 'annoying', 'anonymous', 'another', 'ans', 'ansr', 'answer', 'answered', 'answerin', 'answering', 'answers', 'answr', 'antelope', 'antha', 'anthony', 'anti', 'antibiotic', 'any', 'anybody', 'anyhow', 'anymore', 'anyone', 'anyones', 'anyplaces', 'anythiing', 'anythin', 'anything', 'anythingtomorrow', 'anytime', 'anyway', 'anyways', 'anywhere', 'aom', 'apart', 'apartment', 'apes', 'aphex', 'apnt', 'apo', 'apologetic', 'apologise', 'apologize', 'apology', 'app', 'apparently', 'appeal', 'appear', 'appendix', 'applausestore', 'applebees', 'apples', 'application', 'apply', 'applyed', 'applying', 'appointment', 'appointments', 'appreciate', 'appreciated', 'approaches', 'approaching', 'appropriate', 'approve', 'approved', 'approx', 'apps', 'appt', 'appy', 'april', 'aptitude', 'aquarius', 'ar', 'arcade', 'archive', 'ard', 'are', 'area', 'aren', 'arent', 'arestaurant', 'aretaking', 'areyouunique', 'argh', 'argue', 'arguing', 'argument', 'arguments', 'arithmetic', 'arm', 'armand', 'arms', 'arng', 'arngd', 'arnt', 'around', 'arr', 'arrange', 'arranging', 'arrested', 'arrive', 'arrived', 'arrow', 'arsenal', 'art', 'artists', 'arts', 'arty', 'arun', 'as', 'asa', 'asap', 'ashes', 'ashley', 'ashwini', 'asia', 'asian', 'asjesus', 'ask', 'askd', 'asked', 'askin', 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'twenty', 'twice', 'twiggs', 'twinks', 'twins', 'twittering', 'two', 'txt', 'txt250', 'txt43', 'txtauction', 'txtin', 'txting', 'txtno', 'txts', 'txtstop', 'txttowin', 'tyler', 'type', 'typical', 'tyrone', 'u4', 'ubandu', 'ubi', 'ugh', 'ugo', 'uh', 'uhhhhrmm', 'uin', 'ujhhhhhhh', 'uk', 'ukp', 'uks', 'ultimate', 'ultimately', 'umma', 'ummma', 'ummmmmaah', 'un', 'unable', 'unbelievable', 'unbreakable', 'unclaimed', 'uncle', 'uncles', 'uncomfortable', 'unconditionally', 'unconscious', 'unconvinced', 'uncountable', 'under', 'understand', 'understanding', 'understood', 'underwear', 'undrstnd', 'undrstndng', 'uneventful', 'unfolds', 'unfortunately', 'unfortuntly', 'unhappiness', 'uni', 'unicef', 'uniform', 'unintentional', 'unintentionally', 'unique', 'units', 'univ', 'university', 'unknown', 'unless', 'unlike', 'unlimited', 'unnecessarily', 'unni', 'unrecognized', 'unredeemed', 'unsecured', 'unsold', 'unsub', 'unsubscribe', 'unsubscribed', 'until', 'up', 'up4', 'upcharge', 'upd8', 'updat', 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'violated', 'violence', 'violet', 'vip', 'vipclub4u', 'virgin', 'virgins', 'virtual', 'visa', 'visit', 'visiting', 'vitamin', 'vivek', 'vl', 'voda', 'vodafone', 'vodka', 'voice', 'voicemail', 'voila', 'volcanoes', 'vomit', 'vomiting', 'vote', 'voted', 'vouch4me', 'voucher', 'vouchers', 'vpod', 'vry', 'vth', 'vu', 'w1', 'w111wx', 'w1a', 'w1j6hl', 'w1jhl', 'w1t1jy', 'w4', 'w45wq', 'w8in', 'wa', 'waaaat', 'wad', 'wadebridge', 'wah', 'wahala', 'wahay', 'waheed', 'waheeda', 'waht', 'wait', 'waited', 'waitin', 'waiting', 'wake', 'waking', 'wales', 'waliking', 'walk', 'walkabout', 'walked', 'walkin', 'walking', 'walks', 'wall', 'wallpaper', 'walls', 'walmart', 'walsall', 'wamma', 'wan', 'wan2', 'wana', 'wanna', 'wannatell', 'want', 'want2come', 'wanted', 'wanting', 'wants', 'wap', 'waqt', 'warm', 'warned', 'warner', 'warning', 'warranty', 'was', 'washob', 'wasn', 'wasnt', 'waste', 'wasted', 'wasting', 'wat', 'watch', 'watched', 'watches', 'watchin', 'watching', 'watchng', 'water', 'watever', 'wating', 'watr', 'wats', 'watts', 'wave', 'wavering', 'waves', 'way', 'way2sms', 'waz', 'wc1n', 'wc1n3xx', 'we', 'weak', 'weakness', 'weaknesses', 'weapon', 'wear', 'wearing', 'weather', 'web', 'web2mobile', 'webadres', 'webeburnin', 'webpage', 'website', 'wed', 'weddin', 'wedding', 'weddingfriend', 'wednesday', 'wee', 'weed', 'week', 'weekdays', 'weekend', 'weekends', 'weekly', 'weeks', 'weigh', 'weighed', 'weight', 'weightloss', 'weird', 'weirdest', 'weirdo', 'weirdy', 'weiyi', 'welcome', 'welcomes', 'well', 'wellda', 'welp', 'wen', 'wendy', 'wenever', 'went', 'wenwecan', 'wer', 'were', 'werebored', 'weren', 'werethe', 'wesley', 'west', 'westlife', 'westonzoyland', 'westshore', 'wet', 'wetherspoons', 'wewa', 'whassup', 'what', 'whatever', 'whats', 'whatsup', 'wheellock', 'when', 'whenever', 'whenevr', 'whens', 'where', 'whereare', 'wherever', 'wherevr', 'wherre', 'whether', 'which', 'while', 'whilltake', 'whispers', 'white', 'whn', 'who', 'whole', 'whom', 'whor*', 'whos', 'whose', 'whr', 'why', 'wi', 'wicked', 'wicket', 'wid', 'widelive', 'wif', 'wife', 'wifi', 'wihtuot', 'wil', 'wildlife', 'will', 'willing', 'willpower', 'win', 'win150ppmx3age16', 'wind', 'window', 'windows', 'winds', 'windy', 'wine', 'wined', 'wings', 'wining', 'winner', 'winning', 'wins', 'winterstone', 'wipro', 'wire3', 'wisdom', 'wise', 'wish', 'wisheds', 'wishes', 'wishin', 'wishing', 'wishlist', 'wiskey', 'wit', 'with', 'withdraw', 'wither', 'within', 'without', 'witot', 'witout', 'wiv', 'wizzle', 'wk', 'wkend', 'wkent', 'wkg', 'wkly', 'wknd', 'wld', 'wml', 'wn', 'wnevr', 'wnt', 'woah', 'wocay', 'woke', 'woken', 'woman', 'womdarfull', 'women', 'won', 'wondar', 'wondarfull', 'wonder', 'wonderful', 'wondering', 'wonders', 'wont', 'woo', 'woodland', 'woods', 'woohoo', 'woot', 'woould', 'word', 'words', 'work', 'workand', 'workin', 'working', 'workout', 'works', 'world', 'worlds', 'worms', 'worried', 'worries', 'worry', 'worrying', 'worse', 'worth', 'worthless', 'wot', 'wotu', 'would', 'woulda', 'wouldn', 'wow', 'wrc', 'wrench', 'wrenching', 'wright', 'write', 'writhing', 'wrk', 'wrkin', 'wrking', 'wrks', 'wrld', 'wrnog', 'wrong', 'wrongly', 'wrote', 'ws', 'wtc', 'wtf', 'wth', 'wud', 'wudn', 'wuld', 'wun', 'www', 'wylie', 'x2', 'xam', 'xavier', 'xchat', 'xclusive', 'xin', 'xmas', 'xoxo', 'xt', 'xuhui', 'xx', 'xxuk', 'xxx', 'xxxmobilemovieclub', 'xxxx', 'xxxxx', 'xxxxxx', 'xxxxxxx', 'xxxxxxxx', 'xy', 'y87', 'ya', 'yah', 'yahoo', 'yalrigu', 'yalru', 'yan', 'yar', 'yavnt', 'yaxx', 'yay', 'yck', 'yeah', 'year', 'years', 'yeesh', 'yeh', 'yelling', 'yellow', 'yelow', 'yep', 'yer', 'yes', 'yest', 'yesterday', 'yet', 'yetty', 'yetunde', 'yi', 'yifeng', 'yijue', 'ym', 'yo', 'yoga', 'yogasana', 'yor', 'yorge', 'you', 'youdoing', 'young', 'younger', 'youphone', 'your', 'youre', 'yourinclusive', 'yourjob', 'yours', 'yourself', 'youuuuu', 'youwanna', 'yoville', 'yowifes', 'yoyyooo', 'yr', 'yrs', 'ystrday', 'ything', 'yummmm', 'yummy', 'yun', 'yunny', 'yuo', 'yuou', 'yup', 'yupz', 'zaher', 'zed', 'zeros', 'zhong', 'zogtorius', 'zoom', 'zouk', 'zyada', 'œharry']fea_train is [[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]]正常邮件正常邮件垃圾邮件正常邮件正常邮件正常邮件正常邮件正常邮件垃圾邮件正常邮件
四、Scala 贝叶斯案例
object Naive_bayes { def main(args: Array[String]) { //1 构建Spark对象 val conf = new SparkConf().setAppName("Naive_bayes").setMaster("local") val sc = new SparkContext(conf) //读取样本数据1 val data = sc.textFile("sample_naive_bayes_data.txt") val parsedData = data.map { line => val parts = line.split(',') LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' 、').map(_.toDouble))) } //样本数据划分训练样本与测试样本 val splits = parsedData.randomSplit(Array(0.9, 0.1), seed = 11L) val training = splits(0) val test = splits(1) //新建贝叶斯分类模型模型,并训练 ,lambda 拉普拉斯估计 val model = NaiveBayes.train(training, lambda = 1.0) //对测试样本进行测试 val predictionAndLabel = test.map(p => (model.predict(p.features), p.label)) val print_predict = predictionAndLabel.take(10020) println("prediction" + "\t" + "label") for (i <- 0 to print_predict.length - 1) { println(print_predict(i)._1 + "\t" + print_predict(i)._2) } val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() println(accuracy) //保存模型 val ModelPath = "naive_bayes_model" model.save(sc, ModelPath) val sameModel = NaiveBayesModel.load(sc, ModelPath) }}
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