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best_regex_frontier.txt
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best_regex_frontier.txt
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/om2/user/mnye/conda_envs/ec3/bin/python
[33m(B[mstarted:
bad for Data column no. 922
preg: WHS\d_\d+
preg sample: ['WHS8_52', 'WHS3_2', 'WHS5_8443']
exs ['WHS3_43', 'WHS7_134', 'MDG_0000000033', 'WHS7_139', 'WHS4_117']
bad for Data column no. 184
preg: \.\d+
preg sample: ['.1', '.1', '.859']
exs ['9.225009E-02', '.1675194', '.1039294', '.1376776', '.128372']
bad for Data column no. 467
preg: hu\d(\d|\u)+
preg sample: ['hu48C', 'hu324', 'hu666']
exs ['hu5CABA7', 'hu24A473', 'huFAA0CF', 'hu154164', 'hu7DE7FD']
bad for Data column no. 476
preg: \u+
preg sample: ['R', 'DTR', 'DX']
exs ['end', 'NN', 'PRP$', 'MD', 'WDT']
bad for Data column no. 150
preg: N\d\d
preg sample: ['N95', 'N82', 'N82']
exs ['N2', 'N42', 'N19', 'N27', 'N20']
bad for Data column no. 299
preg: E07000\d\d\d
preg sample: ['E07000386', 'E07000216', 'E07000732']
exs ['E06000005', 'E07000196', 'E06000021', 'E07000010', 'E07000219']
bad for Data column no. 334
preg: -00:\d\d:\d\d.\d
preg sample: ['-00:95:61,7', '-00:58:9342', '-00:39:65i4']
exs ['-00:17:28.6', '+00:03:04.8', '-00:22:06.4', '-00:10:05.6', '-00:36:31.5']
bad for Data column no. 493
preg: [0\.0\d+]
preg sample: ['[0.08]', '[0.0257]', '[0.04]']
exs ['[0.055]', '[0.1275]', '[]', '[0.01]', '[0.005]']
bad for Data column no. 891
preg: r|v\d?
preg sample: ['v0', 'r', 'r1']
exs ['g1', 'g3', 'i6', 'g5', 'x']
bad for Data column no. 792
preg: \d*\u*
preg sample: ['57U', '44', 'BE']
exs ['31', '34', '3', 'N_W04', '09X']
bad for Data column no. 765
preg: -?\d\.\d+( \(0\.\d+\))?
preg sample: ['-8.2 (0.9)', '-1.1', '8.3']
exs ['NA', '-0.186 (0.0215)', '1.11 ( 0)', '0.163 ( 0)', '-0.523 (0.0227)']
bad for Data column no. 944
preg: \u+\d?
preg sample: ['L', 'AQ9', 'U8']
exs ['RSW', 'SHWT', 'WCI', 'RTWT', 'D05G']
bad for Data column no. 374
preg: q000\d(_000\d)?
preg sample: ['q0004_0007', 'q0007', 'q0007_0007']
exs ['q0011_0002', 'q0010_0009', 'q0011_0001', 'q0007_0008', 'q0009_0014']
bad for Data column no. 660
preg: ENGL?\d\d\d
preg sample: ['ENG060', 'ENG475', 'ENGL780']
exs ['ENGL119', '281', 'ENGL442', 'ENGL461', 'GWS281']
bad for Data column no. 188
preg: cat\. \d\d
preg sample: ['cat. 55', 'cat. 36', 'cat. 06']
exs ['cat. 29', 'cat. 22', 'cat. 3', 'cat. 52', 'cat. 58']
bad for Data column no. 920
preg: A\.\d\d
preg sample: ['A.37', 'A.86', 'A.81']
exs ['A.36', 'A.10', 'A.101', 'A.74', 'A.72']
bad for Data column no. 330
preg: Resp\d\d
preg sample: ['Resp44', 'Resp80', 'Resp64']
exs ['Resp4', 'Resp43', 'Resp14', 'Resp47', 'Resp48']
bad for Data column no. 396
preg: \u+( |/\u+)?
preg sample: ['Y/DZH', 'HUOZYKY/IV', 'Y']
exs ['PPM', 'PH', 'LB/HR', 'PSIG', 'MOL %']
bad for Data column no. 680
preg: Z:-?0\.\d\d
preg sample: ['Z:0.72', 'Z:0.82', 'Z:0.04']
exs ['Z:1.13', 'Z:0.14', 'Z:0.95', 'Z:-0.09', 'Z:1.14']
bad for Data column no. 769
preg: (\u|\l|\d|-)+\d+
preg sample: ['1-U49', '-3', 'v5']
exs ['H91', 'C49A', 'NC230', 'B97', 'NC362']
bad for Data column no. 308
preg: \u\u\u\u
preg sample: ['JSGK', 'KFNT', 'PRSI']
exs ['L48H', 'CGCE', 'KGM7', 'KGL6', 'YBEX']
bad for Data column no. 375
preg: P\u\.\d\d\d\d\.\d\d\d
preg sample: ['PX.9217.061', 'PU.7044.183', 'PB.6223.395']
exs ['AC.0500.040', 'VE.0200.020', 'LY.0600.010', 'PC.2300.080', 'CP.1700.010']
bad for Data column no. 474
preg: \u+
preg sample: ['L', 'YP', 'J']
exs ['AMERSTD', 'L & S', 'POL SCI', 'ANTHRO', 'ESPM']
bad for Data column no. 79
preg: \d+(,\d\d\d)+
preg sample: ['0994,528,648,339,025', '5,965', '36732,152']
exs ['36,984', '-', '39,895', '124,168', '5,833,587']
bad for Data column no. 871
preg: -0.7\d+
preg sample: ['-0u728', '-007201', '-0i783']
exs ['.', '-0.7632', '-0.7286', '-0.7256', '-0.753']
bad for Data column no. 729
preg: COMISARIA \d\d
preg sample: ['COMISARIA 32', 'COMISARIA 80', 'COMISARIA 18']
exs ['COMISARIA 3', 'COMISARIA 35', 'COMISARIA 10', 'COMISARIA 20', 'COMISARIA 17']
bad for Data column no. 664
preg: N\d
preg sample: ['N2', 'N7', 'N8']
exs ['N4', 'N6', 'N9', 'N3', 'N10']
TESTING ONLY:
Data column no. 776
TRAIN ['JPCLN034.png', 'JPCLN115.png', 'JPCLN103.png', 'JPCLN049.png', 'JPCNN030.png']
TEST ['JPCLN060.png', 'JPCLN093.png', 'JPCNN031.png', 'JPCNN054.png', 'JPCNN066.png']
Human written regex: JPC\u\u\d+\.png
best Posterior:
(lambda ((lambda ((lambda (r_kleene (lambda (r_dot $0)) (r_kleene (lambda (r_const $0)) $0))) ((lambda (r_d (r_d $0))) $0))) ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) (string_g $0))))
(.)*(JPCLN)*\d\d.(.)*g
samples:
['JPCLN41nn3Ng', '01ng', 'JPCLNJPCLNJPCLNJPCLN01Pg', 'JPCLN11n.g', 'nJPCLNJPCLNJPCLN005pn9g']
MISS (train), Ground truth: -77.51694948352468, found ll: tensor([-94.1396], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -77.51694948352468, found ll: tensor([-153.9124], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda ((lambda (r_kleene (lambda (r_dot $0)) (r_kleene (lambda (r_const $0)) $0))) ((lambda (r_d (r_d $0))) $0))) ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) (string_p ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) $0)))))
(.)*(JPCLN)*\d\d.(.)*p.(.)*
samples:
['JPCLNJPCLN03.gp.', 'JPCLN035pgggJg9J', '33gp.n', 'JPCLNJPCLNJPCLNJPCLN11.gpn.', '10.pg']
MISS (train), Ground truth: -77.51694948352468, found ll: tensor([-95.1746], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -77.51694948352468, found ll: tensor([-156.8211], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['JPCLN060.png', 'JPCLN093.png', 'JPCNN031.png', 'JPCNN054.png', 'JPCNN066.png']
trainingExamples ['JPCLN034.png', 'JPCLN115.png', 'JPCLN103.png', 'JPCLN049.png', 'JPCNN030.png']
Data column no. 922
TRAIN ['WHS4_107', 'WHS7_103', 'WHS7_105', 'WHS6_116', 'WHS9_85']
TEST ['WHS3_43', 'WHS7_134', 'MDG_0000000033', 'WHS7_139', 'WHS4_117']
Human written regex: WHS\d_\d+
best Posterior:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (string_underscore (r_kleene (lambda (r_d $0)) ((lambda (r_d (r_d $0))) $0)))))
WHS\d(\d)*_(\d)*\d\d
samples:
['WHS80_757', 'WHS83_01', 'WHS6_036', 'WHS01_14', 'WHS7_06']
HIT (train), Ground truth: -53.4531772947261, found ll: tensor([-46.9445], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (string_underscore (r_d (r_kleene (lambda (r_d $0)) (r_d $0))))))
WHS\d(\d)*_\d(\d)*\d
samples:
['WHS9_36', 'WHS11_55', 'WHS11_10', 'WHS3_11', 'WHS17_77']
HIT (train), Ground truth: -53.4531772947261, found ll: tensor([-46.9445], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['WHS3_43', 'WHS7_134', 'MDG_0000000033', 'WHS7_139', 'WHS4_117']
trainingExamples ['WHS4_107', 'WHS7_103', 'WHS7_105', 'WHS6_116', 'WHS9_85']
Data column no. 354
TRAIN ['WSW', 'NNW', 'W', 'NNE', 'E']
TEST ['ESE', 'SSE', 'S', 'SSW', 'NE']
Human written regex: \u+
best Posterior:
(lambda (r_u (r_kleene (lambda (r_u $0)) $0)))
\u(\u)*
samples:
['WWW', 'N', 'NE', 'N', 'N']
HIT (train), Ground truth: -43.46368090439571, found ll: tensor([-21.6073], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -47.41492462297713, found ll: tensor([-31.5205], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_u (r_kleene (lambda (r_u (string_W $0))) (r_kleene (lambda (r_u $0)) $0))))
\u(\uW)*(\u)*
samples:
['NNW', 'S', 'NNWN', 'WEE', 'NEW']
HIT (train), Ground truth: -43.46368090439571, found ll: tensor([-19.9386], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -47.41492462297713, found ll: tensor([-30.9475], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['ESE', 'SSE', 'S', 'SSW', 'NE']
trainingExamples ['WSW', 'NNW', 'W', 'NNE', 'E']
Data column no. 523
TRAIN ['CONJ', 'ADP', 'VERB', 'ADV', '.']
TEST ['DET', 'ADJ', 'NUM', 'PRON', 'NOUN']
Human written regex: (\u)+|\.
best Posterior:
(lambda (r_alt (lambda (string_period $0)) (lambda (r_kleene (lambda (r_u $0)) ((lambda ((lambda (r_u (r_u $0))) (r_u $0))) $0))) $0))
(\.)|((\u)*\u\u\u)
samples:
['AVD', 'PDA', 'RDDM', '.', '.']
HIT (train), Ground truth: -58.78314796293972, found ll: tensor([-40.1594], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -70.63687911868399, found ll: tensor([-71.1741], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_alt (lambda (string_period $0)) (lambda (r_kleene (lambda (r_u $0)) ((lambda ((lambda (r_u (r_u $0))) (r_u $0))) $0))) $0))
(\.)|((\u)*\u\u\u)
samples:
['.', '.', '.', 'DRAR', '.']
HIT (train), Ground truth: -58.78314796293972, found ll: tensor([-40.1594], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -70.63687911868399, found ll: tensor([-71.1741], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['DET', 'ADJ', 'NUM', 'PRON', 'NOUN']
trainingExamples ['CONJ', 'ADP', 'VERB', 'ADV', '.']
Data column no. 184
TRAIN ['.1301411', '.2883103', '.1275464', '.1376095', '.2999201']
TEST ['9.225009E-02', '.1675194', '.1039294', '.1376776', '.128372']
Human written regex: \.\d+
best Posterior:
(lambda ((lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) ((lambda (r_d (r_d $0))) $0))) ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0)))
\.\d(\d)*\d\d\d\d\d\d
samples:
['.1171198', '.1315601', '.3602518', '.1011372', '.0634181']
HIT (train), Ground truth: -104.85062957438967, found ll: tensor([-76.8591], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))) (r_d ((lambda (r_d (r_d $0))) $0))))
\.\d\d\d\d\d\d\d
samples:
['.0622394', '.1301012', '.1243010', '.9863742', '.1100797']
HIT (train), Ground truth: -104.85062957438967, found ll: -76.77993774414062
HIT (test), Ground truth: -inf, found ll: -inf
Posterior predictive samples...
testingExamples ['9.225009E-02', '.1675194', '.1039294', '.1376776', '.128372']
trainingExamples ['.1301411', '.2883103', '.1275464', '.1376095', '.2999201']
Data column no. 501
TRAIN ['u43', 'u10', 'u16', 'u58', 'u02']
TEST ['u46', 'u22', 'u42', 'u30', 'u36']
Human written regex: u\d\d
best Posterior:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) $0))
u\d(\d)*
samples:
['u58', 'u4', 'u0', 'u1', 'u14']
MISS (train), Ground truth: -23.025850929940454, found ll: tensor([-27.2043], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -23.025850929940454, found ll: tensor([-29.2711], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) $0))
u\d\d
samples:
['u20', 'u36', 'u31', 'u88', 'u21']
HIT (train), Ground truth: -23.025850929940454, found ll: -20.272804260253906
HIT (test), Ground truth: -23.025850929940454, found ll: -22.339649200439453
Posterior predictive samples...
testingExamples ['u46', 'u22', 'u42', 'u30', 'u36']
trainingExamples ['u43', 'u10', 'u16', 'u58', 'u02']
Data column no. 760
TRAIN ['PM', 'PF', 'RF', 'DM', 'GS']
TEST ['PE', 'PB', 'DO', 'SO', 'CT']
Human written regex: \u\u
best Posterior:
(lambda ((lambda (r_u (r_u $0))) $0))
\u\u
samples:
['MD', 'MM', 'DP', 'PF', 'SD']
HIT (train), Ground truth: -32.58096538021482, found ll: -18.953296661376953
MISS (test), Ground truth: -32.58096538021482, found ll: -54.06806564331055
best Likelihood:
(lambda (r_alt (lambda (string_P (r_u $0))) (lambda ((lambda (r_u (r_u $0))) $0)) $0))
(P\u)|(\u\u)
samples:
['DD', 'PR', 'PR', 'GS', 'FD']
HIT (train), Ground truth: -32.58096538021482, found ll: -17.404130935668945
MISS (test), Ground truth: -32.58096538021482, found ll: -53.62693405151367
Posterior predictive samples...
testingExamples ['PE', 'PB', 'DO', 'SO', 'CT']
trainingExamples ['PM', 'PF', 'RF', 'DM', 'GS']
Data column no. 49
TRAIN ['SB1', 'OND', 'NH', 'CH', 'PSC']
TEST ['NGH', 'USB', 'NASCC', 'EB', 'WHP']
Human written regex: (\u)+\u\d?
best Posterior:
(lambda ((lambda (r_kleene (lambda (r_u $0)) ((lambda (r_u (r_kleene (lambda (r_d $0)) $0))) $0))) $0))
(\u)*\u(\d)*
samples:
['HPS1', 'P', 'UD', 'P1', 'BN']
HIT (train), Ground truth: -49.717509715971175, found ll: tensor([-36.7465], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -63.21989949730284, found ll: tensor([-75.3505], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_alt (lambda ((lambda (r_u (r_u $0))) (r_kleene (lambda (r_d $0)) $0))) (lambda ((lambda ((lambda (r_u (r_u $0))) (r_u $0))) $0)) $0))
(\u\u(\d)*)|(\u\u\u)
samples:
['BBN', 'BCC', 'CN', 'SC11', 'CSB']
HIT (train), Ground truth: -49.717509715971175, found ll: tensor([-30.0962], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -63.21989949730284, found ll: tensor([-inf], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['NGH', 'USB', 'NASCC', 'EB', 'WHP']
trainingExamples ['SB1', 'OND', 'NH', 'CH', 'PSC']
Data column no. 732
TRAIN ['SR504', 'HR530', 'HR512', 'HR559', 'HR514']
TEST ['HR526', 'HR544', 'HR520', 'HR562', 'HR506']
Human written regex: \uR5\d\d
best Posterior:
(lambda (r_u ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) $0)))
\uR5\d\d
samples:
['HR513', 'HR500', 'HR512', 'HR544', 'HR540']
HIT (train), Ground truth: -39.31633362004787, found ll: -21.47345542907715
HIT (test), Ground truth: -39.31633362004787, found ll: -35.396263122558594
best Likelihood:
(lambda (r_alt (lambda ((lambda (r_u (r_kleene (lambda (r_d $0)) $0))) $0)) (lambda (string_H $0)) ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) $0)))
(\u(\d)*)|(H)R5\d\d
samples:
['HR525', 'HR525', 'HR545', 'SR542', 'HR502']
HIT (train), Ground truth: -39.31633362004787, found ll: tensor([-22.3939], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -39.31633362004787, found ll: -37.668216705322266
Posterior predictive samples...
testingExamples ['HR526', 'HR544', 'HR520', 'HR562', 'HR506']
trainingExamples ['SR504', 'HR530', 'HR512', 'HR559', 'HR514']
Data column no. 450
TRAIN ['-4.26', '-1.69', '-3.91', '-1.622', '-1']
TEST ['-2.316', '-3.19', '-0.933', '-1.568', '-0.96']
Human written regex: -\d(\.(\d)+)?
best Posterior:
(lambda ((lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (r_kleene (lambda ((lambda (r_dot (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))) (r_kleene (lambda ((lambda (r_kleene (lambda (r_d $0)) (string_period ((lambda (r_d (r_d $0))) $0)))) $0)) $0)))
-\d(\d)*(.\d\d\d)*((\d)*\.\d\d)*
samples:
['-16', '-6.021', '-13.943.211', '-6', '-1.966']
HIT (train), Ground truth: -41.94025182975587, found ll: tensor([-33.9398], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -53.923180923971834, found ll: tensor([-65.4838], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (string_dash (r_kleene (lambda ((lambda (r_kleene (lambda (r_d $0)) (string_period ((lambda (r_d (r_d $0))) $0)))) $0)) (r_kleene (lambda (r_d $0)) $0))))
-((\d)*\.\d\d)*(\d)*
samples:
['-', '-', '-2.99', '-.2691.69.2211.4493', '-2']
HIT (train), Ground truth: -41.94025182975587, found ll: tensor([-39.7882], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -53.923180923971834, found ll: tensor([-72.9542], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['-2.316', '-3.19', '-0.933', '-1.568', '-0.96']
trainingExamples ['-4.26', '-1.69', '-3.91', '-1.622', '-1']
Data column no. 350
TRAIN ['MN', 'SD', 'WY', 'NE', 'MO']
TEST ['MS', 'TX', 'KY', 'NM', 'DC']
Human written regex: \u\u
best Posterior:
(lambda ((lambda (r_u (r_u $0))) $0))
\u\u
samples:
['OS', 'OO', 'NM', 'OM', 'YN']
HIT (train), Ground truth: -32.58096538021482, found ll: -20.332683563232422
MISS (test), Ground truth: -32.58096538021482, found ll: -42.708518981933594
best Likelihood:
(lambda ((lambda (r_u (r_u $0))) $0))
\u\u
samples:
['SM', 'MS', 'DS', 'WO', 'SW']
HIT (train), Ground truth: -32.58096538021482, found ll: -20.332683563232422
MISS (test), Ground truth: -32.58096538021482, found ll: -42.708518981933594
Posterior predictive samples...
testingExamples ['MS', 'TX', 'KY', 'NM', 'DC']
trainingExamples ['MN', 'SD', 'WY', 'NE', 'MO']
Data column no. 467
TRAIN ['hu48AC54', 'hu264A0A', 'hu294056', 'hu26B551', 'hu3CDB6A']
TEST ['hu5CABA7', 'hu24A473', 'huFAA0CF', 'hu154164', 'hu7DE7FD']
Human written regex: hu\d(\d|\u)+
best Posterior:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (r_kleene (lambda (r_u $0)) (r_kleene (lambda (r_d $0)) (r_kleene (lambda (r_u $0)) $0)))))
hu\d(\d)*(\u)*(\d)*(\u)*
samples:
['hu54', 'hu26', 'hu445ACBB', 'hu9BB3', 'hu40CDA']
HIT (train), Ground truth: -112.33451482306555, found ll: tensor([-85.1324], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (r_kleene (lambda (r_u $0)) (r_kleene (lambda (r_d $0)) (r_d (r_kleene (lambda (r_u $0)) $0))))))
hu\d(\d)*(\u)*(\d)*\d(\u)*
samples:
['hu62AB64C', 'hu275', 'hu5243', 'hu9B664', 'hu52259ABCA']
HIT (train), Ground truth: -112.33451482306555, found ll: tensor([-81.9450], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['hu5CABA7', 'hu24A473', 'huFAA0CF', 'hu154164', 'hu7DE7FD']
trainingExamples ['hu48AC54', 'hu264A0A', 'hu294056', 'hu26B551', 'hu3CDB6A']
Data column no. 622
TRAIN ['AJDJ6SQUDORP6', 'A35C4777EXZZQH', 'A2C9H5QRLXXICT', 'A1JVVYYO7G56DS', 'A382RYVVZOJ8PX']
TEST ['A3GTTANAYUVSDY', 'AZIN9ATSFV0ZG', 'A2YO4SCWAWNYBI', 'A2Q7SZISE1RXQQ', 'AARIXIQYNH38W']
Human written regex: A(\d|\u)**
best Posterior:
(lambda (r_kleene (lambda ((lambda (r_kleene (lambda (r_u $0)) ((lambda (r_u (r_kleene (lambda (r_d $0)) $0))) $0))) $0)) $0))
((\u)*\u(\d)*)*
samples:
['VG5OP477227DH2', 'ZCIJ757977', '', '', 'PRQAV5J']
HIT (train), Ground truth: -289.4835077979257, found ll: tensor([-252.0928], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -293.43871990403125, found ll: tensor([-307.4614], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) $0))) ((lambda (r_kleene (lambda (r_d $0)) (r_kleene (lambda (r_u $0)) $0))) ((lambda ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) ((lambda ((lambda (r_u (r_u $0))) (r_u $0))) ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) $0)))) $0))))
A.(.)*(\d)*(\u)*.(.)*\u\u\u.(.)*
samples:
['AD6CVQOOJH', 'A7782755EZQ9LPR263k5H', 'AV87EVG1ZYO2J1', 'ASJDJCKQY9VX', 'ADJYRYJ53H']
HIT (train), Ground truth: -289.4835077979257, found ll: tensor([-202.3591], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -293.43871990403125, found ll: tensor([-350.7862], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['A3GTTANAYUVSDY', 'AZIN9ATSFV0ZG', 'A2YO4SCWAWNYBI', 'A2Q7SZISE1RXQQ', 'AARIXIQYNH38W']
trainingExamples ['AJDJ6SQUDORP6', 'A35C4777EXZZQH', 'A2C9H5QRLXXICT', 'A1JVVYYO7G56DS', 'A382RYVVZOJ8PX']
Data column no. 476
TRAIN ['JJ', 'RBR', 'EX', 'NNS', 'VBN']
TEST ['end', 'NN', 'PRP$', 'MD', 'WDT']
Human written regex: \u+
best Posterior:
(lambda (r_kleene (lambda (r_u $0)) $0))
(\u)*
samples:
['JBVJR', 'RNRS', 'VNJXNN', 'F', 'ES']
HIT (train), Ground truth: -51.36616834155856, found ll: tensor([-36.6306], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_alt (lambda (r_u (r_u $0))) (lambda ((lambda ((lambda (r_u (r_u $0))) (r_u $0))) $0)) $0))
(\u\u)|(\u\u\u)
samples:
['BX', 'JN', 'SR', 'XSS', 'XBR']
HIT (train), Ground truth: -51.36616834155856, found ll: -29.461196899414062
HIT (test), Ground truth: -inf, found ll: -inf
Posterior predictive samples...
testingExamples ['end', 'NN', 'PRP$', 'MD', 'WDT']
trainingExamples ['JJ', 'RBR', 'EX', 'NNS', 'VBN']
Data column no. 554
TRAIN ['CA', 'VA', 'FL', 'CO', 'KY']
TEST ['MD', 'MI', 'CT', 'OH', 'TX']
Human written regex: \u\u
best Posterior:
(lambda ((lambda (r_u (r_u $0))) $0))
\u\u
samples:
['CA', 'LL', 'AL', 'AL', 'AA']
HIT (train), Ground truth: -32.58096538021482, found ll: -20.332683563232422
MISS (test), Ground truth: -32.58096538021482, found ll: -65.77201843261719
best Likelihood:
(lambda (r_u (r_u $0)))
\u\u
samples:
['OV', 'YA', 'AA', 'AA', 'YO']
HIT (train), Ground truth: -32.58096538021482, found ll: -20.332683563232422
MISS (test), Ground truth: -32.58096538021482, found ll: -65.77201843261719
Posterior predictive samples...
testingExamples ['MD', 'MI', 'CT', 'OH', 'TX']
trainingExamples ['CA', 'VA', 'FL', 'CO', 'KY']
Data column no. 940
TRAIN ['IH', 'AP', 'RS', 'F', 'MX']
TEST ['XR', 'UP', 'NM', 'PZ', 'QO']
Human written regex: \u\u?
best Posterior:
(lambda (r_kleene (lambda (r_u $0)) $0))
(\u)*
samples:
['', '', 'A', 'PI', 'AAHA']
HIT (train), Ground truth: -32.788604744993066, found ll: tensor([-28.9608], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -36.04670128301455, found ll: tensor([-59.7291], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_alt (lambda ((lambda (r_u (r_u $0))) $0)) (lambda (string_F $0)) $0))
(\u\u)|(F)
samples:
['XI', 'F', 'F', 'F', 'MA']
HIT (train), Ground truth: -32.788604744993066, found ll: -20.1606388092041
MISS (test), Ground truth: -36.04670128301455, found ll: -52.887474060058594
Posterior predictive samples...
testingExamples ['XR', 'UP', 'NM', 'PZ', 'QO']
trainingExamples ['IH', 'AP', 'RS', 'F', 'MX']
Data column no. 496
TRAIN ['GA', 'PA', 'LA', 'IL', 'VA']
TEST ['WI', 'FL', 'CA', 'NC', 'OH']
Human written regex: \u\u
best Posterior:
(lambda ((lambda (r_u (r_u $0))) $0))
\u\u
samples:
['LA', 'AA', 'LV', 'PA', 'AL']
HIT (train), Ground truth: -32.58096538021482, found ll: -16.197996139526367
MISS (test), Ground truth: -32.58096538021482, found ll: -57.870086669921875
best Likelihood:
(lambda (r_kleene (lambda (r_u $0)) (r_alt (lambda (string_A $0)) (lambda $0) $0)))
(\u)*(A)|
samples:
['ILLV', '', 'A', 'VGA', 'A']
HIT (train), Ground truth: -32.58096538021482, found ll: tensor([-20.4707], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -32.58096538021482, found ll: tensor([-69.1849], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['WI', 'FL', 'CA', 'NC', 'OH']
trainingExamples ['GA', 'PA', 'LA', 'IL', 'VA']
Data column no. 369
TRAIN ['PHI', 'USA', 'SCO', 'RUS', 'SVK']
TEST ['NZL', 'GER', 'CHN', 'NIR', 'ENG']
Human written regex: \u\u\u
best Posterior:
(lambda ((lambda ((lambda (r_u (r_u $0))) (r_u $0))) $0))
\u\u\u
samples:
['ISS', 'DUS', 'VRV', 'VKC', 'ASS']
HIT (train), Ground truth: -48.87144807032223, found ll: -33.79325485229492
MISS (test), Ground truth: -48.87144807032223, found ll: -90.40133666992188
best Likelihood:
(lambda (r_alt (lambda (string_S ((lambda (r_u (r_u $0))) $0))) (lambda ((lambda ((lambda (r_u (r_u $0))) (r_u $0))) $0)) $0))
(S\u\u)|(\u\u\u)
samples:
['VCS', 'SOS', 'IVU', 'SAR', 'OUP']
HIT (train), Ground truth: -48.87144807032223, found ll: -34.11725997924805
MISS (test), Ground truth: -48.87144807032223, found ll: -94.3702392578125
Posterior predictive samples...
testingExamples ['NZL', 'GER', 'CHN', 'NIR', 'ENG']
trainingExamples ['PHI', 'USA', 'SCO', 'RUS', 'SVK']
Data column no. 596
TRAIN ['NP', 'AO', 'CP', 'SPP', 'FP']
TEST ['TR', 'CS', 'BD', 'MAR', 'FIS']
Human written regex: \u+
best Posterior:
(lambda (r_u (r_u (r_kleene (lambda (r_u $0)) $0))))
\u\u(\u)*
samples:
['PC', 'PS', 'FF', 'PP', 'PP']
HIT (train), Ground truth: -43.4636809043957, found ll: tensor([-21.1454], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -47.41492462297713, found ll: tensor([-69.2693], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_u (r_kleene (lambda (r_u $0)) (r_kleene (lambda (string_P $0)) $0))))
\u(\u)*(P)*
samples:
['SF', 'NFPPP', 'OFOSAPPP', 'C', 'F']
HIT (train), Ground truth: -43.4636809043957, found ll: tensor([-21.3889], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -47.41492462297713, found ll: tensor([-74.6392], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['TR', 'CS', 'BD', 'MAR', 'FIS']
trainingExamples ['NP', 'AO', 'CP', 'SPP', 'FP']
Data column no. 720
TRAIN ['(715) 967-2697', '(608) 819-2220', '(920) 988-2524', '(608) 442-0253', '(262) 723-4043']
TEST ['(920) 887-4282', '(262) 930-5176', '(920) 743-7943', '(715) 485-8764', '(608) 873-5217']
Human written regex: \(\d\d\d\) \d\d\d-\d\d\d\d
best Posterior:
(lambda (string_right_paren (r_kleene (lambda (r_d $0)) ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) (string_dash ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))))))
\((\d)*\) \d\d\d-\d\d\d\d
samples:
['(490) 720-2534', '() 713-0263', '(4) 228-6880', '(4) 024-0692', '() 210-5740']
MISS (train), Ground truth: -115.12925464970229, found ll: tensor([-120.5666], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -115.12925464970229, found ll: tensor([-128.1440], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (string_right_paren (r_kleene (lambda (r_d $0)) ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) (string_dash ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))))))
\((\d)*\) \d\d\d-\d\d\d\d
samples:
['() 081-6927', '() 882-6256', '(8227) 227-2922', '(4) 069-9953', '(82) 829-2962']
MISS (train), Ground truth: -115.12925464970229, found ll: tensor([-120.5666], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -115.12925464970229, found ll: tensor([-128.1440], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['(920) 887-4282', '(262) 930-5176', '(920) 743-7943', '(715) 485-8764', '(608) 873-5217']
trainingExamples ['(715) 967-2697', '(608) 819-2220', '(920) 988-2524', '(608) 442-0253', '(262) 723-4043']
Data column no. 53
TRAIN ['rec-93-org', 'rec-51-org', 'rec-321-org', 'rec-281-dup-0', 'rec-227-org']
TEST ['rec-372-org', 'rec-39-org', 'rec-13-org', 'rec-355-org', 'rec-198-org']
Human written regex: rec-\d\d\d?-(org)|(dup-0)
best Posterior:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (string_dash ((lambda (r_l (r_l $0))) ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) $0)))))
rec-\d(\d)*-\l\l.(.)*
samples:
['rec-12-org-', 'rec-288-rr--', 'rec-2328-rr-', 'rec-88-rr0g', 'rec-9-rogg']
MISS (train), Ground truth: -36.86507801452205, found ll: tensor([-57.2913], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -36.86507801452204, found ll: tensor([-51.7268], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (string_dash ((lambda (r_l (r_l $0))) ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) $0)))))
rec-\d(\d)*-\l\l.(.)*
samples:
['rec-318-do)g', 'rec-1331-oug', 'rec-2-or0-g-', 'rec-311-ropgg', 'rec-885-oogg-M0']
MISS (train), Ground truth: -36.86507801452205, found ll: tensor([-57.2913], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -36.86507801452204, found ll: tensor([-51.7268], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['rec-372-org', 'rec-39-org', 'rec-13-org', 'rec-355-org', 'rec-198-org']
trainingExamples ['rec-93-org', 'rec-51-org', 'rec-321-org', 'rec-281-dup-0', 'rec-227-org']
Data column no. 150
TRAIN ['N22', 'N33', 'N34', 'N13', 'N23']
TEST ['N2', 'N42', 'N19', 'N27', 'N20']
Human written regex: N\d\d
best Posterior:
(lambda (r_const ((lambda (r_d (r_d $0))) $0)))
N\d\d
samples:
['N31', 'N34', 'N33', 'N33', 'N32']
HIT (train), Ground truth: -23.025850929940454, found ll: -11.753284454345703
HIT (test), Ground truth: -inf, found ll: -inf
best Likelihood:
(lambda ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) $0))
N\d\d
samples:
['N24', 'N21', 'N31', 'N22', 'N92']
HIT (train), Ground truth: -23.025850929940454, found ll: -11.753284454345703
HIT (test), Ground truth: -inf, found ll: -inf
Posterior predictive samples...
testingExamples ['N2', 'N42', 'N19', 'N27', 'N20']
trainingExamples ['N22', 'N33', 'N34', 'N13', 'N23']
Data column no. 741
TRAIN ['#217', '#222', '#070', '#007', '#215']
TEST ['#054', '#221', '#062', '#065', '#204']
Human written regex: #\d\d\d
best Posterior:
(lambda ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) $0))
#\d\d\d
samples:
['#022', '#502', '#522', '#872', '#207']
HIT (train), Ground truth: -34.538776394910684, found ll: -22.43019676208496
MISS (test), Ground truth: -34.538776394910684, found ll: -44.37188720703125
best Likelihood:
(lambda (string_hash ((lambda (r_d (r_d $0))) (r_d $0))))
#\d\d\d
samples:
['#710', '#275', '#717', '#020', '#021']
HIT (train), Ground truth: -34.538776394910684, found ll: -22.43019676208496
MISS (test), Ground truth: -34.538776394910684, found ll: -44.37188720703125
Posterior predictive samples...
testingExamples ['#054', '#221', '#062', '#065', '#204']
trainingExamples ['#217', '#222', '#070', '#007', '#215']
Data column no. 18
TRAIN ['A-05334-58432', 'A-05333-58214', 'C-05271-37204', 'C-05276-38389', 'A-05334-58579']
TEST ['A-05276-38355', 'A-05334-58331', 'C-05276-38616', 'C-05334-58599', 'A-05323-55341']
Human written regex: A|C-\d+-\d+
best Posterior:
(lambda (r_u ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) (string_dash ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) ((lambda (r_d (r_d $0))) $0))))))
\u-05\d\d\d-\d\d\d\d\d
samples:
['A-05882-18783', 'C-05933-33521', 'A-05843-52352', 'A-05343-42587', 'A-05172-39486']
HIT (train), Ground truth: -153.25234958049924, found ll: -86.60868835449219
HIT (test), Ground truth: -153.25234958049924, found ll: -90.1746826171875
best Likelihood:
(lambda (r_u ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) (string_dash ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) ((lambda (r_d (r_d $0))) $0))))))
\u-05\d\d\d-\d\d\d\d\d
samples:
['A-05235-83933', 'C-05737-33778', 'A-05394-32935', 'A-05253-91324', 'A-05272-35233']
HIT (train), Ground truth: -153.25234958049924, found ll: -86.60868835449219
HIT (test), Ground truth: -153.25234958049924, found ll: -90.1746826171875
Posterior predictive samples...
testingExamples ['A-05276-38355', 'A-05334-58331', 'C-05276-38616', 'C-05334-58599', 'A-05323-55341']
trainingExamples ['A-05334-58432', 'A-05333-58214', 'C-05271-37204', 'C-05276-38389', 'A-05334-58579']
Data column no. 589
TRAIN ['A1XB03X4J35ATE', 'AXPOBOA6RMCV0', 'A1K8QNLYYYX21W', 'A2QKM3JUFWBSMO', 'A1T0NW527WZT7G']
TEST ['A24LC97AU3QC7G', 'A1MUEKEQQVROE7', 'A3LC6M2EMDBBXP', 'A1K9QGO39NG7DV', 'A100Y89FZO4J0B']
Human written regex: A(\u|\d)++
best Posterior:
(lambda (r_kleene (lambda ((lambda (r_kleene (lambda (r_u $0)) ((lambda (r_u (r_kleene (lambda (r_d $0)) $0))) $0))) $0)) $0))
((\u)*\u(\d)*)*
samples:
['', 'BAX', 'FV1172', 'YW', 'M']
HIT (train), Ground truth: -281.5965245472988, found ll: tensor([-260.4001], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -283.3743811113579, found ll: tensor([-305.7537], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_kleene (lambda ((lambda (r_kleene (lambda (r_u $0)) ((lambda (r_u (r_kleene (lambda (r_d $0)) $0))) $0))) $0)) $0))
((\u)*\u(\d)*)*
samples:
['AS0', '', 'KWBQRCJA11MGQ825KW', 'M1802207XXBASJXAEVARUB7', 'JTT0YJZ301']
HIT (train), Ground truth: -281.5965245472988, found ll: tensor([-260.4001], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -283.3743811113579, found ll: tensor([-305.7537], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['A24LC97AU3QC7G', 'A1MUEKEQQVROE7', 'A3LC6M2EMDBBXP', 'A1K9QGO39NG7DV', 'A100Y89FZO4J0B']
trainingExamples ['A1XB03X4J35ATE', 'AXPOBOA6RMCV0', 'A1K8QNLYYYX21W', 'A2QKM3JUFWBSMO', 'A1T0NW527WZT7G']
Data column no. 666
TRAIN ['(281) 460-9574', '(801) 473-6431', '(713) 478-4292', '(713) 591-1559', '(281) 460-2390']
TEST ['(314) 800-3559', '(956) 778-2268', '(662) 687-1620', '(281) 382-0084', '(757) 784-5526']
Human written regex: \(\d\d\d\) \d\d\d-\d\d\d\d
best Posterior:
(lambda (string_right_paren (r_kleene (lambda (r_d $0)) ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) (string_dash ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))))))
\((\d)*\) \d\d\d-\d\d\d\d
samples:
['(921770) 111-8023', '(2314) 481-7742', '(280214797) 957-0335', '() 780-3919', '() 109-0232']
MISS (train), Ground truth: -115.12925464970229, found ll: tensor([-124.4709], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -115.12925464970229, found ll: tensor([-131.7671], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (string_right_paren (r_kleene (lambda (r_d $0)) ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) (string_dash ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))))))
\((\d)*\) \d\d\d-\d\d\d\d
samples:
['() 951-8214', '(17) 351-4516', '(92) 417-9093', '(9) 405-3324', '() 594-9807']
MISS (train), Ground truth: -115.12925464970229, found ll: tensor([-124.4709], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -115.12925464970229, found ll: tensor([-131.7671], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['(314) 800-3559', '(956) 778-2268', '(662) 687-1620', '(281) 382-0084', '(757) 784-5526']
trainingExamples ['(281) 460-9574', '(801) 473-6431', '(713) 478-4292', '(713) 591-1559', '(281) 460-2390']
Data column no. 581
TRAIN ['us13C03', 'us13C13', 'us13C11', 'us13A08', 'us13B06']
TEST ['us13B05', 'us13C04', 'us13A10', 'us13B10', 'us13C01']
Human written regex: us13\u\d\d
best Posterior:
(lambda (r_const (r_u ((lambda (r_d (r_d $0))) $0))))
us13\u\d\d
samples:
['us13B41', 'us13B83', 'us13C01', 'us13B01', 'us13C10']
HIT (train), Ground truth: -39.31633362004787, found ll: -19.918222427368164
HIT (test), Ground truth: -39.31633362004787, found ll: -29.33217430114746
best Likelihood:
(lambda (r_const (r_u ((lambda (r_d (r_d $0))) $0))))
us13\u\d\d
samples:
['us13C08', 'us13C11', 'us13C11', 'us13B00', 'us13C31']
HIT (train), Ground truth: -39.31633362004787, found ll: -19.918222427368164
HIT (test), Ground truth: -39.31633362004787, found ll: -29.33217430114746
Posterior predictive samples...
testingExamples ['us13B05', 'us13C04', 'us13A10', 'us13B10', 'us13C01']
trainingExamples ['us13C03', 'us13C13', 'us13C11', 'us13A08', 'us13B06']
Data column no. 299
TRAIN ['E07000193', 'E07000170', 'E07000127', 'E07000027', 'E07000225']
TEST ['E06000005', 'E07000196', 'E06000021', 'E07000010', 'E07000219']
Human written regex: E07000\d\d\d
best Posterior:
(lambda ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) $0))
E07000\d\d\d
samples:
['E07000119', 'E07000712', 'E07000317', 'E07000132', 'E07000122']
HIT (train), Ground truth: -34.538776394910684, found ll: -27.155675888061523
HIT (test), Ground truth: -inf, found ll: -inf
best Likelihood:
(lambda (r_const (r_d (r_d (r_d $0)))))
E07000\d\d\d
samples:
['E07000710', 'E07000372', 'E07000233', 'E07000232', 'E07000092']
HIT (train), Ground truth: -34.538776394910684, found ll: -27.155675888061523
HIT (test), Ground truth: -inf, found ll: -inf
Posterior predictive samples...
testingExamples ['E06000005', 'E07000196', 'E06000021', 'E07000010', 'E07000219']
trainingExamples ['E07000193', 'E07000170', 'E07000127', 'E07000027', 'E07000225']
Data column no. 638
TRAIN ['kt831qh3476', 'vw545jd0450', 'st391cf4769', 'vx806dr5738', 'rp023xr5328']
TEST ['vx793tc4890', 'qy821tc8518', 'zw345zp0788', 'bb896gq6656', 'qc288yw9773']
Human written regex: \l+\d+\l+\d+
best Posterior:
(lambda ((lambda (r_l (r_l $0))) ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) ((lambda (r_l (r_l $0))) ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0)))))
\l\l\d\d\d\l\l\d\d\d\d
samples:
['vd304wr6583', 'cp334dd5612', 'sx383dj0454', 'vd388fr6553', 'dd741rv3588']
HIT (train), Ground truth: -183.87550394601823, found ll: -129.42510986328125
HIT (test), Ground truth: -183.87550394601823, found ll: -172.72467041015625
best Likelihood:
(lambda ((lambda (r_l (r_l $0))) (r_d ((lambda (r_d (r_d $0))) ((lambda (r_l (r_l $0))) ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))))))
\l\l\d\d\d\l\l\d\d\d\d
samples:
['rh974dr3650', 'dx627kj9735', 'fv535rt8702', 'fr265tf8382', 'rp837xr6367']
HIT (train), Ground truth: -183.87550394601823, found ll: -129.42510986328125
HIT (test), Ground truth: -183.87550394601823, found ll: -172.72467041015625
Posterior predictive samples...
testingExamples ['vx793tc4890', 'qy821tc8518', 'zw345zp0788', 'bb896gq6656', 'qc288yw9773']
trainingExamples ['kt831qh3476', 'vw545jd0450', 'st391cf4769', 'vx806dr5738', 'rp023xr5328']
Data column no. 364
TRAIN ['DE', 'SE', 'BD', 'NA', 'CH']
TEST ['GB', 'NL', 'FR', 'UA', 'US']
Human written regex: \u\u
best Posterior:
(lambda ((lambda (r_u (r_u $0))) $0))
\u\u
samples:
['AB', 'BA', 'CA', 'SA', 'NC']
HIT (train), Ground truth: -32.58096538021482, found ll: -20.332683563232422
MISS (test), Ground truth: -32.58096538021482, found ll: -55.615596771240234
best Likelihood:
(lambda (r_u (r_u $0)))
\u\u
samples:
['AS', 'DN', 'AN', 'DD', 'EH']
HIT (train), Ground truth: -32.58096538021482, found ll: -20.332683563232422
MISS (test), Ground truth: -32.58096538021482, found ll: -55.615596771240234
Posterior predictive samples...
testingExamples ['GB', 'NL', 'FR', 'UA', 'US']
trainingExamples ['DE', 'SE', 'BD', 'NA', 'CH']
Data column no. 334
TRAIN ['-00:16:05.9', '-00:19:52.9', '-00:33:24.7', '-00:44:02.3', '-00:24:25.0']
TEST ['-00:17:28.6', '+00:03:04.8', '-00:22:06.4', '-00:10:05.6', '-00:36:31.5']
Human written regex: -00:\d\d:\d\d.\d
best Posterior:
(lambda ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) (r_kleene (lambda (r_d $0)) (string_period (r_kleene (lambda (r_d $0)) $0))))))
-00:\d\d.(.)*(\d)*\.(\d)*
samples:
['-00:34:34.4', '-00:03::::.', '-00:05e0.', '-00:91::62.4', '-00:44:::::3:73.']
HIT (train), Ground truth: -80.38636828219032, found ll: tensor([-73.2511], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) (r_kleene (lambda (r_d $0)) (string_period (r_kleene (lambda (r_d $0)) $0))))))
-00:\d\d.(.)*(\d)*\.(\d)*
samples:
['-00:00::.2', '-00:22::.1', '-00:71:::60.52', '-00:24::42.054', '-00:04:0.5']
HIT (train), Ground truth: -80.38636828219032, found ll: tensor([-73.2511], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['-00:17:28.6', '+00:03:04.8', '-00:22:06.4', '-00:10:05.6', '-00:36:31.5']
trainingExamples ['-00:16:05.9', '-00:19:52.9', '-00:33:24.7', '-00:44:02.3', '-00:24:25.0']
Data column no. 38
TRAIN ['SRX897027', 'SRX897025', 'SRX897032', 'SRX894622', 'SRX897016']
TEST ['SRX897019', 'SRX894623', 'SRX897038', 'SRX897022', 'SRX897035']
Human written regex: SRX89\d+
best Posterior:
(lambda ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) ((lambda (r_d (r_d $0))) $0)))
SRX89\d\d\d\d
samples:
['SRX890367', 'SRX890073', 'SRX890357', 'SRX893447', 'SRX896277']
HIT (train), Ground truth: -59.914645471079815, found ll: -36.956722259521484
HIT (test), Ground truth: -59.914645471079815, found ll: -49.233131408691406
best Likelihood:
(lambda (r_const ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))))
SRX89\d\d\d\d
samples:
['SRX890723', 'SRX892230', 'SRX892244', 'SRX896173', 'SRX890222']
HIT (train), Ground truth: -59.914645471079815, found ll: -36.956722259521484
HIT (test), Ground truth: -59.914645471079815, found ll: -49.233131408691406
Posterior predictive samples...
testingExamples ['SRX897019', 'SRX894623', 'SRX897038', 'SRX897022', 'SRX897035']
trainingExamples ['SRX897027', 'SRX897025', 'SRX897032', 'SRX894622', 'SRX897016']
Data column no. 493
TRAIN ['[0.0175]', '[0.035]', '[0.0475]', '[0.03]', '[0.015]']
TEST ['[0.055]', '[0.1275]', '[]', '[0.01]', '[0.005]']
Human written regex: [0\.0\d+]
best Posterior:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (string_right_bracket $0)))
[0\.0\d(\d)*]
samples:
['[0.01]', '[0.0313]', '[0.054]', '[0.015]', '[0.03]']
HIT (train), Ground truth: -32.9530550090939, found ll: tensor([-24.3112], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_const (r_d (r_kleene (lambda (r_d $0)) $0)))) (string_right_bracket $0)))
[0\.0\d(\d)*]
samples:
['[0.031]', '[0.07]', '[0.07]', '[0.0535]', '[0.01]']
HIT (train), Ground truth: -32.9530550090939, found ll: tensor([-24.3112], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['[0.055]', '[0.1275]', '[]', '[0.01]', '[0.005]']
trainingExamples ['[0.0175]', '[0.035]', '[0.0475]', '[0.03]', '[0.015]']
Data column no. 247
TRAIN ["'02:13:00'", "'03:27:00'", "'09:33:00'", "'15:40:00'", "'22:24:00'"]
TEST ["'17:07:00'", "'02:10:00'", "'05:10:00'", "'05:55:00'", "'13:05:00'"]
Human written regex: '\d\d:\d\d:00'
best Posterior:
(lambda ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) ((lambda ((lambda (r_d (r_d $0))) (r_const $0))) $0)))
.(.)*\d\d:00'
samples:
[":-212:00'", "5:''03:00'", ":17:00'", "'37:00'", "::27:00'"]
MISS (train), Ground truth: -46.05170185988091, found ll: tensor([-65.7744], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -46.05170185988091, found ll: tensor([-93.4199], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_dot (r_kleene (lambda (r_dot $0)) $0))) ((lambda ((lambda (r_d (r_d $0))) (r_const $0))) $0)))
.(.)*\d\d:00'
samples:
["2224:00'", "141:00'", ":13:00'", "2322:00'", "244:00'"]
MISS (train), Ground truth: -46.05170185988091, found ll: tensor([-65.7744], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -46.05170185988091, found ll: tensor([-93.4199], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ["'17:07:00'", "'02:10:00'", "'05:10:00'", "'05:55:00'", "'13:05:00'"]
trainingExamples ["'02:13:00'", "'03:27:00'", "'09:33:00'", "'15:40:00'", "'22:24:00'"]
Data column no. 506
TRAIN ['S5795', 'S5552', 'S6506', 'H5529', 'H5859']
TEST ['H1099', 'H5580', 'H2261', 'H8145', 'H2879']
Human written regex: (S|H)\d+
best Posterior:
(lambda ((lambda (r_u (r_kleene (lambda (r_d $0)) $0))) ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0)))
\u(\d)*\d\d\d\d
samples:
['H5955', 'S2554', 'H5560', 'H8556', 'H5957']
HIT (train), Ground truth: -63.380381373879544, found ll: tensor([-34.4359], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -63.380381373879544, found ll: tensor([-65.2958], grad_fn=<AddBackward0>)
best Likelihood:
(lambda ((lambda (r_u (r_kleene (lambda (r_d $0)) $0))) ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) (r_alt (lambda (r_d $0)) (lambda $0) $0))))
\u(\d)*5\d\d(\d)|
samples:
['H569', 'S9557', 'S525', 'S5858', 'H550']
HIT (train), Ground truth: -63.380381373879544, found ll: tensor([-36.1700], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -63.380381373879544, found ll: tensor([-inf], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['H1099', 'H5580', 'H2261', 'H8145', 'H2879']
trainingExamples ['S5795', 'S5552', 'S6506', 'H5529', 'H5859']
Data column no. 891
TRAIN ['r6', 'r1', 'r2', 'v1', 'r']
TEST ['g1', 'g3', 'i6', 'g5', 'x']
Human written regex: (r|v)\d?
best Posterior:
(lambda (r_l (r_kleene (lambda (r_d $0)) $0)))
\l(\d)*
samples:
['v', 'r2', 'r162', 'r', 'r6']
HIT (train), Ground truth: -16.141812177575634, found ll: tensor([-12.9515], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-58.7056], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_alt (lambda (r_l $0)) (lambda (r_l (r_d $0))) $0))
(\l)|(\l\d)
samples:
['r', 'r1', 'v', 'r6', 'r1']
HIT (train), Ground truth: -16.141812177575634, found ll: -10.234539031982422
HIT (test), Ground truth: -inf, found ll: -55.98872375488281
Posterior predictive samples...
testingExamples ['g1', 'g3', 'i6', 'g5', 'x']
trainingExamples ['r6', 'r1', 'r2', 'v1', 'r']
Data column no. 911
TRAIN ['KW-0497', 'KW-0494', 'KW-1216', 'KW-0528', 'KW-0568']
TEST ['KW-1203', 'KW-0959', 'KW-1071', 'KW-0505', 'KW-0499']
Human written regex: KW-\d+
best Posterior:
(lambda ((lambda (r_const ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0))) $0))
KW-\d\d\d\d
samples:
['KW-0057', 'KW-4150', 'KW-1570', 'KW-2908', 'KW-7214']
HIT (train), Ground truth: -59.914645471079815, found ll: -42.78369140625
HIT (test), Ground truth: -59.914645471079815, found ll: -46.491050720214844
best Likelihood:
(lambda (r_const ((lambda ((lambda (r_d (r_d $0))) ((lambda (r_d (r_d $0))) $0))) $0)))
KW-\d\d\d\d
samples:
['KW-9096', 'KW-1568', 'KW-9868', 'KW-0252', 'KW-1506']
HIT (train), Ground truth: -59.914645471079815, found ll: -42.78369140625
HIT (test), Ground truth: -59.914645471079815, found ll: -46.491050720214844
Posterior predictive samples...
testingExamples ['KW-1203', 'KW-0959', 'KW-1071', 'KW-0505', 'KW-0499']
trainingExamples ['KW-0497', 'KW-0494', 'KW-1216', 'KW-0528', 'KW-0568']
Data column no. 792
TRAIN ['22G', '2', '22EXC', '9', 'BL']
TEST ['31', '34', '3', 'N_W04', '09X']
Human written regex: \d*\u*
best Posterior:
(lambda (r_kleene (lambda (r_d $0)) (r_kleene (lambda (r_u $0)) $0)))
(\d)*(\u)*
samples:
['9L', 'LXGX', '20', '2222BG', '']
HIT (train), Ground truth: -48.613327758411955, found ll: tensor([-28.7304], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_alt (lambda (r_d $0)) (lambda (r_kleene (lambda (r_d $0)) (r_u (r_kleene (lambda (r_u $0)) $0)))) $0))
(\d)|((\d)*\u(\u)*)
samples:
['2', 'G', '9', '2', '2']
HIT (train), Ground truth: -48.613327758411955, found ll: tensor([-26.0104], grad_fn=<AddBackward0>)
HIT (test), Ground truth: -inf, found ll: tensor([-inf], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['31', '34', '3', 'N_W04', '09X']
trainingExamples ['22G', '2', '22EXC', '9', 'BL']
Data column no. 508
TRAIN ['N00028139', 'N00009573', 'N00027605', 'N00024852', 'N00035483']
TEST ['N00005582', 'N00033443', 'N00031129', 'N00005282', 'N00033054']
Human written regex: N000\d+
best Posterior:
(lambda ((lambda ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0))) $0))
N000\d\d\d\d\d
samples:
['N00089296', 'N00070285', 'N00033822', 'N00062322', 'N00084205']
HIT (train), Ground truth: -74.89330683884977, found ll: -54.995365142822266
HIT (test), Ground truth: -74.89330683884977, found ll: -54.01658630371094
best Likelihood:
(lambda ((lambda (r_const ((lambda (r_d (r_d $0))) $0))) ((lambda ((lambda (r_d (r_d $0))) (r_d $0))) $0)))
N000\d\d\d\d\d
samples:
['N00048270', 'N00079926', 'N00053507', 'N00079222', 'N00093421']
HIT (train), Ground truth: -74.89330683884977, found ll: -54.995365142822266
HIT (test), Ground truth: -74.89330683884977, found ll: -54.01658630371094
Posterior predictive samples...
testingExamples ['N00005582', 'N00033443', 'N00031129', 'N00005282', 'N00033054']
trainingExamples ['N00028139', 'N00009573', 'N00027605', 'N00024852', 'N00035483']
Data column no. 842
TRAIN ['-11.50%', '14.20%', '0.70%', '33.80%', '6.70%']
TEST ['35.50%', '26.20%', '25.20%', '46.60%', '7.20%']
Human written regex: -?\d?\d\.\d\d%
best Posterior:
(lambda (r_kleene (lambda (string_dash $0)) ((lambda (r_kleene (lambda (r_d $0)) (string_period ((lambda (r_d (r_d $0))) $0)))) (string_percent $0))))
(-)*(\d)*\.\d\d%
samples:
['.71%', '.01%', '-0.48%', '-.07%', '--08.48%']
MISS (train), Ground truth: -48.37800347949227, found ll: tensor([-48.3887], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -50.680588572486315, found ll: tensor([-57.7829], grad_fn=<AddBackward0>)
best Likelihood:
(lambda (r_kleene (lambda (string_dash $0)) ((lambda (r_kleene (lambda (r_d $0)) (string_period ((lambda (r_d (r_d $0))) $0)))) (string_percent $0))))
(-)*(\d)*\.\d\d%
samples:
['.03%', '6.74%', '0.36%', '7.70%', '-0.01%']
MISS (train), Ground truth: -48.37800347949227, found ll: tensor([-48.3887], grad_fn=<AddBackward0>)
MISS (test), Ground truth: -50.680588572486315, found ll: tensor([-57.7829], grad_fn=<AddBackward0>)
Posterior predictive samples...
testingExamples ['35.50%', '26.20%', '25.20%', '46.60%', '7.20%']
trainingExamples ['-11.50%', '14.20%', '0.70%', '33.80%', '6.70%']
Data column no. 200
TRAIN ['NY', 'IL', 'MS', 'VA', 'FL']
TEST ['LA', 'WI', 'AZ', 'GA', 'TX']
Human written regex: \u\u
best Posterior:
(lambda ((lambda (r_u (r_u $0))) $0))
\u\u
samples:
['SN', 'NV', 'VN', 'LV', 'LY']