数据集获取:sms.tsv

Github: https://github.com/jalajthanaki/NLPython/blob/master/ch8/Spamflteringapplication/data/sms.tsv

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代码实现细节分析:

(1)导入包

import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import CountVectorizer

(2)加载数据并显示

#数据路径
path = '/home/stillxjy/NLP/sms.tsv'
#读取表格数据
sms = pd.read_table(path,header=None,names=['label','message'])
#数据的大小,行,列
print(sms.shape)
#前10条数据
print(sms.head(10))
#非垃圾邮件和垃圾邮件的数目
print(sms.label.value_counts())

输出:共有5572份邮件,每个邮件由label和message组成,非垃圾邮件4825份,垃圾邮件747份

(5572, 2)
  label                                            message
0   ham  Go until jurong point, crazy.. Available only ...
1   ham                      Ok lar... Joking wif u oni...
2  spam  Free entry in 2 a wkly comp to win FA Cup fina...
3   ham  U dun say so early hor... U c already then say...
4   ham  Nah I don't think he goes to usf, he lives aro...
5  spam  FreeMsg Hey there darling it's been 3 week's n...
6   ham  Even my brother is not like to speak with me. ...
7   ham  As per your request 'Melle Melle (Oru Minnamin...
8  spam  WINNER!! As a valued network customer you have...
9  spam  Had your mobile 11 months or more? U R entitle...
ham     4825
spam     747

(3)预处理:将标签转换为数字

#根据类别标签,添加数值类别标签数据,将ham转换为0 spam转换为1
sms['label_num'] = sms.label.map({'ham':0,'spam':1})
#添加数值类别数据后的前10条数据
print(sms.head(10))

输出:

Name: label, dtype: int64
  label                                            message  label_num
0   ham  Go until jurong point, crazy.. Available only ...          0
1   ham                      Ok lar... Joking wif u oni...          0
2  spam  Free entry in 2 a wkly comp to win FA Cup fina...          1
3   ham  U dun say so early hor... U c already then say...          0
4   ham  Nah I don't think he goes to usf, he lives aro...          0
5  spam  FreeMsg Hey there darling it's been 3 week's n...          1
6   ham  Even my brother is not like to speak with me. ...          0
7   ham  As per your request 'Melle Melle (Oru Minnamin...          0
8  spam  WINNER!! As a valued network customer you have...          1
9  spam  Had your mobile 11 months or more? U R entitle...          1

(4)数据集分割:将数据集分为训练集和测试集

X = sms.message
y = sms.label_num
print(X.shape)
print(y.shape)

#将整个数据分为训练集和数据集
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=1)
print('训练数据数目:{0}'.format(X_train.shape))
print('测试数据数目:{0}'.format(X_test.shape))
print('训练数据百分比: {0}'.format(X_train.shape[0]*1.0/X.shape[0]))

输出:

(5572,)
(5572,)
训练数据数目:(4179,)
测试数据数目:(1393,)
训练数据百分比: 0.75
输出矩阵中不为0的数据的情况:

(5)将文字数据,转换为频率矩阵数字数据。

#实例化
vect = CountVectorizer()

#获取所有邮件的单词表,并将邮件信息转换为频率矩阵
X_train_dtm = vect.fit_transform(X_train)
print('输出矩阵中不为0的数据的情况:\n {0}'.format(X_train_dtm))
print('单词表:{0}\n 单词的数目:{1}'.format(vect.get_feature_names(),len(vect.get_feature_names())))
print('频率矩阵大小:{0}'.format(X_train_dtm.shape))

#同理,根据已经获取的单词表,将测试数据转换为频率矩阵
X_test_dtm = vect.transform(X_test)

输出:所有4179份训练邮件中共有7456单词,所以最后训练数据X_train_dtm为7179x7456的矩阵
每一行代表一个邮件,第i行中第j列数据代表:在第i份邮件中单词j出现的次数

(0, 2022)	1
  (0, 4779)	1
  (0, 4662)	1
  (0, 6892)	1
  (0, 6656)	1
  (0, 50)	1
  (0, 4743)	1
  (0, 4375)	1
  (0, 1552)	1
  (0, 264)	1
  (0, 4983)	1
  (0, 7424)	1
  (0, 3170)	1
  (0, 2864)	2
  (0, 4987)	1
  (0, 1572)	1
  (0, 3880)	1
  (0, 5479)	1
  (0, 3971)	1
  (0, 4781)	1
  (0, 5193)	1
  (0, 3181)	1
  (0, 509)	1
  (1, 6758)	1
  (1, 3316)	1
  :	:
  (4177, 3700)	1
  (4177, 837)	1
  (4177, 307)	1
  (4177, 6662)	1
  (4177, 6034)	1
  (4177, 4508)	1
  (4177, 2556)	1
  (4177, 5490)	1
  (4177, 254)	1
  (4177, 2744)	1
  (4177, 4778)	1
  (4177, 4446)	1
  (4177, 4255)	1
  (4177, 3629)	1
  (4177, 7257)	1
  (4177, 1574)	1
  (4177, 6887)	1
  (4177, 3738)	1
  (4177, 5656)	1
  (4177, 6514)	1
  (4177, 6656)	1
  (4178, 5999)	1
  (4178, 7257)	1
  (4178, 4238)	1
  (4178, 1691)	1
单词表:['00', '000', '008704050406', '0121', '01223585236', '01223585334', '0125698789', '02', '0207', '02072069400', '02073162414', '02085076972', '021', '03', '04', '0430', '05', '050703', '0578', '06', '07', '07008009200', '07090201529', '07090298926', '07123456789', '07732584351', '07734396839', '07742676969', '0776xxxxxxx', '07781482378', '07786200117', '078', '07801543489', '07808', '07808247860', '07808726822', '07815296484', '07821230901', '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', '08448714184', '0845', '08450542832', '08452810071', '08452810073', '08452810075over18', '0870', '08700435505150p', '08700469649', '08700621170150p', '08701213186', '08701417012', '08701417012150p', '0870141701216', '087016248', '08701752560', '0870241182716', '08702840625', '08704050406', '08704439680', '08706091795', '0870737910216yrs', '08707509020', '08707808226', '08708034412', '08709222922', '08709501522', '0871', '087104711148', '08712101358', '08712103738', '0871212025016', '08712300220', '087123002209am', '08712317606', '08712400602450p', '08712400603', '08712402050', '08712402578', '08712402779', '08712402902', '08712402972', '08712404000', '08712405020', '08712405022', '08712460324', '0871277810710p', '0871277810810', '0871277810910p', '08714342399', '08714712379', '08714712388', '08714712394', '08714712412', '08715203028', '08715203649', '08715203652', '08715203685', '08715203694', '08715500022', '08715705022', '08717168528', '08717205546', '0871750', '08717898035', '08718711108', '08718720201', '08718723815', '08718725756', '08718726270', '087187262701', '08718726970', '08718726971', '08718726978', '08718727868', '08718727870', '08718727870150ppm', '08718730555', '08718730666', '08718738001', '08718738002', '08719180248', '08719181503', '08719181513', '08719899217', '08719899229', '08719899230', '09', '09050000301', '09050000332', '09050000460', '09050000555', '09050000878', '09050000928', '09050001295', '09050001808', '09050002311', '09050003091', '09050090044', '09050280520', '09053750005', '09056242159', '09057039994', '09058091854', '09058091870', '09058094454', '09058094455', '09058094507', '09058094565', '09058094583', '09058094594', '09058094597', '09058094599', '09058098002', '09058099801', '09061104276', '09061104283', '09061209465', '09061213237', '09061221061', '09061221066', '09061701444', '09061701461', '09061701939', '09061702893', '09061743386', '09061743806', '09061743810', '09061743811', '09061744553', '09061749602', '09061790121', '09061790125', '09061790126', '09063440451', '09063458130', '0906346330', '09064011000', '09064012103', '09064012160', '09064015307', '09064017295', '09064018838', '09064019014', '09064019788', '09065069120', '09065069154', '09065171142', '09065174042', '09065394514', '09065989180', '09065989182', '09066350750', '09066358152', '09066358361', '09066361921', '09066362231', '09066364311', '09066364349', '09066364589', '09066368470', '09066380611', '09066382422', '09066612661', '09066649731from', '09066660100', '09071512433', '09077818151', '09090204448', '09094100151', '09094646631', '09095350301', '09096102316', '09099725823', '09099726395', '09099726429', '09099726481', '09099726553', '09111032124', '09701213186', '0a', '10', '100', '1000', '1000s', '100p', '100percent', '1013', '1030', '10am', '10k', '10p', '10ppm', '10th', '11', '1120', '113', '1131', '114', '116', '118p', '11mths', '11pm', '12', '1205', '120p', '121', '1225', '123', '125', '1250', '125gift', '128', '12hrs', '12mths', '13', '130', '1327', '139', '14', '140', '1405', '140ppm', '145', '1450', '146tf150p', '14thmarch', '15', '150', '1500', '150p', '150p16', '150pm', '150ppermesssubscription', '150ppm', '150pw', '151', '153', '15pm', '16', '165', '1680', '169', '177', '18', '180', '1843', '18p', '18yrs', '195', '1956669', '1apple', '1b6a5ecef91ff9', '1cup', '1er', '1hr', '1im', '1lemon', '1mega', '1million', '1pm', '1st', '1st4terms', '1stchoice', '1thing', '1tulsi', '1win150ppmx3', '1winaweek', '1winawk', '1x150p', '1yf', '20', '200', '2000', '2003', '2004', '2005', '2006', '2007', '2025050', '20m12aq', '20p', '21', '21870000', '21st', '22', '220', '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', '2channel', '2day', '2docd', '2end', '2ez', '2find', '2getha', '2geva', '2go', '2gthr', '2hrs', '2kbsubject', '2lands', '2marrow', '2moro', '2morow', '2morro', '2morrow', '2morrowxxxx', '2mro', '2mrw', '2mwen', '2nd', '2nite', '2optout', '2px', '2rcv', '2stop', '2stoptxt', '2u', '2watershd', '2waxsto', '2wks', '2wt', '2wu', '2yr', '2yrs', '30', '300', '3000', '300603', '300p', '3030', '30apr', '30ish', '30pm', '30pp', '30s', '31', '3100', '310303', '31p', '32', '32000', '326', '33', '330', '350', '3510i', '3650', '36504', '3680', '3750', '37819', '38', '382', '391784', '3aj', '3d', '3days', '3g', '3gbp', '3hrs', '3lions', '3lp', '3miles', '3mins', '3mobile', '3optical', '3pound', '3qxj9', '3rd', '3ss', '3uz', '3wks', '3x', '3xx', '40', '400', '400mins', '400thousad', '402', '40411', '40533', '40gb', '40mph', '41685', '41782', '420', '4217', '42810', '430', '434', '44', '440', '4403ldnw1a7rw18', '44345', '447797706009', '447801259231', '448712404000', '449050000301', '45', '450p', '450ppw', '45239', '45pm', '47', '4719', '4742', '47per', '48', '4882', '48922', '49', '49557', '4a', '4d', '4eva', '4few', '4fil', '4get', '4got', '4info', '4msgs', '4mths', '4qf2', '4t', '4th', '4txt', '4u', '4utxt', '4w', '4wrd', '4xx26', '4years', '50', '500', '5000', '50award', '50ea', '50gbp', '50p', '50perweeksub', '50perwksub', '50pm', '50ppm', '50rcvd', '50s', '515', '5226', '523', '5249', '526', '528', '530', '54', '542', '5digital', '5free', '5ish', '5k', '5min', '5mls', '5p', '5pm', '5th', '5wb', '5we', '5wkg', '5wq', '5years', '60', '600', '6031', '6089', '60p', '61', '61200', '61610', '62468', '630', '63miles', '645', '65', '66', '6669', '674', '67441233', '68866', '69101', '69669', '69696', '69698', '69855', '69866', '69888', '69888nyt', '69911', '69969', '69988', '6hrs', '6ish', '6missed', '6months', '6ph', '6pm', '6th', '6wu', '6zf', '700', '7250', '7250i', '730', '731', '75', '750', '75max', '762', '7634', '7684', '77', '7732584351', '78', '786', '7876150ppm', '7am', '7cfca1a', '7ish', '7oz', '7pm', '7th', '7ws', '800', '8000930705', '80062', '8007', '80082', '80086', '80122300p', '80155', '80182', '8027', '80488', '80608', '8077', '80878', '81010', '81151', '81303', '81618', '820554ad0a1705572711', '82242', '82277', '82324', '82468', '83049', '83110', '83118', '83222', '83332', '83338', '83355', '83600', '83738', '84', '84025', '84122', '84128', '84199', '84484', '85', '85023', '85069', '85222', '85233', '8552', '86021', '861', '864233', '86688', '86888', '87021', '87066', '87070', '87077', '87121', '87131', '872', '87239', '87575', '88039', '88066', '88088', '88222', '88600', '88800', '8883', '88877', '88888', '89034', '89070', '89080', '89105', '89545', '89555', '89693', '89938', '8am', '8ball', '8lb', '8p', '8pm', '8th', '8wp', '900', '9061100010', '910', '9153', '92h', '930', '9307622', '945', '95', '9755', '9758', '99', '9996', '9ae', '9am', '9pm', '9t', '9th', '9yt', '____', 'a21', 'a30', 'aah', 'aaniye', 'aaooooright', 'aathi', 'abbey', 'abdomen', 'abeg', 'abel', 'aberdeen', 'abi', 'ability', 'abiola', 'abj', 'able', 'abnormally', 'about', 'aboutas', 'abroad', 'absence', 'absolutely', 'absolutly', 'abstract', 'abt', 'abta', 'ac', 'academic', 'acc', 'accent', 'accenture', 'accept', 'access', 'accessible', 'accidant', 'accident', 'accidentally', 'accommodation', 'accomodate', 'accomodations', 'accordin', 'accordingly', 'account', 'accounting', 'accounts', 'achan', 'ache', 'achieve', 'acid', 'acl03530150pm', 'acnt', 'aco', 'across', 'act', 'acted', 'actin', 'acting', 'action', 'activ8', 'activate', 'active', 'activities', 'actor', 'actual', 'actually', 'ad', 'adam', 'add', 'addamsfa', 'added', 'addicted', 'addie', 'adding', 'address', 'adewale', 'adjustable', 'admin', 'administrator', 'admirer', 'admission', 'admit', 'adore', 'adoring', 'adp', 'adress', 'adrian', 'adrink', 'ads', 'adsense', 'adult', 'adults', 'advance', 'adventure', 'advice', 'advise', 'advising', 'advisors', 'aeronautics', 'aeroplane', 'affair', 'affairs', 'affection', 'affections', 'affidavit', 'afford', 'afghanistan', 'afraid', 'africa', 'african', 'aft', 'after', 'afternon', 'afternoon', 'afterwards', 'aftr', 'ag', 'again', 'against', 'age', 'age16', 'age23', 'agency', 'agent', 'agents', 'ages', 'agidhane', 'aging', 'ago', 'agree', 'ah', 'aha', 'ahead', 'ahhh', 'ahhhh', 'ahmad', 'aids', 'aig', 'aight', 'ain', 'aint', 'air', 'air1', 'airport', 'airtel', 'aiya', 'aiyah', 'aiyar', 'aiyo', 'ajith', 'ak', 'aka', 'akon', 'al', 'alaipayuthe', 'album', 'alcohol', 'aldrine', 'alert', 'alertfrom', 'alerts', 'alex', 'alfie', 'algarve', 'algebra', 'ali', 'alian', 'alibi', 'alive', 'all', 'allah', 'allday', 'allow', 'allowed', 'allows', 'almost', 'alone', 'along', 'alot', 'already', 'alright', 'alrite', 'also', 'alto18', 'alwa', 'always', 'alwys', 'am', 'amanda', 'amazing', 'ambitious', 'american', 'ami', 'amk', 'amla', 'amma', 'ammae', 'ammo', 'among', 'amongst', 'amore', 'amount', 'amp', 'amplikater', 'amrita', 'ams', 'amt', 'amused', 'an', 'analysis', 'anand', 'and', 'anderson', 'andre', 'andres', 'andrews', 'angry', 'animal', 'animation', 'anna', 'annie', 'anniversary', 'announced', 'announcement', 'annoyin', 'annoying', 'anot', 'another', 'ans', 'ansr', 'answer', 'answered', 'answerin', 'answering', 'answers', 'answr', 'antelope', 'antha', 'anthony', 'anti', 'antibiotic', 'any', 'anybody', 'anymore', 'anyone', 'anyones', 'anyplaces', 'anythiing', 'anythin', 'anything', 'anythingtomorrow', 'anytime', 'anyway', 'anyways', 'anywhere', 'aom', 'apart', 'apartment', 'apes', 'apeshit', 'aphex', 'apnt', 'apo', 'apologetic', 'apologise', 'apologize', 'apology', 'app', 'apparently', 'appeal', 'appear', 'appendix', 'applausestore', 'applebees', 'apples', 'application', 'apply', 'applying', 'appointment', 'appointments', 'appreciate', 'appropriate', 'approve', 'approved', 'approx', 'apps', 'appt', 'appy', 'april', 'aproach', 'aptitude', 'aquarius', 'ar', 'arab', 'arabian', 'arcade', 'archive', 'ard', 'are', 'area', 'aren', 'arent', 'arestaurant', 'aretaking', 'argentina', 'argh', 'argue', 'arguing', 'argument', 'arguments', 'aries', 'arise', 'arises', 'arm', 'armand', 'armenia', 'arms', 'arng', 'around', 'aroundn', 'arr', 'arrange', 'arranging', 'arrested', 'arrival', 'arrive', 'arrow', 'arsenal', 'art', 'arts', 'arty', 'arul', 'arun', 'as', 'asap', 'asda', 'ashes', 'ashley', 'ashwini', 'asia', 'asian', 'asjesus', 'ask', 'askd', 'asked', 'askin', 'asking', 'asks', 'asleep', 'asp', 'ass', 'assessment', 'asshole', 'assistance', 'associate', 'asssssholeeee', 'assume', 'assumed', 'asthere', 'astne', 'astrology', 'astronomer', 'asusual', 'at', 'ate', 'athletic', 'athome', 'atlanta', 'atlast', 'atleast', 'atm', 'atrocious', 'attached', 'attempt', 'attend', 'attending', 'attention', 'attitude', 'attraction', 'attractive', 'attracts', 'attributed', 'auction', 'audition', 'audrey', 'audrie', 'august', 'aunt', 'aunties', 'aunts', 'aunty', 'aust', 'australia', 'auto', 'autocorrect', 'av', 'availa', 'available', 'avalarr', 'avatar', 'avble', 'ave', 'avent', 'avenue', 'avin', 'avo', 'avoid', 'avoids', 'await', 'awaiting', 'awake', 'award', 'awarded', 'away', 'awesome', 'awkward', 'aww', 'awww', 'ax', 'axel', 'axis', 'ay', 'ayn', 'b4', 'b4190604', 'b4280703', 'b4u', 'b4utele', 'ba', 'ba128nnfwfly150ppm', 'babe', 'babes', 'baby', 'babygoodbye', 'babyjontet', 'babysit', 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'upcharge', 'upd8', 'updat', 'update', 'update_now', 'upgrade', 'upgrading', 'upgrdcentre', 'upload', 'uploaded', 'upon', 'ups', 'upset', 'upstairs', 'upto', 'ur', 'urawinner', 'ure', 'urfeeling', 'urgent', 'urgently', 'urgh', 'urgnt', 'urgoin', 'urgran', 'url', 'urmom', 'urn', 'urself', 'us', 'usa', 'usb', 'usc', 'use', 'used', 'useful', 'useless', 'user', 'uses', 'usf', 'usher', 'using', 'usps', 'usual', 'usually', 'uterus', 'utter', 'uttered', 'uup', 'uwana', 'uworld', 'va', 'vaazhthukkal', 'vague', 'vaguely', 'vale', 'valentine', 'valentines', 'valid', 'valid12hrs', 'valuable', 'value', 'valued', 'values', 'valuing', 'varaya', 'vargu', 'various', 'varma', 'varunnathu', 'vary', 'vasai', 'vat', 'vatian', 'vava', 'vday', 've', 'vegas', 'vegetables', 'velachery', 'velusamy', 'venaam', 'venugopal', 'verified', 'verify', 'verifying', 'version', 'versus', 'very', 'vettam', 'vewy', 'via', 'vibrate', 'vibrator', 'vic', 'victoria', 'victors', 'vid', 'video', 'videochat', 'videophones', 'videos', 'videosound', 'videosounds', 'view', 'vijay', 'vijaykanth', 'vikky', 'vilikkam', 'vill', 'villa', 'village', 'vinobanagar', 'violated', 'violence', 'violet', 'vip', 'virgil', 'virgin', 'virgins', 'virtual', 'visa', 'visionsms', 'visit', 'visitor', 'visitors', 'vital', 'viva', 'vivek', 'vl', 'voda', 'vodafone', 'vodka', 'voice', 'voicemail', 'volcanoes', 'vomit', 'vomitin', 'vomiting', 'vote', 'voted', 'voucher', 'vouchers', 'vpod', 'vry', 'vs', 'vth', 'w1', 'w111wx', 'w14rg', 'w1a', 'w1j6hl', 'w1jhl', 'w1t1jy', 'w4', 'w45wq', 'w8in', 'wa', 'wa14', 'waaaat', 'wad', 'wadebridge', 'wah', 'wahay', 'waheed', 'waheeda', 'wahleykkum', 'waht', 'wait', 'waited', 'waitin', 'waiting', 'wake', 'waking', 'wales', 'walk', 'walkabout', 'walked', 'walking', 'wall', 'wallet', 'wallpaper', 'walls', 'walmart', 'walsall', 'wamma', 'wan', 'wan2', 'wana', 'wanna', 'wannatell', 'want', 'want2come', 'wanted', 'wanting', 'wants', 'wap', 'waqt', 'warm', 'warming', 'warned', 'warner', 'warning', 'warranty', 'warwick', 'was', 'wasn', 'wasnt', 'waste', 'wasted', 'wasting', 'wat', 'watch', 'watched', 'watchin', 'watching', 'watchng', 'water', 'watever', 'watevr', 'wating', 'watr', 'wats', 'wave', 'wavering', 'waves', 'way', 'way2sms', 'waz', 'wc1n', 'wc1n3xx', 'we', 'weak', 'weakness', 'weaknesses', 'weapon', 'wear', 'wearing', 'weaseling', 'weasels', 'weather', 'web', 'web2mobile', 'webadres', 'webeburnin', 'website', 'wed', 'weddin', 'wedding', 'wedlunch', 'wednesday', 'weds', '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', 'wesleys', 'west', 'western', 'westlife', 'westonzoyland', 'wet', 'wetherspoons', 'wewa', 'what', 'whatever', 'whats', 'wheat', 'wheel', 'wheellock', 'when', 'whenever', 'whenevr', 'whens', 'where', 'whereare', 'wherever', 'wherevr', 'wherre', 'whether', 'which', 'while', 'whilltake', 'whispers', 'white', 'whn', 'who', 'whoever', 'whole', 'whom', 'whore', 'whos', 'whose', 'whr', 'why', 'wi', 'wicked', 'wicket', 'wicklow', 'wid', 'widelive', 'wif', 'wife', 'wifes', 'wifi', 'wihtuot', 'wil', 'wild', 'wildlife', 'will', 'willing', 'willpower', 'win', 'win150ppmx3age16', 'wind', 'window', 'windows', 'winds', 'wine', 'wined', 'wings', 'winner', 'winnersclub', 'winning', 'wins', 'winterstone', 'wipe', 'wipro', 'wire3', 'wisdom', 'wise', 'wish', 'wisheds', 'wishes', 'wishin', 'wishing', 'wishlist', 'wiskey', 'wit', 'with', 'wither', 'within', 'without', 'witin', 'witot', 'witout', 'wiv', 'wizzle', 'wk', 'wkend', 'wkent', 'wkg', 'wkly', 'wknd', 'wks', 'wlcome', 'wld', 'wml', 'wn', 'wnevr', 'wnt', 'wo', 'woah', 'wocay', 'woke', 'woken', 'woman', 'womdarfull', 'women', 'won', 'wondar', 'wondarfull', 'wonder', 'wonderful', 'wondering', 'wonders', 'wont', 'woo', 'woodland', 'woods', 'woohoo', 'woot', 'woozles', 'worc', 'word', 'words', 'work', 'workage', 'workand', 'workin', 'working', 'workout', 'works', 'world', 'worlds', 'worms', 'worried', 'worries', 'worry', 'worrying', 'worse', 'worst', 'worth', 'worthless', 'wot', 'wotu', 'wotz', 'woul', 'would', 'woulda', 'wouldn', 'wounds', 'wow', 'wrc', 'wrecked', 'wrench', 'wrenching', 'wright', 'write', 'wrk', 'wrkin', 'wrking', 'wrld', 'wrnog', 'wrong', 'wrongly', 'wrote', 'ws', 'wt', 'wtc', 'wtf', 'wth', 'wthout', 'wtlp', 'wud', 'wudn', 'wuld', 'wuldnt', 'wun', 'www', 'wylie', 'x2', 'x29', 'x49', 'xafter', 'xam', 'xavier', 'xchat', 'xclusive', 'xin', 'xmas', 'xoxo', 'xt', 'xuhui', 'xx', 'xxsp', 'xxx', 'xxxmobilemovieclub', 'xxxx', 'xxxxx', 'xxxxxx', 'xy', 'y87', 'ya', 'yah', 'yahoo', 'yalrigu', 'yalru', 'yam', 'yan', 'yar', 'yarasu', 'yards', 'yavnt', 'yaxx', 'yaxxx', 'yay', 'yck', 'yeah', 'year', 'years', 'yeesh', 'yeh', 'yelling', 'yellow', 'yen', 'yeovil', 'yep', 'yer', 'yes', 'yest', 'yesterday', 'yet', 'yetunde', 'yijue', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yor', 'yorge', 'you', 'youdoing', 'youi', 'youphone', 'your', 'youre', 'yourjob', 'yours', 'yourself', 'youwanna', 'yowifes', 'yoyyooo', 'yr', 'yrs', 'ything', 'yummmm', 'yummy', 'yun', 'yunny', 'yuo', 'yuou', 'yup', 'zac', 'zaher', 'zealand', 'zebra', 'zed', 'zeros', 'zhong', 'zindgi', 'zoe', 'zoom', 'zouk', 'zyada', 'èn', '〨ud']
 单词的数目:7456
频率矩阵大小:(4179, 7456)

(6)训练分类器,并对测试数据进行预测

from sklearn import linear_model
#获得逻辑回归分类器,并设置正则化系数为 1/C
clf = linear_model.LogisticRegression(C=1e5)

#训练分类器
clf.fit(X_train_dtm, y_train)

#训练结束后获取测试集结果
y_pred_class = clf.predict(X_test_dtm)

(7)预测结果分析

from sklearn import metrics
#获取分类正确的比例,对应元素相等数目/总数目
accuracy = metrics.accuracy_score(y_test, y_pred_class)
print('准确率为:{0}'.format(accuracy))

#获取预测分类结果和实际结果的详细信息:混淆矩阵
cm = metrics.confusion_matrix(y_test, y_pred_class)
print(cm)

#非垃圾邮件错误预测为垃圾邮件
print(X_test[y_test < y_pred_class])

#垃圾邮件错误预测为非垃圾邮件
print(X_test[y_test > y_pred_class])

y_pred_prob = clf.predict_proba(X_test_dtm)[:, 1]
print('各个邮件类别为0(非垃圾邮件)的概率:')
print(y_pred_prob)

输出:

准确率为:0.9885139985642498
[[1205    3]
 [  13  172]]
2340    Cheers for the message Zogtorius. I’ve been st...
4009    Forgot you were working today! Wanna chat, but...
1497    I'm always on yahoo messenger now. Just send t...
Name: message, dtype: object
1777                    Call FREEPHONE 0800 542 0578 now!
763     Urgent Ur £500 guaranteed award is still uncla...
3132    LookAtMe!: Thanks for your purchase of a video...
1875    Would you like to see my XXX pics they are so ...
1893    CALL 09090900040 & LISTEN TO EXTREME DIRTY LIV...
4298    thesmszone.com lets you send free anonymous an...
4394    RECPT 1/3. You have ordered a Ringtone. Your o...
4949    Hi this is Amy, we will be sending you a free ...
761     Romantic Paris. 2 nights, 2 flights from £79 B...
19      England v Macedonia - dont miss the goals/team...
2821    INTERFLORA - “It's not too late to order Inter...
2247    Hi ya babe x u 4goten bout me?' scammers getti...
4514    Money i have won wining number 946 wot do i do...
Name: message, dtype: object
各个邮件类别为0(非垃圾邮件)的概率:
[9.90605877e-07 4.03318205e-09 1.38284799e-07 ... 6.48403564e-06
 1.00000000e+00 3.77161208e-09]

Process finished with exit code 0