1 4 9 16 25
10 20
1 2 4 8 16
20
14
25
8
[[4, 8, 8], [10, 0, 8], [9, 5, 2]]
54
[[1, 5, 3], [1, 4, 4], [10, 10, 6]]
44
True
False
input 1 numbers:1
input 2 numbers:2
input 3 numbers:3
input 4 numbers:4
input 5 numbers:5
input 6 numbers:6
input 7 numbers:7
input 8 numbers:8
input 9 numbers:9
input 10 numbers:0
input 11 numbers:1
input 12 numbers:2
input 13 numbers:3
input 14 numbers:4
input 15 numbers:5
input 16 numbers:6
[['1', '2', '3', '4'], ['5', '6', '7', '8'], ['9', '10', '1', '2'], ['3', '4', '5', '6']]
False
[[4 9 2]
[3 5 7]
[8 1 6]]
True
[[9, 6, 2, 4, 1, 7, 8, 3, 5], [7, 3, 1, 5, 8, 9, 2, 4, 6], [8, 5, 4, 3, 2, 6, 7, 9, 1], [3, 8, 5, 9, 4, 1, 6, 2, 7], [2, 9, 7, 6, 5, 3, 4, 1, 8], [1, 4, 6, 8, 7, 2, 3, 5, 9], [4, 7, 3, 1, 6, 5, 9, 8, 2], [5, 2, 9, 7, 3, 8, 1, 6, 4], [6, 1, 8, 2, 9, 4, 5, 7, 3]]
True
(0,3)
(4,0)
5.0
(0,3)--(4,0)
5.0
1.4142135623730951
(1,1,Tigger,E)
(1,1,Tigger,N)
(2,2,Bank,S)
(2,2,Bank,W)
(-8,2,Bank,W)
(-8,2,Bank,E)
(2,2,Bank,E)
random_search swap Tour length:616
threshold_accepting swap Tour length:1551
simulated_annealing swap Tour length:1680
simulated_annealing swap 0 1723
simulated_annealing swap 1 1637
simulated_annealing swap 2 1658
simulated_annealing swap 3 1729
simulated_annealing swap 4 1736
simulated_annealing insert 0 1616
simulated_annealing insert 1 1837
simulated_annealing insert 2 1521
simulated_annealing insert 3 1653
simulated_annealing insert 4 1590
simulated_annealing inverse 0 1664
simulated_annealing inverse 1 1669
simulated_annealing inverse 2 1720
simulated_annealing inverse 3 1559
simulated_annealing inverse 4 1675
simulated_annealing swap 1736.0 1637.0 1696.6
simulated_annealing insert 1837.0 1521.0 1643.4
simulated_annealing inverse 1720.0 1559.0 1657.4
random_search inverse 0 470
random_search inverse 1 470
random_search inverse 2 450
random_search inverse 3 451
random_search inverse 4 454
threshold_accepting inverse 0 1488
threshold_accepting inverse 1 1643
threshold_accepting inverse 2 1572
threshold_accepting inverse 3 1364
threshold_accepting inverse 4 1658
simulated_annealing inverse 0 1629
simulated_annealing inverse 1 1644
simulated_annealing inverse 2 1442
simulated_annealing inverse 3 1630
simulated_annealing inverse 4 1716
random_search inverse 470.0 450.0 459.0
threshold_accepting inverse 1658.0 1364.0 1545.0
simulated_annealing inverse 1716.0 1442.0 1612.2
[[0 1]
[2 3]
[4 5]]
Degree = 2, interaction_only = True, include_bias = True
[[ 1. 0. 1. 0.]
[ 1. 2. 3. 6.]
[ 1. 4. 5. 20.]]
Degree = 2, interaction_only = True, include_bias = False
[[ 0. 1. 0.]
[ 2. 3. 6.]
[ 4. 5. 20.]]
Degree = 2, interaction_only = False, include_bias = True
[[ 1. 0. 1. 0. 0. 1.]
[ 1. 2. 3. 4. 6. 9.]
[ 1. 4. 5. 16. 20. 25.]]
Degree = 2, interaction_only = False, include_bias = False
[[ 0. 1. 0. 0. 1.]
[ 2. 3. 4. 6. 9.]
[ 4. 5. 16. 20. 25.]]
Degree = 3, interaction_only = True, include_bias = True
[[ 1. 0. 1. 0.]
[ 1. 2. 3. 6.]
[ 1. 4. 5. 20.]]
Degree = 3, interaction_only = True, include_bias = False
[[ 0. 1. 0.]
[ 2. 3. 6.]
[ 4. 5. 20.]]
Degree = 3, interaction_only = False, include_bias = True
[[ 1. 0. 1. 0. 0. 1. 0. 0. 0. 1.]
[ 1. 2. 3. 4. 6. 9. 8. 12. 18. 27.]
[ 1. 4. 5. 16. 20. 25. 64. 80. 100. 125.]]
Degree = 3, interaction_only = False, include_bias = False
[[ 0. 1. 0. 0. 1. 0. 0. 0. 1.]
[ 2. 3. 4. 6. 9. 8. 12. 18. 27.]
[ 4. 5. 16. 20. 25. 64. 80. 100. 125.]]
[[1.000e+00 2.272e+03]
[2.000e+00 nan]
[3.000e+00 1.386e+03]
...
[7.410e+02 5.392e+03]
[7.420e+02 5.906e+03]
[7.430e+02 4.881e+03]]
(743, 2)
317389767.3397781 0.41632369094258925 989.0248710607153 [2.59619213]
Degree=1, 317389767.3397781 0.41632369094258914 989.0248710607158 [0. 2.59619213]
Degree=2, 179983507.8781791 0.6690122985058977 1974.7608169567045 [ 0. -5.2654565 0.01053222]
Degree=3, 139350144.0317253 0.7437366099834488 1333.2879656082441 [ 0.00000000e+00 4.94226019e+00 -2.35893797e-02 3.04960132e-05]
Degree=1, 317389767.33977795 [ 2.59619213 989.02487106]
Degree=2, 179983507.87817925 [ 1.05322215e-02 -5.26545650e+00 1.97476082e+03]
Degree=3, 139350144.0317253 [ 3.04960132e-05 -2.35893797e-02 4.94226019e+00 1.33328797e+03]
Testing started at 15:15 ...
D:\ProgramData\Anaconda3\python.exe "D:\Program Files\JetBrains\PyCharm 2020.2.31\plugins\python\helpers\pycharm\_jb_pytest_runner.py" --target lab3_linear_regression_gradient_descent.py::test_random_data
Launching pytest with arguments lab3_linear_regression_gradient_descent.py::test_random_data in E:\work\homework\machine_learning\homework_ml2020_shao\Lab03\Question
============================= test session starts =============================
platform win32 -- Python 3.8.3, pytest-5.4.3, py-1.9.0, pluggy-0.13.1 -- D:\ProgramData\Anaconda3\python.exe
cachedir: .pytest_cache
rootdir: E:\work\homework\machine_learning\homework_ml2020_shao\Lab03\Question
collecting ... collected 1 item
lab3_linear_regression_gradient_descent.py::test_random_data PASSED [100%]X的数据规模为 ： (2000, 1)
Y的数据规模为 ： (2000, 1)
系数为3和3.27
(2000, 2)
[[1. 5.14565512]
[1. 6.63812737]
[1. 8.77687454]
...
[1. 9.10442437]
[1. 4.54407881]
[1. 0.78512724]]
Gradient Descent:
(array([[2.99971338],
[3.27004318]]), 3588)
Stochastic Gradient Descent:
(array([[2.99856289],
[3.27021674]]), 2994)
Mini-batch Gradient Descent:
(array([[2.99863047],
[3.27019828]]), 2996)
============================== 1 passed in 4.26s ==============================
data_feature_name: ['city=厦门', 'city=泉州', 'city=福州', 'income']
data_transform [[ 0. 0. 1. 12.]
[ 1. 0. 0. 15.]
[ 0. 1. 0. 10.]]
new_data_transform [[ 0. 0. 1. 13.]
[ 1. 0. 0. 16.]
[ 0. 1. 0. 11.]
[ 0. 0. 0. 9.]]
analyzer= word
data_feature_name ['***上太阳升', '我爱北京', '我爱北京***']
data_transform [[0 1 1]
[1 0 0]]
====================================
analyzer= char
data_feature_name ['上', '京', '北', '升', '天', '太', '安', '我', '爱', '门', '阳', '，']
data_transform [[0 2 2 0 1 0 1 2 2 1 0 1]
[1 0 0 1 1 1 1 0 0 1 1 0]]
====================================
ngram_range= (1, 1)
data_feature_name ['上', '京', '北', '升', '天', '太', '安', '我', '爱', '门', '阳', '，']
data_transform [[0 2 2 0 1 0 1 2 2 1 0 1]
[1 0 0 1 1 1 1 0 0 1 1 0]]
====================================
ngram_range= (2, 2)
data_feature_name ['上太', '京天', '京，', '北京', '天安', '太阳', '安门', '我爱', '爱北', '门上', '阳升', '，我']
data_transform [[0 1 1 2 1 0 1 2 2 0 0 1]
[1 0 0 0 1 1 1 0 0 1 1 0]]
====================================
ngram_range= (1, 2)
data_feature_name ['上', '上太', '京', '京天', '京，', '北', '北京', '升', '天', '天安', '太', '太阳', '安', '安门', '我', '我爱', '爱', '爱北', '门', '门上', '阳', '阳升', '，', '，我']
data_transform [[0 0 2 1 1 2 2 0 1 1 0 0 1 1 2 2 2 2 1 0 0 0 1 1]
[1 1 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0]]
====================================
Tfid
ngram_range= (1, 1)
data_feature_name ['上', '京', '北', '升', '天', '太', '安', '我', '爱', '门', '阳']
data_transform [[0. 0.42567716 0.42567716 0. 0.30287281 0.
0.30287281 0.42567716 0.42567716 0.30287281 0. ]
[0.42567716 0. 0. 0.42567716 0.30287281 0.42567716
0.30287281 0. 0. 0.30287281 0.42567716]]
====================================
ngram_range= (2, 2)
data_feature_name ['上太', '京天', '北京', '天安', '太阳', '安门', '我爱', '爱北', '门上', '阳升']
data_transform [[0. 0.44665616 0.44665616 0.31779954 0. 0.31779954
0.44665616 0.44665616 0. 0. ]
[0.44665616 0. 0. 0.31779954 0.44665616 0.31779954
0. 0. 0.44665616 0.44665616]]
====================================
ngram_range= (1, 2)
data_feature_name ['上', '上太', '京', '京天', '北', '北京', '升', '天', '天安', '太', '太阳', '安', '安门', '我', '我爱', '爱', '爱北', '门', '门上', '阳', '阳升']
data_transform [[0. 0. 0.30814893 0.30814893 0.30814893 0.30814893
0. 0.2192505 0.2192505 0. 0. 0.2192505
0.2192505 0.30814893 0.30814893 0.30814893 0.30814893 0.2192505
0. 0. 0. ]
[0.30814893 0.30814893 0. 0. 0. 0.
0.30814893 0.2192505 0.2192505 0.30814893 0.30814893 0.2192505
0.2192505 0. 0. 0. 0. 0.2192505
0.30814893 0.30814893 0.30814893]]
====================================
[[ 1. -1. 2.]
[ 2. 0. 0.]
[ 0. 1. -1.]]
StandardScaler()
[[ 0. -1.22474487 1.33630621]
[ 1.22474487 0. -0.26726124]
[-1.22474487 1.22474487 -1.06904497]]
MinMaxScaler()
[[0.5 0. 1. ]
[1. 0.5 0.33333333]
[0. 1. 0. ]]
Normalizer()
[[ 0.40824829 -0.40824829 0.81649658]
[ 1. 0. 0. ]
[ 0. 0.70710678 -0.70710678]]
['00', '000', '000pes', '008704050406', '0089', '0121', '01223585236', '01223585334', '02', '0207', '02073162414', '021', '03', '04', '0430', '05', '0578', '06', '07', '07008009200', '07046744435', '07090201529', '07090298926', '07099833605', '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', '08448350055', '0845', '08450542832', '08452810071', '08452810073', '0870', '08700435505150p', '08700621170150p', '08701213186', '08701237397', '08701417012', '08701417012150p', '0870141701216', '087016248', '08701752560', '0870241182716', '08702490080', '08702840625', '08704050406', '08704439680ts', '08706091795', '0870737910216yrs', '08707500020', '08707509020', '0870753331018', '08707808226', '08708800282', '08709222922', '08709501522', '0871', '087104711148', '08712101358', '08712103738', '0871212025016', '08712300220', '087123002209am', '08712317606', '08712400200', '08712400602450p', '08712400603', '08712402050', '08712402578', '08712402779', '08712402972', '08712404000', '08712405020', '08712405022', '08712460324', '08712466669', '0871277810710p', '0871277810810', '08714342399', '087147123779am', '08714712379', '08714712388', '08714712394', '08714712412', '08714714011', '08715203028', '08715203649', '08715203656', '08715203677', '08715203685', '08715205273', '08715500022', '08715705022', '08717111821', '08717168528', '08717205546', '0871750', '08717507382', '08717509990', '08717890890', '08717895698', '08717898035', '08718711108', '08718720201', '08718723815', '08718726270', '087187262701', '08718726970', '08718726971', '08718726978', '087187272008', '08718727868', '08718727870', '08718727870150ppm', '08718730666', '08718738001', '08718738002', '08718738034', '08719180219', '08719180248', '08719181259', '08719181503', '08719181513', '08719839835', '08719899217', '08719899229', '08719899230', '09', '09041940223', '09050000301', '09050000332', '09050000460', '09050000555', '09050000878', '09050000928', '09050001295', '09050001808', '09050002311', '09050003091', '09050090044', '09050280520', '09056242159', '09057039994', '09058091854', '09058091870', '09058094455', '09058094507', '09058094565', '09058094583', '09058094594', '09058094597', '09058094599', '09058095107', '09058095201', '09058097189', '09058098002', '09058099801', '09061104276', '09061209465', '09061213237', '09061221061', '09061221066', '09061701444', '09061701461', '09061701851', '09061701939', '09061702893', '09061743386', '09061743806', '09061743810', '09061743811', '09061744553', '09061790121', '09061790126', '09063440451', '09063442151', '09063458130', '0906346330', '09064011000', '09064012103', '09064012160', '09064015307', '09064017295', '09064017305', '09064018838', '09064019014', '09064019788', '09065069120', '09065171142', '09065174042', '09065394514', '09065394973', '09065989180', '09065989182', '09066358152', '09066358361', '09066362206', '09066362220', '09066362231', '09066364311', '09066364349', '09066364589', '09066368327', '09066368753', '09066380611', '09066612661', '09066649731from', '09077818151', '09090204448', '09090900040', '09094100151', '09094646899', '09099725823', '09099726395', '09099726429', '09099726481', '09111030116', '09111032124', '0a', '0quit', '10', '100', '1000', '1000s', '100p', '100percent', '100txt', '1013', '1030', '10am', '10k', '10p', '10ppm', '10th', '11', '1120', '113', '114', '1146', '116', '118p', '11mths', '11pm', '12', '1205', '120p', '121', '1225', '123', '125', '125gift', '12hours', '12hrs', '12mths', '13', '1327', '139', '14', '140', '1405', '145', '1450', '14tcr', '14thmarch', '15', '150', '1500', '150p', '150pm', '150ppermesssubscription', '150ppm', '150ppmsg', '151', '153', '15541', '15pm', '16', '165', '1680', '169', '177', '18', '180', '1843', '18yrs', '195', '1956669', '1b6a5ecef91ff9', '1da', '1er', '1hr', '1im', '1mega', '1pm', '1st', '1stchoice', '1stone', '1thing', '1win150ppmx3', '1winaweek', '1winawk', '1x150p', '20', '200', '2000', '2003', '2004', '2005', '2006', '200p', '2025050', '20m12aq', '20p', '21', '21870000', '21st', '22', '220', '220cm2', '23f', '23g', '24', '24hrs', '24th', '25', '250', '250k', '25p', '26', '2667', '26th', '27', '28', '28days', '28thfeb', '29', '2bold', '2c', '2channel', '2day', '2end', '2exit', '2ez', '2find', '2getha', '2geva', '2go', '2gthr', '2hook', '2i', '2kbsubject', '2lands', '2marrow', '2moro', '2morow', '2morro', '2morrow', '2morrowxxxx', '2mro', '2mrw', '2mwen', '2nd', '2nhite', '2nite', '2optout', '2p', '2price', '2px', '2rcv', '2stop', '2stoptxt', '2u', '2watershd', '2waxsto', '2wt', '2wu', '2years', '2yr', '2yrs', '30', '300', '300p', '3030', '30apr', '30ish', '30pm', '30pp', '30s', '31', '3100', '31p', '32', '32000', '32323', '326', '33', '330', '350', '3510i', '35p', '36504', '3680', '373', '3750', '37819', '391784', '3aj', '3d', '3g', '3gbp', '3hrs', '3lions', '3lp', '3miles', '3mins', '3mobile', '3optical', '3pound', '3qxj9', '3rd', '3ss', '3uz', '3wks', '3xx', '400', '400mins', '402', '4041', '40411', '40533', '40gb', '40mph', '41685', '41782', '420', '42049', '4217', '42478', '434', '44', '440', '4403ldnw1a7rw18', '44345', '447801259231', '448712404000', '449050000301', '45', '450', '450p', '450ppw', '450pw', '45239', '47', '4719', '4742', '47per', '48', '4882', '48922', '4a', '4d', '4eva', '4few', '4fil', '4get', '4give', '4got', '4info', '4jx', '4msgs', '4mths', '4my', '4qf2', '4t', '4th', '4the', '4thnov', '4txt', '4u', '4w', '4ward', '4wrd', '4xx26', '4years', '50', '500', '5000', '505060', '50award', '50gbp', '50p', '50perweeksub', '50perwksub', '50pmmorefrommobile2bremoved', '50ppm', '50rcvd', '523', '5249', '530', '54', '542', '5digital', '5free', '5k', '5min', '5mls', '5p', '5pm', '5th', '5wb', '5we', '5wq', '5years', '600', '6031', '6089', '60p', '61200', '61610', '62220cncl', '6230', '62468', '62735', '630', '63miles', '645', '65', '650', '6669', '674', '69101', '69200', '69669', '69696', '69855', '69866', '69876', '69888', '69888nyt', '69911', '69969', '69988', '6days', '6hl', '6hrs', '6missed', '6months', '6pm', '6th', '6times', '6wu', '6zf', '700', '71', '7250', '7250i', '730', '74355', '75', '750', '7548', '75max', '762', '7634', '7684', '77', '7732584351', '786', '7876150ppm', '79', '7cfca1a', '7ish', '7mp', '7oz', '7pm', '7th', '7zs', '80', '800', '80062', '8007', '80082', '80086', '80122300p', '80155', '80160', '80182', '8027', '80488', '80608', '8077', '80878', '81010', '81151', '81303', '81618', '82050', '820554ad0a1705572711', '82242', '82277', '82324', '82468', 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'together', 'tohar', 'tok', 'toking', 'tol', 'told', 'toledo', 'tolerance', 'toll', 'tom', 'tomarrow', 'tomeandsaid', 'tomo', 'tomorro', 'tomorrow', 'tomorw', 'tone', 'tones', 'tones2u', 'tones2you', 'tonexs', 'tonght', 'tongued', 'tonight', 'tonights', 'tonite', 'tons', 'too', 'took', 'tookplace', 'tool', 'tooo', 'tooth', 'toothpaste', 'tootsie', 'top', 'topic', 'toplay', 'topped', 'tops', 'torch', 'torrents', 'tortilla', 'torture', 'tosend', 'toshiba', 'toss', 'tot', 'total', 'totally', 'touch', 'touched', 'tough', 'tour', 'towards', 'town', 'toxic', 'toyota', 'tp', 'track', 'trade', 'traditions', 'traffic', 'train', 'trained', 'training', 'trainners', 'trains', 'tram', 'tranquility', 'transaction', 'transcribing', 'transfer', 'transfered', 'transferred', 'transfr', 'transfred', 'transport', 'trash', 'trauma', 'trav', 'travel', 'traveling', 'travelled', 'travelling', 'treadmill', 'treat', 'treated', 'treats', 'trebles', 'tree', 'trek', 'trends', 'tried', 'trip', 'trips', 'trishul', 'trivia', 'tron', 'trouble', 'troubleshooting', 'trouser', 'truble', 'truck', 'true', 'true18', 'truffles', 'truly', 'truro', 'trust', 'truth', 'try', 'tryin', 'trying', 'ts', 'tsandcs', 'tscs', 'tscs08714740323', 'tscs087147403231winawk', 'tsunami', 'tsunamis', 'tt', 'ttyl', 'tue', 'tues', 'tuesday', 'tuition', 'tul', 'tunde', 'turkeys', 'turn', 'turned', 'turning', 'turns', 'tuth', 'tv', 'twat', 'twelve', 'twice', 'twiggs', 'twilight', 'twinks', 'twins', 'two', 'tx', 'txt', 'txt250', 'txt43', 'txt82228', 'txtauction', 'txtin', 'txting', 'txtno', 'txts', 'txtstar', 'txtstop', 'txttowin', 'txtx', 'tyler', 'type', 'types', 'typical', 'tyrone', 'u2moro', 'u4', 'uawake', 'ubandu', 'ugh', 'ugo', 'uh', 'uhhhhrmm', 'uin', 'ujhhhhhhh', 'uk', 'uks', 'ultimate', 'ultimatum', 'um', 'umma', 'ummmmmaah', 'un', 'unable', 'unbreakable', 'unclaimed', 'uncle', 'uncles', 'unconscious', 'unconvinced', 'uncountable', 'uncut', 'under', 'understand', 'understanding', 'understood', 'underwear', 'undrstnd', 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'valuing', 'varaya', 'vargu', 'various', 'varma', 'varunnathu', 'vary', 'vasai', 'vat', 'vatian', 'vava', 'vco', 'vday', 've', 'vegas', 'vegetables', 'veggie', 'vehicle', 'velachery', 'velly', 'velusamy', 'venaam', 'verified', 'verify', 'verifying', 'version', 'versus', 'very', 'vettam', 'vewy', 'via', 'vibrant', 'vibrate', 'vibrator', 'vic', 'victoria', 'victors', 'vid', 'video', 'videochat', 'videophones', 'videos', 'vijay', 'vijaykanth', 'vikky', 'vill', 'village', 'vinobanagar', 'violence', 'vip', 'virgil', 'virgin', 'virgins', 'virtual', 'visa', 'visionsms', 'visit', 'visitor', 'visitors', 'vitamin', 'viva', 'vivek', 'vivekanand', 'vl', 'voda', 'vodafone', 'vodka', 'voice', 'voicemail', 'voila', 'volcanoes', 'vomit', 'vomiting', 'vote', 'voted', 'vouch4me', 'voucher', 'vouchers', 'vry', 'vs', 'vth', 'vu', 'w1', 'w111wx', 'w14rg', 'w1a', 'w1j', 'w1j6hl', 'w1jhl', 'w4', 'w45wq', 'wa', 'wa14', 'waaaat', 'wad', 'wadebridge', 'wah', 'wahala', 'wahay', 'waheed', 'waheeda', 'wahleykkum', 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'weiyi', 'welcome', 'welcomes', 'well', 'wellda', 'welp', 'wen', 'went', 'wer', 'were', 'werebored', 'weren', 'werethe', 'wesley', 'western', 'westlife', 'westonzoyland', 'westshore', 'wet', 'wetherspoons', 'whassup', 'what', 'whatever', 'whats', 'whatsup', 'wheat', 'wheel', 'when', 'whenever', 'whenevr', 'whens', 'where', 'whereare', 'wherever', 'wherevr', 'wherre', 'whether', 'which', 'while', 'whilltake', 'whispers', 'white', 'whn', 'who', 'whoever', 'whole', 'whom', 'whos', 'whose', 'whr', 'why', 'wicked', 'wicklow', 'wid', 'widelive', 'wif', 'wife', 'wifes', 'wifi', 'wihtuot', 'wikipedia', 'wil', 'wild', 'wildlife', 'will', 'willing', 'willpower', 'win', 'win150ppmx3age16', 'wind', 'window', 'windows', 'winds', 'windy', 'wine', 'wining', 'winner', 'winnersclub', 'winning', 'wins', 'winterstone', 'wipe', 'wipro', 'wisdom', 'wise', 'wish', 'wisheds', 'wishes', 'wishin', 'wishing', 'wishlist', 'wiskey', 'wit', 'with', 'wither', 'within', 'without', 'witin', 'witot', 'witout', 'wiv', 'wizzle', 'wk', 'wkend', 'wkg', 'wkly', 'wknd', 'wks', 'wlcome', 'wld', 'wml', 'wn', 'wnevr', 'wnt', 'wo', 'woah', 'wocay', 'woke', 'woken', 'woman', 'women', 'won', 'wonder', 'wonderful', 'wondering', 'wonders', 'wont', 'woo', 'woodland', 'woohoo', 'woould', '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', 'would', 'woulda', 'wouldn', 'wounds', 'wow', 'wrc', 'wrecked', 'wrench', 'wrenching', 'wright', 'write', 'wrk', 'wrkin', 'wrking', 'wrks', 'wrld', 'wrnog', 'wrong', 'wrongly', 'wrote', 'ws', 'wt', 'wtc', 'wtf', 'wth', 'wthout', 'wtlp', 'wud', 'wuld', 'wuldnt', 'wun', 'www', 'wylie', 'x29', 'x49', 'xafter', 'xam', 'xavier', 'xchat', 'xclusive', 'xin', 'xmas', 'xoxo', 'xt', 'xuhui', 'xx', 'xxsp', 'xxuk', 'xxx', 'xxxmobilemovieclub', 'xxxx', 'xxxxx', 'xxxxxx', 'xxxxxxx', 'xxxxxxxx', 'xy', 'y87', 'ya', 'yah', 'yahoo', 'yalru', 'yam', 'yan', 'yar', 'yarasu', 'yaxx', 'yaxxx', 'yay', 'yck', 'yeah', 'year', 'years', 'yeh', 'yelling', 'yellow', 'yelow', 'yen', 'yeovil', 'yep', 'yer', 'yes', 'yest', 'yesterday', 'yet', 'yetunde', 'yhl', 'yifeng', 'yijue', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yor', 'yorge', 'you', 'youdoing', 'youi', 'younger', 'youphone', 'your', 'youre', 'yourinclusive', 'yourjob', 'yours', 'yourself', 'youwanna', 'yoville', 'yoyyooo', 'yr', 'yrs', 'ystrday', 'ything', 'yummmm', 'yummy', 'yun', 'yunny', 'yuo', 'yuou', 'yup', 'yupz', 'zac', 'zaher', 'zebra', 'zed', 'zeros', 'zhong', 'zindgi', 'zoe', 'zogtorius', 'zoom', 'zouk', 'zyada', 'ú1', '〨ud']
抽取训练集的短信文本 (0, 1001) 0.20009309431187025
(0, 5455) 0.2373503118913133
(0, 4859) 0.21905199712263723
(0, 4643) 0.20009309431187025
(0, 447) 0.26001483559551203
(0, 6800) 0.21270348454835966
(0, 1303) 0.1855527591282815
(0, 4889) 0.26001483559551203
(0, 6653) 0.08751458366369526
(0, 6956) 0.17092654603333898
(0, 1832) 0.1808736483221684
(0, 5652) 0.233499042668188
(0, 7464) 0.16247169481077797
(0, 6624) 0.14643775667784498
(0, 6694) 0.12627449995071288
(0, 6801) 0.2826793592997107
(0, 5582) 0.15576000194651374
(0, 6799) 0.1855527591282815
(0, 4652) 0.1531687063129556
(0, 7267) 0.20906662918174937
(0, 7370) 0.1239002156417896
(0, 2848) 0.23005396495369115
(0, 7027) 0.12654927464291724
(0, 7011) 0.13019424667729967
(0, 2967) 0.2826793592997107
: :
(4176, 6774) 0.14585716903385093
(4177, 6869) 0.4807846447077556
(4177, 979) 0.44344163952220617
(4177, 3058) 0.36027677244939754
(4177, 4724) 0.41466101922375376
(4177, 4299) 0.21853321622168898
(4177, 4812) 0.2853597928079453
(4177, 7327) 0.37586639405148836
(4178, 4051) 0.3235761001240709
(4178, 6021) 0.37866618387226986
(4178, 2531) 0.36702566893440586
(4178, 2248) 0.36702566893440586
(4178, 7233) 0.26261638754103267
(4178, 5118) 0.28102168071444195
(4178, 3676) 0.45013293743383914
(4178, 3433) 0.20771052777884438
(4178, 1547) 0.18721935802328127
(4178, 6694) 0.20145700519918117
(4178, 6774) 0.11469461885584192
(4179, 5127) 0.6156109948291256
(4179, 1732) 0.5744359650089301
(4179, 1866) 0.3584262944407417
(4179, 1025) 0.30118844632621306
(4179, 7528) 0.19259815888028214
(4179, 6774) 0.1864630944684757
抽取测试集的短信文本 (0, 7341) 0.17144853826222842
(0, 6846) 0.3365279781794401
(0, 6774) 0.10009011035829249
(0, 6694) 0.17580470717794366
(0, 5825) 0.3935581768791935
(0, 5737) 0.23507982222698545
(0, 4993) 0.3518453837813197
(0, 4872) 0.16637604492047084
(0, 4719) 0.16309095528125545
(0, 3552) 0.13197069547458676
(0, 3279) 0.20283028307171092
(0, 3269) 0.15351259752288254
(0, 2799) 0.3435454620302035
(0, 1999) 0.29353274718121336
(0, 1272) 0.2197624166251059
(0, 914) 0.19824129683459432
(0, 903) 0.23302578051743916
(1, 7528) 0.17356229266917425
(1, 6774) 0.16803360095593692
(1, 6134) 0.2735594095854812
(1, 4658) 0.4178287662511174
(1, 4299) 0.23529447998652414
(1, 3857) 0.47405586966085483
(1, 2728) 0.42803403208906887
(1, 1980) 0.4227611851531592
: :
(1392, 4556) 0.10217993830617908
(1392, 4299) 0.09802393533518185
(1392, 3670) 0.10197251646008026
(1392, 3659) 0.09545203832714272
(1392, 3323) 0.1594368022701567
(1392, 2868) 0.22097670558455443
(1392, 2715) 0.2752544200689884
(1392, 2308) 0.1196698273419931
(1392, 2307) 0.22736588810558353
(1392, 1951) 0.246080510471533
(1392, 1343) 0.39781498985816566
(1393, 7398) 0.2576563828452968
(1393, 6774) 0.11191927184127015
(1393, 6694) 0.19658220720498007
(1393, 6653) 0.13624136326771477
(1393, 4286) 0.44007089637350827
(1393, 3659) 0.15260668314345713
(1393, 3269) 0.171655501944399
(1393, 1935) 0.2732519755035495
(1393, 1577) 0.16728071091000699
(1393, 1160) 0.36950332973479644
(1393, 1107) 0.3254717257521903
(1393, 522) 0.2793677702221603
(1393, 399) 0.31574629980366664
(1393, 308) 0.3075842593586148
Prediction: 0. Real: 0. Message: twenty past five he said will this train have been to durham already or not coz i am in a reserved seat
Prediction: 0. Real: 0. Message: So i could kiss and feel you next to me...
Prediction: 0. Real: 0. Message: Networking job is there.
Prediction: 0. Real: 0. Message: Wat would u like 4 ur birthday?
Prediction: 1. Real: 1. Message: A £400 XMAS REWARD IS WAITING FOR YOU! Our computer has randomly picked you from our loyal mobile customers to receive a £400 reward. Just call 09066380611
Prediction: 0. Real: 0. Message: How much it will cost approx . Per month.
Prediction: 0. Real: 0. Message: <#> great loxahatchee xmas tree burning update: you can totally see stars here
Prediction: 0. Real: 0. Message: This girl does not stay in bed. This girl doesn't need recovery time. Id rather pass out while having fun then be cooped up in bed
Prediction: 0. Real: 0. Message: You know, wot people wear. T shirts, jumpers, hat, belt, is all we know. We r at Cribbs
Prediction: 0. Real: 0. Message: I love your ass! Do you enjoy doggy style? :)
Accuracy: 0.9684361549497847
[[1222 3]
[ 41 128]]
D:\Program Files\JetBrains\PyCharm 2020.2.31\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
self.figure.tight_layout()
Accuracy: 0.9616077481060454 [0.96502242 0.96143498 0.96143498 0.95695067 0.96319569]
Precision: 0.9927499693289168 [0.98245614 1. 1. 0.99038462 0.99090909]
Recall: 0.7189261744966442 [0.75167785 0.7114094 0.71333333 0.68666667 0.73154362]
F1: 0.8336981728568773 [0.85171103 0.83137255 0.83268482 0.81102362 0.84169884]
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'worried', 'worries', 'worry', 'worse', 'worst', 'worth', 'worthless', 'wot', 'wotu', 'wotz', 'woul', 'would', 'woulda', 'wouldn', 'wow', 'wrench', 'wrenching', 'wright', 'write', 'wrk', 'wrkin', 'wrking', 'wrks', 'wrld', 'wrnog', 'wrong', 'wrongly', 'wrote', 'ws', 'wt', 'wtc', 'wtf', 'wud', 'wudn', 'wuld', 'wuldnt', 'wun', 'www', 'wylie', 'x2', 'x29', 'x49', 'xam', 'xavier', 'xchat', 'xclusive', 'xin', 'xmas', 'xoxo', 'xt', 'xuhui', 'xx', 'xxsp', 'xxuk', 'xxx', 'xxxmobilemovieclub', 'xxxx', 'xxxxx', 'xxxxxxx', 'xxxxxxxx', 'xxxxxxxxxxxxxx', 'xy', 'ya', 'yah', 'yahoo', 'yalrigu', 'yalru', 'yam', 'yan', 'yar', 'yarasu', 'yards', 'yavnt', 'yaxx', 'yaxxx', 'yay', 'yck', 'yeah', 'year', 'years', 'yeesh', 'yeh', 'yelling', 'yellow', 'yelow', 'yen', 'yeovil', 'yep', 'yer', 'yes', 'yest', 'yesterday', 'yet', 'yetunde', 'yhl', 'yi', 'yifeng', 'yijue', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yor', 'yorge', 'you', 'youdoing', 'youi', 'young', 'younger', 'youphone', 'your', 'youre', 'yourinclusive', 'yourjob', 'yours', 'yourself', 'youwanna', 'yoville', 'yowifes', 'yr', 'yrs', 'yummy', 'yun', 'yunny', 'yuo', 'yuou', 'yup', 'yupz', 'zac', 'zaher', 'zealand', 'zebra', 'zed', 'zeros', 'zhong', 'zoe', 'zogtorius', 'zoom', 'zouk', 'èn', 'ú1', '〨ud']
抽取训练集的短信文本 (0, 909) 0.20438401949794643
(0, 6833) 0.26490232092420557
(0, 7383) 0.24130996890465167
(0, 5032) 0.2949842219347177
(0, 4709) 0.12207898456993951
(0, 5985) 0.32069689665611123
(0, 4195) 0.20519933909390178
(0, 6691) 0.1820459927696977
(0, 1089) 0.286706589547201
(0, 937) 0.10917383581882723
(0, 2202) 0.6113118923017103
(0, 4754) 0.2546938713271099
(0, 1515) 0.13084210994476853
(1, 4249) 0.14118087628019105
(1, 2822) 0.14629051984111227
(1, 3136) 0.1906265093944309
(1, 4098) 0.19114696889804964
(1, 5766) 0.322235607400984
(1, 5837) 0.2196112303334554
(1, 3466) 0.17162698689595773
(1, 1066) 0.2372172644069237
(1, 3943) 0.24172581417546002
(1, 3284) 0.46113587313077425
(1, 1619) 0.322235607400984
(1, 7397) 0.10258867159144483
: :
(4178, 2174) 0.29587238659195575
(4178, 6518) 0.21584922669731407
(4178, 3508) 0.14232421165971526
(4178, 4012) 0.19452039225189174
(4178, 2999) 0.1761917565269771
(4178, 3966) 0.200451096962158
(4178, 6656) 0.10514443008907012
(4178, 3136) 0.1999053039578211
(4178, 4098) 0.200451096962158
(4178, 7397) 0.10758220166893535
(4179, 3798) 0.3609719039682721
(4179, 2895) 0.2981703164161335
(4179, 7062) 0.2741464318753719
(4179, 5995) 0.3086637558525794
(4179, 925) 0.23385022603005992
(4179, 5625) 0.2692284923851668
(4179, 2496) 0.25551205932966314
(4179, 2300) 0.37865109514128586
(4179, 1138) 0.2692284923851668
(4179, 1591) 0.22493260477766577
(4179, 6541) 0.1406949635892785
(4179, 6239) 0.19762841126772965
(4179, 3620) 0.13409104739028252
(4179, 6656) 0.09140727150247424
(4179, 4249) 0.2574194538657163
抽取测试集的短信文本 (0, 7346) 0.28328827579764726
(0, 7176) 0.18398120533331602
(0, 6810) 0.20630799881988768
(0, 6039) 0.16317590772419102
(0, 4662) 0.1593977683745911
(0, 4249) 0.14175587303742238
(0, 4076) 0.20318318734128268
(0, 3756) 0.3283931566373565
(0, 3632) 0.34703906807374796
(0, 3466) 0.1723259835555528
(0, 3268) 0.27302669024514475
(0, 2738) 0.30807482540749814
(0, 2736) 0.2778718490629148
(0, 2070) 0.3151636719356976
(0, 1817) 0.3151636719356976
(0, 1568) 0.16317590772419102
(1, 7252) 0.4589579968595217
(1, 7075) 0.37910887146869665
(1, 5676) 0.658595196673499
(1, 4754) 0.3644992833280652
(1, 4267) 0.2811168085766989
(2, 6916) 0.24781657150106387
(2, 5774) 0.27402251436281294
(2, 4447) 0.3168782724022855
(2, 4349) 0.4308516941854713
: :
(1390, 933) 0.30678444760538537
(1391, 7215) 0.32374827188468575
(1391, 5823) 0.5297370126280292
(1391, 4662) 0.3000554093433684
(1391, 3620) 0.27800249839888996
(1391, 3229) 0.2908128901527511
(1391, 2733) 0.5057034328659069
(1391, 1229) 0.3270153161856651
(1392, 6556) 0.4806022974126301
(1392, 4736) 0.7142856960057783
(1392, 3620) 0.36981346017368916
(1392, 3610) 0.3493642524323374
(1393, 7403) 0.15556092739073174
(1393, 7169) 0.19161424213902464
(1393, 6931) 0.2804600705806564
(1393, 6656) 0.10510050397885476
(1393, 5124) 0.4150471664417337
(1393, 4734) 0.4150471664417337
(1393, 4709) 0.1579950949203299
(1393, 4076) 0.21212024975352523
(1393, 4066) 0.3251834369221765
(1393, 3610) 0.1456530512824301
(1393, 3025) 0.24234307411184083
(1393, 2671) 0.3955811100099777
(1393, 1154) 0.3045018927231745
(1400, 3473)
(1400,)
['intelligent', 'science', 'fiction', 'thriller', 'social', 'satire', 'class', 'style', 'set', 'old']
bad?
-N-> class 1 (333 for class 0, 533 for class 1)
-Y-> class 0 (358 for class 0, 176 for class 1)
0.6364285714285715
bad?
-N-> worst?
| -N-> class 1 (281 for class 0, 514 for class 1)
| -Y-> class 0 (52 for class 0, 19 for class 1)
-Y-> stupid?
| -N-> class 0 (281 for class 0, 168 for class 1)
| -Y-> class 0 (77 for class 0, 8 for class 1)
0.66
bad?
-N-> worst?
| -N-> many?
| | -N-> class 1 (204 for class 0, 274 for class 1)
| | -Y-> class 1 (77 for class 0, 240 for class 1)
| -Y-> present?
| | -N-> class 0 (52 for class 0, 13 for class 1)
| | -Y-> class 1 (0 for class 0, 6 for class 1)
-Y-> stupid?
| -N-> wonderfully?
| | -N-> class 0 (280 for class 0, 153 for class 1)
| | -Y-> class 1 (1 for class 0, 15 for class 1)
| -Y-> bob?
| | -N-> class 0 (76 for class 0, 4 for class 1)
| | -Y-> class 1 (1 for class 0, 4 for class 1)
0.6764285714285714
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'08718730555', '08718730666', '08718738001', '08718738002', '08719180219', '08719180248', '08719181259', '08719181503', '08719181513', '08719839835', '08719899229', '08719899230', '09', '09041940223', '09050000555', '09050000878', '09050000928', '09050001295', '09050001808', '09050002311', '09050003091', '09050090044', '09050280520', '09056242159', '09058091854', '09058091870', '09058094454', '09058094455', '09058094565', '09058094594', '09058094597', '09058094599', '09058095107', '09058095201', '09058097189', '09058098002', '09058099801', '09061104283', '09061209465', '09061213237', '09061221061', '09061221066', '09061701444', '09061701461', '09061701851', '09061701939', '09061702893', '09061743386', '09061743806', '09061743810', '09061743811', '09061744553', '09061749602', '09061790121', '09063442151', '09063458130', '0906346330', '09064011000', '09064012103', '09064012160', '09064015307', '09064017305', '09064018838', '09064019014', '09065069154', '09065171142', '09065174042', '09065394514', '09065394973', '09065989182', '09066350750', '09066358152', '09066358361', '09066362206', '09066362231', '09066364311', '09066364349', '09066368327', '09066368470', '09066368753', '09066380611', '09066382422', '09066612661', '09066649731from', '09066660100', '09071512432', '09071512433', '09071517866', '09077818151', '09090204448', '09090900040', '09094100151', '09094646631', '09094646899', '09095350301', '09099725823', '09099726395', '09099726429', '09099726481', '09099726553', '09111030116', '09111032124', '09701213186', '0a', '0quit', '10', '100', '1000', '1000call', '1000s', '100p', '100percent', '100txt', '1013', '1030', '10am', '10k', '10p', '10ppm', '10th', '11', '1131', '114', '1146', '118p', '11mths', '11pm', '12', '120p', '121', '1225', '123', '125', '1250', '125gift', '128', '12hours', '12hrs', '12mths', '13', '130', '1327', '139', '14', '1405', '145', '1450', '146tf150p', '14tcr', '14thmarch', '15', '150', '1500', '150p', '150p16', '150pm', 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'tayseer', 'tb', 'tbs', 'tc', 'tcr', 'tcs', 'tddnewsletter', 'tea', 'teach', 'teacher', 'teachers', 'teaches', 'teaching', 'team', 'teams', 'tear', 'tears', 'tease', 'teasing', 'tech', 'technical', 'technologies', 'tee', 'teenager', 'teeth', 'tel', 'telediscount', 'telephone', 'teletext', 'tell', 'telling', 'tellmiss', 'tells', 'telly', 'telphone', 'telugu', 'temales', 'temp', 'temper', 'temple', 'ten', 'tenants', 'tenerife', 'tension', 'teresa', 'term', 'terms', 'termsapply', 'terrible', 'terrific', 'terror', 'terrorist', 'terry', 'tescos', 'tessy', 'test', 'testing', 'tests', 'texas', 'text', 'text82228', 'textand', 'textbook', 'textbuddy', 'textcomp', 'texted', 'textin', 'texting', 'textoperator', 'textpod', 'texts', 'tgxxrz', 'th', 'than', 'thangam', 'thank', 'thanks', 'thanksgiving', 'thanku', 'thankyou', 'thanx', 'that', 'that2worzels', 'thatmum', 'thats', 'the', 'the4th', 'theater', 'theatre', 'thecd', 'thedailydraw', 'their', 'thekingshead', 'them', 'themed', 'themes', 'themob', 'then', 'thenampet', 'theoretically', 'theory', 'thepub', 'there', 'theres', 'these', 'thesedays', 'thesis', 'they', 'theyre', 'thgt', 'thia', 'thing', 'things', 'think', 'thinked', 'thinkin', 'thinking', 'thinks', 'thinkthis', 'thinl', 'thirtyeight', 'thirunelvali', 'this', 'thk', 'thm', 'thnk', 'tho', 'those', 'thot', 'thou', 'though', 'thought', 'thoughts', 'thousands', 'thout', 'thread', 'threats', 'three', 'threw', 'thriller', 'throat', 'through', 'throw', 'throwin', 'thrown', 'throws', 'thru', 'ths', 'tht', 'thts', 'thuglyfe', 'thurs', 'thursday', 'thus', 'thx', 'thy', 'tick', 'ticket', 'tickets', 'tiger', 'tight', 'tihs', 'tiime', 'til', 'till', 'tim', 'time', 'times', 'timin', 'timing', 'timings', 'tiny', 'tips', 'tired', 'tirunelvali', 'tirupur', 'tis', 'tissco', 'title', 'titles', 'tiwary', 'tix', 'tiz', 'tke', 'tkts', 'tlk', 'tlp', 'tm', 'tmorrow', 'tmr', 'tmrw', 'tms', 'tmw', 'tnc', 'tncs', 'to', 'toa', 'toaday', 'tobed', 'tocall', 'toclaim', 'today', 'todays', 'todo', 'tog', 'together', 'tohar', 'toilet', 'tok', 'toking', 'tol', 'told', 'tolerance', 'tolerat', 'toll', 'tom', 'tomarrow', 'tome', 'tomeandsaid', 'tomo', 'tomorro', 'tomorrow', 'tomorw', 'tone', 'tones', 'tones2u', 'tones2you', 'tonght', 'tongued', 'tonight', 'tonights', 'tonite', 'tons', 'tonsolitusaswell', 'too', 'took', 'tookplace', 'tool', 'tooo', 'toot', 'tooth', 'toothpaste', 'tootsie', 'top', 'topic', 'toplay', 'topped', 'tops', 'tor', 'torch', 'torrents', 'tortilla', 'torture', 'tosend', 'toshiba', 'toss', 'tot', 'total', 'totally', 'totes', 'touch', 'touched', 'tough', 'toughest', 'tour', 'towards', 'town', 'toyota', 'tp', 'track', 'trackmarque', 'trade', 'traditions', 'traffic', 'train', 'trained', 'training', 'trainners', 'trains', 'tram', 'tranquility', 'transaction', 'transcribing', 'transfer', 'transfered', 'transferred', 'transfr', 'transfred', 'transport', 'trav', 'travel', 'traveling', 'travelling', 'treadmill', 'treasure', 'treat', 'treated', 'treats', 'trebles', 'tree', 'trek', 'trends', 'trial', 'tried', 'trip', 'triple', 'trips', 'trishul', 'tron', 'trouble', 'troubleshooting', 'trouser', 'truble', 'truck', 'true', 'true18', 'truffles', 'truly', 'truro', 'trust', 'trusting', 'truth', 'try', 'tryin', 'trying', 'ts', 'tsandcs', 'tscs', 'tscs087147403231winawk', 'tsunami', 'tsunamis', 'tt', 'ttyl', 'tue', 'tues', 'tuesday', 'tui', 'tuition', 'tul', 'tulip', 'tunde', 'tunji', 'turkeys', 'turn', 'turned', 'turning', 'turns', 'tuth', 'tv', 'twat', 'twelve', 'twenty', 'twice', 'twiggs', 'twilight', 'twittering', 'two', 'tx', 'txt', 'txt250', 'txt43', 'txt82228', 'txtauction', 'txtin', 'txting', 'txtno', 'txts', 'txtstop', 'txtx', 'tyler', 'type', 'types', 'typical', 'tyrone', 'u4', 'uawake', 'ubandu', 'ubi', 'ugadi', 'ugh', 'ugo', 'uh', 'uk', 'uks', 'ultimately', 'ultimatum', 'umma', 'ummifying', 'ummma', 'ummmmmaah', 'un', 'unable', 'unbelievable', 'unbreakable', 'unclaimed', 'uncle', 'uncles', 'uncomfortable', 'unconditionally', 'unconscious', 'unconsciously', 'unconvinced', 'uncountable', 'uncut', 'under', 'underdtand', 'understand', 'understanding', 'understood', 'undrstnd', 'uneventful', 'unfolds', 'unfortunately', 'unfortuntly', 'unhappiness', 'unhappy', 'uni', 'unicef', 'uniform', 'unintentionally', 'unique', 'united', 'units', 'university', 'unjalur', 'unkempt', 'unknown', 'unless', 'unlike', 'unlimited', 'unmits', 'unnecessarily', 'unni', 'unrecognized', 'unredeemed', 'unsecured', 'unsold', 'unspoken', 'unsub', 'unsubscribe', 'unsubscribed', 'untamed', 'until', 'unusual', 'up', 'up4', 'upd8', 'update', 'update_now', 'upgrade', 'upload', 'uploaded', 'upon', 'upping', 'ups', 'upset', 'upstairs', 'upto', 'uptown', 'ur', 'urawinner', 'ure', 'urfeeling', 'urgent', 'urgently', 'urgh', 'urgnt', 'urgoin', 'urination', 'url', 'urmom', 'urn', 'urself', 'us', 'usa', 'usb', 'usc', 'use', 'used', 'useful', 'useless', 'user', 'uses', 'usf', 'usher', 'using', 'usmle', 'usps', 'usual', 'usually', 'uterus', 'utter', 'uttered', 'uup', 'uv', 'uve', 'uwana', 'uwant', 'vaazhthukkal', 'vague', 'vale', 'valentine', 'valentines', 'valid', 'valid12hrs', 'value', 'valued', 'values', 'valuing', 'various', 'varunnathu', 'vary', 'vasai', 'vat', 'vava', 'vco', 'vday', 've', 'vegas', 'vegetables', 'veggie', 'velachery', 'velly', 'velusamy', 'venaam', 'verified', 'verify', 'verifying', 'version', 'very', 'vettam', 'via', 'vibrant', 'vibrate', 'vibrator', 'vic', 'victoria', 'video', 'videochat', 'videophones', 'videos', 'videosound', 'videosounds', 'view', 'vijay', 'vikky', 'villa', 'village', 'vinobanagar', 'violated', 'violence', 'violet', 'vip', 'vipclub4u', 'virgil', 'virgin', 'virtual', 'visa', 'visionsms', 'visit', 'visiting', 'visitor', 'visitors', 'vital', 'viva', 'vivek', 'vivekanand', 'vl', 'voda', 'vodafone', 'vodka', 'voice', 'voicemail', 'volcanoes', 'vomit', 'vomitin', 'vomiting', 'vote', 'vouch4me', 'voucher', 'vouchers', 'vpod', 'vry', 'vs', 'vth', 'vu', 'w1', 'w111wx', 'w14rg', 'w1a', 'w1j', 'w1j6hl', 'w1jhl', 'w1t1jy', 'w45wq', 'w8in', 'wa', 'wa14', 'waaaat', 'wah', 'wahala', 'wahay', 'waheed', 'waheeda', 'wahleykkum', 'waht', 'wait', 'waited', 'waitin', 'waiting', 'wake', 'waking', 'wales', 'waliking', 'walk', 'walkabout', 'walked', 'walkin', 'walking', 'walks', 'wall', 'wallet', 'wallpaper', 'walls', 'walmart', 'walsall', 'wamma', 'wan', 'wan2', 'wana', 'wanna', 'wannatell', 'want', 'want2come', 'wanted', 'wanting', 'wants', 'wap', 'waqt', 'warm', 'warming', 'warned', 'warning', 'warranty', 'warwick', 'was', 'washob', 'wasn', 'wasnt', 'waste', 'wasted', 'wasting', 'wat', 'watch', 'watched', 'watches', 'watchin', 'watching', 'water', 'watever', 'wating', 'watr', 'wats', 'watts', 'wavering', 'waves', 'way', 'wc1n', 'wc1n3xx', 'we', 'weak', 'weakness', 'weaknesses', 'weapon', 'wear', 'wearing', 'weasels', 'weather', 'web', 'web2mobile', 'webadres', 'webeburnin', 'webpage', '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', 'went', 'wenwecan', 'wer', 'were', 'werethe', 'wesley', 'wesleys', 'west', 'westlife', 'westonzoyland', 'westshore', 'wet', 'wetherspoons', 'wewa', 'whassup', 'what', 'whatever', 'whats', 'whatsup', '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', 'wid', 'widelive', 'wif', 'wife', 'wifi', 'wihtuot', 'wil', 'wild', 'wildest', 'wildlife', 'will', 'willing', 'willpower', 'win', 'wind', 'window', 'windows', 'winds', 'windy', 'wine', 'wined', 'wining', 'winner', 'winnersclub', 'winning', 'wins', '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', 'woodland', 'woods', 'woohoo', 'woould', 'woozles', 'word', 'words', 'work', 'workand', 'workin', 'working', 'workout', 'works', 'world', 'worlds', 'worms', 'worried', 'worries', 'worry', 'worrying', 'worse', 'worst', 'worth', 'worthless', 'wot', 'wotz', 'would', 'woulda', 'wouldn', 'wounds', 'wow', 'wrc', 'wrench', 'wrenching', 'wright', 'write', 'wrk', 'wrkin', 'wrks', 'wrld', 'wrnog', 'wrong', 'wrote', 'ws', 'wt', 'wtc', 'wtf', 'wth', 'wthout', 'wtlp', 'wud', 'wudn', 'wuld', 'wuldnt', 'wun', 'www', 'wylie', 'x49', 'xafter', 'xam', 'xavier', 'xchat', 'xclusive', 'xin', 'xmas', 'xt', 'xuhui', 'xx', 'xxx', 'xxxmobilemovieclub', 'xxxx', 'xxxxx', 'xxxxxxx', 'xxxxxxxx', 'xxxxxxxxxxxxxx', 'xy', 'y87', 'ya', 'yahoo', 'yalrigu', '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', 'yetty', 'yetunde', 'yhl', 'yi', 'yijue', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yor', 'yorge', 'you', 'youdoing', 'youi', 'young', 'younger', 'youphone', 'your', 'youre', 'yourinclusive', 'yourjob', 'yours', 'yourself', 'youwanna', 'yoville', 'yowifes', 'yoyyooo', 'yr', 'yrs', 'ystrday', 'ything', 'yummmm', 'yummy', 'yun', 'yunny', 'yuo', 'yuou', 'yup', 'yupz', 'zac', 'zaher', 'zealand', 'zebra', 'zed', 'zhong', 'zindgi', 'zoe', 'zogtorius', 'zoom', 'zouk', 'zyada', 'èn']
抽取训练集的短信文本 (0, 3314) 0.3413769603419699
(0, 5235) 0.38707834428362736
(0, 2775) 0.5356511337847825
(0, 4720) 0.20479029589793457
(0, 1609) 0.3270146098248118
(0, 6450) 0.28965317406491353
(0, 7257) 0.462529358668921
(1, 6815) 0.5865489053964534
(1, 6067) 0.27915304448946415
(1, 4670) 0.2745785846244353
(1, 5654) 0.5032508714736004
(1, 6678) 0.17445130413603566
(1, 3274) 0.2622523191355785
(1, 3153) 0.38751720497345177
(2, 6580) 0.2879565400251049
(2, 3903) 0.3269093148541924
(2, 3658) 0.2178086092334525
(2, 737) 0.31037989629024004
(2, 7441) 0.15407349238799528
(2, 1087) 0.25453304999948917
(2, 1333) 0.5433431399701582
(2, 4029) 0.27990868880242087
(2, 2778) 0.38774256301176635
(2, 6564) 0.23236697065557918
(3, 2355) 0.5340800173435639
: :
(4178, 6615) 0.2893602351866251
(4178, 2127) 0.2893602351866251
(4178, 3977) 0.2893602351866251
(4178, 3747) 0.2893602351866251
(4178, 7122) 0.25258882048554626
(4178, 1616) 0.2661600555707996
(4178, 7094) 0.2661600555707996
(4178, 2439) 0.22191985124495336
(4178, 2530) 0.20910080026557365
(4178, 4439) 0.17967163143142323
(4178, 6927) 0.13054363644636868
(4178, 6568) 0.09018756317476724
(4178, 3542) 0.09833877979740435
(4178, 4766) 0.15204154496034772
(4178, 6564) 0.1138264225180134
(4178, 4720) 0.11062828853970848
(4179, 2138) 0.5714026442965389
(4179, 4602) 0.39136465402044357
(4179, 4093) 0.31801118964515634
(4179, 2311) 0.2700776081830415
(4179, 6684) 0.3282126567865389
(4179, 2124) 0.303256280450544
(4179, 7441) 0.1620302061259823
(4179, 6067) 0.25280670740016115
(4179, 3274) 0.237501064801119
抽取测试集的短信文本 (0, 6568) 0.16880465573847853
(0, 6081) 0.30381959787161456
(0, 5024) 0.40752769021047136
(0, 4720) 0.20706369597429533
(0, 4190) 0.40752769021047136
(0, 3425) 0.2446123579937412
(0, 1314) 0.5415974571840902
(0, 742) 0.38859055324533326
(1, 7441) 0.15385814315299945
(1, 7220) 0.5273581179491413
(1, 6927) 0.2661212630230658
(1, 6568) 0.1838529159627162
(1, 6392) 0.3675714154894786
(1, 5924) 0.4362679781632838
(1, 3177) 0.28437989603486713
(1, 2948) 0.43276812076820664
(2, 7441) 0.22852264643990602
(2, 7219) 0.13498939128520052
(2, 7128) 0.14975385909760264
(2, 7117) 0.21426082109594088
(2, 6678) 0.14854650745460457
(2, 6605) 0.13088503748415306
(2, 6595) 0.1994559690945599
(2, 6568) 0.09102444206178821
(2, 6067) 0.11885038638603114
: :
(1391, 2249) 0.20268927962597597
(1391, 2178) 0.17634562945494286
(1391, 1532) 0.09752863995565456
(1391, 1056) 0.12245301777197196
(1391, 511) 0.20268927962597597
(1392, 6105) 0.5714799063729071
(1392, 3515) 0.5955436621577455
(1392, 2365) 0.5645692721675123
(1393, 7331) 0.25270640977666997
(1393, 7197) 0.28509747245308265
(1393, 6678) 0.11095431894948259
(1393, 6649) 0.21577311627993134
(1393, 6605) 0.1955247611482171
(1393, 6003) 0.28509747245308265
(1393, 4744) 0.2018981555620324
(1393, 4658) 0.18063409824049964
(1393, 3835) 0.415814926864622
(1393, 3765) 0.1861815752846545
(1393, 3379) 0.22719397515315648
(1393, 3106) 0.21032851556022988
(1393, 2958) 0.33133795048920084
(1393, 1581) 0.3152672294319361
(1393, 1532) 0.18324774918883024
(1393, 1087) 0.18799054509224516
(1393, 935) 0.14686284135459776
Prediction: 0. Real: 0. Message: How abt making some of the pics bigger?
Prediction: 0. Real: 0. Message: Sure, whenever you show the fuck up >:(
Prediction: 0. Real: 0. Message: Cant believe i said so many things to you this morning when all i really wanted to say was good morning, i love you! Have a beautiful morning. See you in the library later.
Prediction: 0. Real: 0. Message: Okie but i scared u say i fat... Then u dun wan me already...
Prediction: 1. Real: 1. Message: For ur chance to win a £250 wkly shopping spree TXT: SHOP to 80878. T's&C's www.txt-2-shop.com custcare 08715705022, 1x150p/wk
Prediction: 0. Real: 0. Message: Stupid auto correct on my phone
Prediction: 0. Real: 0. Message: Sat right? Okay thanks...
Prediction: 0. Real: 0. Message: Then u going ikea str aft dat?
Prediction: 0. Real: 0. Message: What u talking bout early morning? It's almost noon where your at!
Prediction: 0. Real: 0. Message: Dnt worry...use ice pieces in a cloth pack.also take 2 tablets.
Accuracy: 0.9684361549497847
[[1197 14]
[ 30 153]]
D:\Program Files\JetBrains\PyCharm 2020.2.31\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
self.figure.tight_layout()
Accuracy: 0.9659131638904768 [0.967713 0.97130045 0.96233184 0.96233184 0.96588869]
Precision: 0.8872695386004354 [0.85443038 0.90277778 0.88732394 0.9037037 0.88811189]
Recall: 0.8568411633109619 [0.91275168 0.87919463 0.84666667 0.80666667 0.83892617]
F1: 0.8653786380139481 [0.88294314 0.87248322 0.84848485 0.85223368 0.8707483 ]
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'xchat', 'xin', 'xmas', 'xoxo', 'xt', 'xuhui', 'xx', 'xxsp', 'xxx', 'xxxmobilemovieclub', 'xxxx', 'xxxxx', 'xxxxxx', 'xxxxxxx', 'xxxxxxxx', 'xy', 'y87', 'ya', 'yah', 'yahoo', 'yalrigu', 'yam', 'yan', 'yar', 'yarasu', 'yay', 'yeah', 'year', 'years', 'yeh', 'yellow', 'yelow', 'yen', 'yeovil', 'yep', 'yer', 'yes', 'yest', 'yesterday', 'yet', 'yetty', 'yetunde', 'yhl', 'yi', 'yifeng', 'yijue', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yor', 'yorge', 'you', 'youdoing', 'youi', 'young', 'youphone', 'your', 'youre', 'yourinclusive', 'yourjob', 'yours', 'yourself', 'youuuuu', 'youwanna', 'yoville', 'yowifes', 'yr', 'yrs', 'ystrday', 'ything', 'yummmm', 'yummy', 'yun', 'yunny', 'yuo', 'yup', 'yupz', 'zac', 'zealand', 'zed', 'zeros', 'zhong', 'zindgi', 'zoe', 'zogtorius', 'zyada', 'èn', '〨ud']
抽取训练集的短信文本 (0, 2085) 0.27607466826579014
(0, 4770) 0.36740594504198965
(0, 4223) 0.3387569383456628
(0, 2144) 0.39886630011826907
(0, 4014) 0.3019017021181723
(0, 3426) 0.5671289088000346
(0, 4680) 0.31963295121718066
(1, 3435) 0.551301897429002
(1, 6524) 0.4243692809921442
(1, 7210) 0.2522407893525967
(1, 6863) 0.26302454408199044
(1, 1823) 0.28399497311533023
(1, 2270) 0.2494890420994012
(1, 7151) 0.33078384897454527
(1, 3996) 0.3617370910119172
(2, 7106) 0.2633293948278542
(2, 3841) 0.2834105980121715
(2, 6723) 0.35484507301178303
(2, 1741) 0.39952302947131363
(2, 4776) 0.2501628060778464
(2, 4297) 0.34555740239156585
(2, 2734) 0.3620617953742334
(2, 7334) 0.40785412402841476
(2, 3996) 0.29219395576629936
(3, 5727) 0.6122795695076909
: :
(4178, 7348) 0.3038573276223719
(4179, 4322) 0.24551220785550568
(4179, 314) 0.24551220785550568
(4179, 7200) 0.24551220785550568
(4179, 686) 0.24551220785550568
(4179, 443) 0.2258276533539101
(4179, 4088) 0.2258276533539101
(4179, 757) 0.23399748162867046
(4179, 5039) 0.2143129271270749
(4179, 6153) 0.219490642224505
(4179, 6877) 0.219490642224505
(4179, 1547) 0.17741483761389681
(4179, 4519) 0.18473687728526225
(4179, 4354) 0.17056612238534463
(4179, 1930) 0.16505232278366672
(4179, 510) 0.3081999351806264
(4179, 4360) 0.28010236685093687
(4179, 1655) 0.1946283726254793
(4179, 4515) 0.18157820169989364
(4179, 6879) 0.11039376158196135
(4179, 4534) 0.15585715537229763
(4179, 1534) 0.09441441328865802
(4179, 4272) 0.15467370041997028
(4179, 2983) 0.10558407076013078
(4179, 2888) 0.11473085858712205
抽取测试集的短信文本 (0, 7362) 0.20268765739427805
(0, 7047) 0.3933412323859542
(0, 6624) 0.19655579056222555
(0, 3270) 0.439400373129396
(0, 2270) 0.33467456318720723
(0, 1507) 0.48883918256454745
(0, 955) 0.4706619934522032
(1, 7176) 0.2898763002081244
(1, 7042) 0.2568678752212181
(1, 6624) 0.20172842601997695
(1, 6001) 0.16529656327264372
(1, 4621) 0.37950017319978213
(1, 4229) 0.3404757013544033
(1, 4205) 0.1430482273280053
(1, 3880) 0.25795163251203934
(1, 3834) 0.27344872266021425
(1, 3594) 0.1467197205283059
(1, 3583) 0.13783504839647093
(1, 3239) 0.35597279150257816
(1, 2047) 0.2213391022895105
(1, 1591) 0.37950017319978213
(2, 6947) 0.4563353486687782
(2, 6936) 0.2773321979704122
(2, 5749) 0.2994247295777017
(2, 5202) 0.3843777257661185
: :
(1391, 7183) 0.14383771430958026
(1391, 6949) 0.20718897536030956
(1391, 6863) 0.15119328777421642
(1391, 6593) 0.16140486040440452
(1391, 6572) 0.2902437699532094
(1391, 6504) 0.20607214504766436
(1391, 5630) 0.44266369171564846
(1391, 3583) 0.11509949189399726
(1391, 3036) 0.17224499019643771
(1391, 2691) 0.24393840144280207
(1391, 2565) 0.2353561939899432
(1391, 2211) 0.29725594401926886
(1391, 2112) 0.3324969227603611
(1391, 1918) 0.3324969227603611
(1391, 1245) 0.25252060889566097
(1391, 914) 0.11287117503479589
(1392, 6062) 0.6126642613082406
(1392, 3583) 0.21208420394619198
(1392, 3403) 0.5635423738540751
(1392, 3323) 0.31904898962403444
(1392, 2304) 0.40036299584618745
(1393, 7367) 0.36390777337953767
(1393, 7362) 0.2479156972847605
(1393, 7348) 0.6174877531100831
(1393, 5825) 0.6517804954682359
Running aboutKNeighborsClassifier() ...
Total samples: 5574, the number of spam: 747
neighbor number: 1, weight type: uniform
Accuracy: 0.9591104734576757
Precision: 1.0
Recall: 0.6724137931034483
f1 score: 0.8041237113402061
Running compareKNeighborsClassifier() ...
Total samples: 5574, the number of spam: 747
accuracy
K is 1 : 0.950286944045911
K is 3 : 0.9235294117647059
K is 5 : 0.9254662840746054
K is 15 : 0.9594691535150647
K is 30 : 0.9549497847919655
K is 50 : 0.9498565279770445
precision
K is 1 : 1.0
K is 3 : 1.0
K is 5 : 0.9984732824427482
K is 15 : 0.9955779942392518
K is 30 : 0.9984643338608523
K is 50 : 0.9975175315568023
recall
K is 1 : 0.6283738229177842
K is 3 : 0.4283131404039412
K is 5 : 0.43969687024462356
K is 15 : 0.700630913521562
K is 30 : 0.6649868914942602
K is 50 : 0.6277344670177598
f1 score
K is 1 : 0.7713156188108347
K is 3 : 0.5993611813199283
K is 5 : 0.588348944168702
K is 15 : 0.8215377439346504
K is 30 : 0.7976722621780028
K is 50 : 0.770012863527753
Running aboutRadiusNeighborsClassifier() ...
Total samples: 5574, the number of spam: 747
Radius: 1.2, weight type: uniform
Accuracy: 0.9117647058823529
Precision: 1.0
Recall: 0.35602094240837695
f1 score: 0.5250965250965252
Running compareRadiusNeighborsClassifier() ...
Total samples: 5574, the number of spam: 747
accuracy
Radius is 1.05 : 0.9395265423242467
Radius is 1.1 : 0.9409612625538021
Radius is 1.15 : 0.9450502152080344
Radius is 1.2 : 0.948493543758967
Radius is 1.25 : 0.9550215208034434
Radius is 1.3 : 0.9651362984218077
Radius is 1.35 : 0.9540172166427545
Radius is 1.4 : 0.9043041606886655
Radius is 1.45 : 0.9025824964131995
precision
Radius is 1.05 : 1.0
Radius is 1.1 : 1.0
Radius is 1.15 : 1.0
Radius is 1.2 : 0.9992307692307693
Radius is 1.25 : 0.9950343077886277
Radius is 1.3 : 0.9946462748992451
Radius is 1.35 : 0.9968348330020591
Radius is 1.4 : 0.9854129648036294
Radius is 1.45 : 1.0
recall
Radius is 1.05 : 0.5377652669145696
Radius is 1.1 : 0.5485382164421161
Radius is 1.15 : 0.579645777811933
Radius is 1.2 : 0.6061569280813405
Radius is 1.25 : 0.6582766911362523
Radius is 1.3 : 0.736004027220092
Radius is 1.35 : 0.6502301012406108
Radius is 1.4 : 0.2727664100054384
Radius is 1.45 : 0.2557658834944194
f1 score
Radius is 1.05 : 0.6978027985502955
Radius is 1.1 : 0.7072028725652206
Radius is 1.15 : 0.7325151404246302
Radius is 1.2 : 0.7533209317429544
Radius is 1.25 : 0.791204128717385
Radius is 1.3 : 0.8452903178220241
Radius is 1.35 : 0.786131357432836
Radius is 1.4 : 0.42686750808450447
Radius is 1.45 : 0.4069425093882004
Total samples: 5574, the number of spam: 747
GaussianNB
Accuracy: 0.9074605451936872
Precision: 0.6057347670250897
Recall: 0.898936170212766
f1 score: 0.7237687366167023
Total samples: 5574, the number of spam: 747
accuracy
分类器 GaussianNB() : 0.900860832137733
分类器 BernoulliNB() : 0.9765423242467717
分类器 MultinomialNB() : 0.9554519368723099
分类器 ComplementNB() : 0.9787661406025825
precision
分类器 GaussianNB() : 0.5899441479580212
分类器 BernoulliNB() : 0.994749058131411
分类器 MultinomialNB() : 1.0
分类器 ComplementNB() : 0.9449865127338877
recall
分类器 GaussianNB() : 0.8940384713414163
分类器 BernoulliNB() : 0.8318888424988252
分类器 MultinomialNB() : 0.6732324977461682
分类器 ComplementNB() : 0.896153405512828
f1 score
分类器 GaussianNB() : 0.7102795759957381
分类器 BernoulliNB() : 0.9058810976262339
分类器 MultinomialNB() : 0.8045286717829517
分类器 ComplementNB() : 0.9196015502193363
Running aboutSVMs ...
kernel: rbf, degree: 1, class weight type: balanced
Accuracy: 0.9835007173601148
Precision: 1.0
Recall: 0.8802083333333334
f1 score: 0.9362880886426592
Running searchForNu ...
poly
accuracy
nu is 0.001 : 0.9874318507890962
nu is 0.01 : 0.9875179340028695
nu is 0.03 : 0.987948350071736
nu is 0.04 : 0.9879770444763271
nu is 0.05 : 0.9877761836441893
nu is 0.1 : 0.9864849354375897
nu is 0.2 : 0.9760975609756096
precision
nu is 0.001 : 0.9828594734627173
nu is 0.01 : 0.9837510104227805
nu is 0.03 : 0.9846415843857023
nu is 0.04 : 0.9853105068719276
nu is 0.05 : 0.9852428471969114
nu is 0.1 : 0.9856836558867638
nu is 0.2 : 0.9906072176911974
recall
nu is 0.001 : 0.9248393485465267
nu is 0.01 : 0.9245996305889861
nu is 0.03 : 0.9268790617568469
nu is 0.04 : 0.9264282260154028
nu is 0.05 : 0.9249786883592696
nu is 0.1 : 0.914911715660134
nu is 0.2 : 0.8338738727005147
f1 score
nu is 0.001 : 0.952801036339649
nu is 0.01 : 0.9530791368796961
nu is 0.03 : 0.9547131754438144
nu is 0.04 : 0.9547897562537867
nu is 0.05 : 0.9539856921360426
nu is 0.1 : 0.948833457635658
nu is 0.2 : 0.9053032988020092
Running aboutGridSearchCV ...
Best score: 0.983
Best parameters set:
kernel: 'linear'
nu: 0.01
accuracy_score 0.9863701578192252
precision_score 0.9837837837837838
recall_score 0.9191919191919192
f1_score 0.9503916449086162
Running aboutRandomizedSearchCV ...
Best score: 0.981
Best parameters set:
kernel: 'linear'
nu: 0.001
accuracy_score 0.9835007173601148
precision_score 0.946236559139785
recall_score 0.9312169312169312
f1_score 0.9386666666666666
Running aboutAdaBoostAndBaggingClassifier ...
base classifier: LogisticRegression(class_weight='balanced')
Ensemble: AdaBoostClassifier(base_estimator=LogisticRegression(class_weight='balanced'))
Accuracy: 0.14275466284074606
Precision: 0.14275466284074606
Recall: 1.0
f1 score: 0.24984306340238543
Running aboutRandomForestClassifier ...
base classifier: RandomForeastClassifier
Accuracy: 0.975609756097561
Precision: 1.0
Recall: 0.8210526315789474
f1 score: 0.9017341040462429
0
Iteration 0, objective=1.91484
1
Iteration 1, objective=1.91484
[[ 2.31287961 1.51333813]
[-2.13455999 -2.15661017]]
[1.91484251 1.91484251]
0
Iteration 0, objective=0.404972
1
Iteration 1, objective=0.383269
2
Iteration 2, objective=0.377954
3
Iteration 3, objective=0.37449
4
Iteration 4, objective=0.371494
5
Iteration 5, objective=0.369823
6
Iteration 6, objective=0.367975
7
Iteration 7, objective=0.365521
8
Iteration 8, objective=0.365161
9
Iteration 9, objective=0.365026
10
Iteration 10, objective=0.364886
11
Iteration 11, objective=0.364776
12
Iteration 12, objective=0.364595
13
Iteration 13, objective=0.364532
14
Iteration 14, objective=0.364504
15
Iteration 15, objective=0.364401
16
Iteration 16, objective=0.364296
17
Iteration 17, objective=0.364194
18
Iteration 18, objective=0.364124
19
Iteration 19, objective=0.364124
=============
(2304000, 3)
LR
CM1 37 18 Mean auc = 0.52035
JM1 21 10 Mean auc = 0.53642
KC1 21 10 Mean auc = 0.57352
KC3 39 19 Mean auc = 0.57267
MC1 38 19 Mean auc = 0.50485
MC2 39 19 Mean auc = 0.60325
MW1 37 18 Mean auc = 0.63356
PC1 37 18 Mean auc = 0.54245
PC2 36 18 Mean auc = 0.49890
PC3 37 18 Mean auc = 0.56137
PC4 37 18 Mean auc = 0.65170
PC5 38 19 Mean auc = 0.57605
Mean of all test instance: 0.5645907720076324
DT
CM1 37 18 Mean auc = 0.52993
JM1 21 10 Mean auc = 0.57057
KC1 21 10 Mean auc = 0.58053
KC3 39 19 Mean auc = 0.58061
MC1 38 19 Mean auc = 0.67976
MC2 39 19 Mean auc = 0.59803
MW1 37 18 Mean auc = 0.61287
PC1 37 18 Mean auc = 0.63306
PC2 36 18 Mean auc = 0.53276
PC3 37 18 Mean auc = 0.61747
PC4 37 18 Mean auc = 0.70994
PC5 38 19 Mean auc = 0.64277
Mean of all test instance: 0.6073572789298225
KNN
CM1 37 18 Mean auc = 0.52481
JM1 21 10 Mean auc = 0.57757
KC1 21 10 Mean auc = 0.59236
KC3 39 19 Mean auc = 0.56336
MC1 38 19 Mean auc = 0.59247
MC2 39 19 Mean auc = 0.61379
MW1 37 18 Mean auc = 0.58745
PC1 37 18 Mean auc = 0.60176
PC2 36 18 Mean auc = 0.49982
PC3 37 18 Mean auc = 0.55596
PC4 37 18 Mean auc = 0.69512
PC5 38 19 Mean auc = 0.66113
Mean of all test instance: 0.5888006498404111
NB
CM1 37 18 Mean auc = 0.61323
JM1 21 10 Mean auc = 0.57666
KC1 21 10 Mean auc = 0.60875
KC3 39 19 Mean auc = 0.56816
MC1 38 19 Mean auc = 0.59915
MC2 39 19 Mean auc = 0.62938
MW1 37 18 Mean auc = 0.47831
PC1 37 18 Mean auc = 0.60860
PC2 36 18 Mean auc = nan
PC3 37 18 Mean auc = 0.68983
PC4 37 18 Mean auc = 0.60993
PC5 38 19 Mean auc = 0.58335
Mean of all test instance: nan
SVM
CM1 37 18 Mean auc = 0.58046
JM1 21 10 Mean auc = 0.47677
KC1 21 10 Mean auc = 0.48760
KC3 39 19 Mean auc = 0.55996
MC1 38 19 Mean auc = 0.62694
MC2 39 19 Mean auc = 0.52803
MW1 37 18 Mean auc = 0.54113
PC1 37 18 Mean auc = 0.58769
PC2 36 18 Mean auc = 0.58157
PC3 37 18 Mean auc = 0.60966
PC4 37 18 Mean auc = 0.70782
PC5 38 19 Mean auc = 0.51615
Mean of all test instance: 0.5669817704042611
RF
CM1 37 18 Mean auc = 0.50133
JM1 21 10 Mean auc = 0.57397
KC1 21 10 Mean auc = 0.60813
KC3 39 19 Mean auc = 0.55378
MC1 38 19 Mean auc = 0.58619
MC2 39 19 Mean auc = 0.59066
MW1 37 18 Mean auc = 0.58084
PC1 37 18 Mean auc = 0.60335
PC2 36 18 Mean auc = 0.49964
PC3 37 18 Mean auc = 0.56184
PC4 37 18 Mean auc = 0.67727
PC5 38 19 Mean auc = 0.67394
Mean of all test instance: 0.5842452240001026
[[0.5203466704269574, 0.5364246103102013, 0.5735210380764666, 0.5726677635823977, 0.5048491253932175, 0.603246268520868, 0.6335640410357306, 0.5424485754193521, 0.4989020886629799, 0.5613665853224091, 0.6517016365670376, 0.5760508607739719], [0.5299317516814793, 0.5705695011604459, 0.5805253135601877, 0.5806060466674104, 0.6797593036472512, 0.5980258484205853, 0.612869888808736, 0.6330579674574603, 0.5327625036316165, 0.6174685000801613, 0.7099392283065633, 0.6427714937359735], [0.5248066108383885, 0.5775743125697269, 0.592361085613034, 0.5633559909226388, 0.5924685235352224, 0.6137912163407968, 0.5874547992735777, 0.6017641541322453, 0.4998168498168498, 0.55595832803434, 0.6951225507391053, 0.6611333762690071], [0.6132285430944721, 0.5766568589188086, 0.608751203989945, 0.5681617315219513, 0.5991531460827118, 0.6293789573423443, 0.47831009704896715, 0.6085958278392779, nan, 0.6898298015290438, 0.6099349467408713, 0.5833547759298597], [0.580462772141544, 0.4767663875103259, 0.4876038306017236, 0.5599649359484989, 0.626937682662873, 0.5280290198576645, 0.5411252461499262, 0.5876912753150015, 0.5815676153640751, 0.609662276785327, 0.7078223896976596, 0.5161478128165148], [0.5013326363277611, 0.5739683046772792, 0.6081329234040501, 0.5537795365742206, 0.586187122885205, 0.5906646207723069, 0.580839190879661, 0.603350779874215, 0.49963569039656, 0.5618400306750669, 0.6772679507387458, 0.6739439007961587]]
Best classifer: NB
Best performance: nan
LR
CM1 37 18 Mean auc = 0.52064
JM1 21 10 Mean auc = 0.53593
KC1 21 10 Mean auc = 0.57111
KC3 39 19 Mean auc = 0.54548
MC1 38 19 Mean auc = 0.50433
MC2 39 19 Mean auc = 0.62434
MW1 37 18 Mean auc = 0.61215
PC1 37 18 Mean auc = 0.54853
PC2 36 18 Mean auc = 0.49872
PC3 37 18 Mean auc = 0.54701
PC4 37 18 Mean auc = 0.64957
PC5 38 19 Mean auc = 0.58295
Mean of all test instance: 0.561730978891374
DT
CM1 37 18 Mean auc = 0.56240
JM1 21 10 Mean auc = 0.56699
KC1 21 10 Mean auc = 0.58734
KC3 39 19 Mean auc = 0.58579
MC1 38 19 Mean auc = 0.66460
MC2 39 19 Mean auc = 0.56139
MW1 37 18 Mean auc = 0.61024
PC1 37 18 Mean auc = 0.64647
PC2 36 18 Mean auc = nan
PC3 37 18 Mean auc = 0.60644
PC4 37 18 Mean auc = 0.71490
PC5 38 19 Mean auc = 0.63548
Mean of all test instance: nan
KNN
CM1 37 18 Mean auc = 0.51030
JM1 21 10 Mean auc = 0.58516
KC1 21 10 Mean auc = 0.59014
KC3 39 19 Mean auc = 0.58080
MC1 38 19 Mean auc = 0.60810
MC2 39 19 Mean auc = 0.58441
MW1 37 18 Mean auc = 0.63891
PC1 37 18 Mean auc = 0.61044
PC2 36 18 Mean auc = 0.49945
PC3 37 18 Mean auc = 0.57043
PC4 37 18 Mean auc = 0.68247
PC5 38 19 Mean auc = 0.65765
Mean of all test instance: 0.593186902265104
NB
CM1 37 18 Mean auc = 0.58875
JM1 21 10 Mean auc = 0.58115
KC1 21 10 Mean auc = 0.59447
KC3 39 19 Mean auc = 0.58111
MC1 38 19 Mean auc = 0.58132
MC2 39 19 Mean auc = 0.65605
MW1 37 18 Mean auc = 0.49624
PC1 37 18 Mean auc = 0.61896
PC2 36 18 Mean auc = 0.67557
PC3 37 18 Mean auc = 0.68227
PC4 37 18 Mean auc = 0.60530
PC5 38 19 Mean auc = 0.59241
Mean of all test instance: 0.6044672924137108
SVM
CM1 37 18 Mean auc = 0.58351
JM1 21 10 Mean auc = 0.46937
KC1 21 10 Mean auc = 0.49916
KC3 39 19 Mean auc = 0.50484
MC1 38 19 Mean auc = 0.62554
MC2 39 19 Mean auc = 0.52438
MW1 37 18 Mean auc = 0.58967
PC1 37 18 Mean auc = 0.67191
PC2 36 18 Mean auc = 0.51428
PC3 37 18 Mean auc = 0.60100
PC4 37 18 Mean auc = 0.69409
PC5 38 19 Mean auc = 0.51969
Mean of all test instance: 0.5664533029640416
RF
CM1 37 18 Mean auc = 0.49912
JM1 21 10 Mean auc = 0.56572
KC1 21 10 Mean auc = 0.59549
KC3 39 19 Mean auc = 0.57203
MC1 38 19 Mean auc = 0.61372
MC2 39 19 Mean auc = 0.64027
MW1 37 18 Mean auc = 0.58272
PC1 37 18 Mean auc = 0.61097
PC2 36 18 Mean auc = 0.49982
PC3 37 18 Mean auc = 0.56206
PC4 37 18 Mean auc = 0.71037
PC5 38 19 Mean auc = 0.65358
Mean of all test instance: 0.5921549865315051
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Best classifer: DT
Best performance: nan
