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Add wodt clf

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Add execution results of RaF, RoF and RRoF
Fix fit time in database records
This commit is contained in:
Ricardo Montañana Gómez
2021-03-10 01:37:00 +01:00
parent f52565b2a5
commit d4cfe77b18
14 changed files with 782 additions and 9 deletions
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14
analysis_mysql.py
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@@ -4,10 +4,18 @@ from experimentation.Sets import Datasets
from experimentation.Utils import TextColor
from experimentation.Database import MySQL
models_tree = ["stree", "oc1", "cart"]
models_ensemble = ["odte", "adaBoost", "bagging"]
models_tree = [
"stree",
"wodt",
"oc1",
"cart",
"baseRaF",
"baseRoF",
"baseRRoF",
]
models_ensemble = ["odte", "adaBoost", "bagging", "TBRaF", "TBRoF", "TBRRoF"]
title = "Best model results"
lengths = (30, 9, 11, 11, 11)
lengths = (30, 9, 11, 11, 11, 11, 11, 11, 11)
def parse_arguments() -> Tuple[str, str, str, bool, bool]:

52
data/ESWA/results_TBRRoF.sql Normal file
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@@ -0,0 +1,52 @@
--
-- TBRRoF
--
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.903019,'balance-scale','TBRRoF',1,0,'{}',0.0237096,211011,0.178456);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.6125,'balloons','TBRRoF',1,0,'{}',0.249671,0.115329,0.0147107);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.975059,'breast-cancer-wisc-diag','TBRRoF',1,0,'{}',0.0144574,142675,0.0638186);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.803922,'breast-cancer-wisc-prog','TBRRoF',1,0,'{}',0.0531358,0.973572,0.0399328);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.970509,'breast-cancer-wisc','TBRRoF',1,0,'{}',0.00955804,237296,0.138799);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.720847,'breast-cancer','TBRRoF',1,0,'{}',0.0609055,158353,0.616083);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.827935,'cardiotocography-10clases','TBRRoF',1,0,'{}',0.0165564,250415,174333);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.92117,'cardiotocography-3clases','TBRRoF',1,0,'{}',0.00984313,386489,373011);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.823077,'conn-bench-sonar-mines-rocks','TBRRoF',1,0,'{}',0.0527948,113023,0.0554272);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.775781,'cylinder-bands','TBRRoF',1,0,'{}',0.0338267,360351,0.429748);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.977065,'dermatology','TBRRoF',1,0,'{}',0.0110894,183941,0.385648);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.851027,'echocardiogram','TBRRoF',1,0,'{}',0.0472795,0.354076,0.0261453);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.882,'fertility','TBRRoF',1,0,'{}',0.0587233,0.410455,0.0491118);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.731984,'haberman-survival','TBRRoF',1,0,'{}',0.0429949,0.667255,0.0682103);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.813607,'heart-hungarian','TBRRoF',1,0,'{}',0.0436205,0.680885,0.0547318);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.827054,'hepatitis','TBRRoF',1,0,'{}',0.0580787,0.658639,0.0417014);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.712495,'ilpd-indian-liver','TBRRoF',1,0,'{}',0.0405313,22513,0.111347);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.950421,'ionosphere','TBRRoF',1,0,'{}',0.0171068,125847,0.0400326);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.966701,'iris','TBRRoF',1,0,'{}',0.0272691,0.382659,0.0201222);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.6716,'led-display','TBRRoF',1,0,'{}',0.0774858,37252,0.237682);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.867222,'libras','TBRRoF',1,0,'{}',0.0437779,384402,0.114302);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.896431,'low-res-spect','TBRRoF',1,0,'{}',0.0203116,300936,0.109406);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.854054,'lymphography','TBRRoF',1,0,'{}',0.0640777,0.861888,0.0410658);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.824359,'mammographic','TBRRoF',1,0,'{}',0.0191403,237551,0.122342);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.810714,'molec-biol-promoter','TBRRoF',1,0,'{}',0.0759873,0.523289,0.0118402);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.890756,'musk-1','TBRRoF',1,0,'{}',0.0265738,202355,0.0349188);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.834648,'oocytes_merluccius_nucleus_4d','TBRRoF',1,0,'{}',0.0264323,601944,178713);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.928392,'oocytes_merluccius_states_2f','TBRRoF',1,0,'{}',0.0149997,491221,0.206203);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.833991,'oocytes_trisopterus_nucleus_2f','TBRRoF',1,0,'{}',0.0267839,46184,0.0751242);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.928947,'oocytes_trisopterus_states_5b','TBRRoF',1,0,'{}',0.0136192,42026,0.312106);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.937439,'parkinsons','TBRRoF',1,0,'{}',0.0303885,0.654357,0.0272996);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.767708,'pima','TBRRoF',1,0,'{}',0.0279917,306414,0.204124);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.860577,'pittsburg-bridges-MATERIAL','TBRRoF',1,0,'{}',0.0651088,0.388944,0.0480131);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.693214,'pittsburg-bridges-REL-L','TBRRoF',1,0,'{}',0.0862127,0.705379,0.0433156);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.686957,'pittsburg-bridges-SPAN','TBRRoF',1,0,'{}',0.0624509,0.596839,0.0344775);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.865407,'pittsburg-bridges-T-OR-D','TBRRoF',1,0,'{}',0.0511018,0.359113,0.0464296);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.725485,'planning','TBRRoF',1,0,'{}',0.0576996,10051,0.166844);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.711364,'post-operative','TBRRoF',1,0,'{}',0.0885492,0.103245,0.0142092);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.943056,'seeds','TBRRoF',1,0,'{}',0.0291748,0.524034,0.03172);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.678338,'statlog-australian-credit','TBRRoF',1,0,'{}',0.0285911,0.465842,0.0118528);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.7538,'statlog-german-credit','TBRRoF',1,0,'{}',0.0283282,588563,0.168927);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.834155,'statlog-heart','TBRRoF',1,0,'{}',0.0358614,0.718854,0.0542387);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.975414,'statlog-image','TBRRoF',1,0,'{}',0.00512352,255329,226215);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.788413,'statlog-vehicle','TBRRoF',1,0,'{}',0.0332766,620603,0.112329);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.99,'synthetic-control','TBRRoF',1,0,'{}',0.00458831,266777,0.0519119);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.985791,'tic-tac-toe','TBRRoF',1,0,'{}',0.00819396,445686,0.0959968);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.846523,'vertebral-column-2clases','TBRRoF',1,0,'{}',0.0342551,137499,0.0622581);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.979792,'wine','TBRRoF',1,0,'{}',0.0228782,0.592137,0.0261656);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','12:16:43','crossval',0.960385,'zoo','TBRRoF',1,0,'{}',0.0408897,0.573084,0.0239393);

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data/ESWA/results_TBRaF.sql Normal file
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@@ -0,0 +1,52 @@
--
-- TBRaF
--
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.898218,'balance-scale','TBRaF',1,0,'{}',0.0221572,147481,0.0929036);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.5375,'balloons','TBRaF',1,0,'{}',0.203182,0.0605146,0.00610705);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.968724,'breast-cancer-wisc-diag','TBRaF',1,0,'{}',0.0158557,122699,0.0508574);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.802041,'breast-cancer-wisc-prog','TBRaF',1,0,'{}',0.0411827,0.516865,0.0182615);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.968274,'breast-cancer-wisc','TBRaF',1,0,'{}',0.0146783,108741,0.0473096);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.730021,'breast-cancer','TBRaF',1,0,'{}',0.0521928,115929,0.0450501);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.830095,'cardiotocography-10clases','TBRaF',1,0,'{}',0.0210438,282584,199304);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.91675,'cardiotocography-3clases','TBRaF',1,0,'{}',0.0118997,315465,192647);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.806731,'conn-bench-sonar-mines-rocks','TBRaF',1,0,'{}',0.0314914,0.502975,0.0217173);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.750391,'cylinder-bands','TBRaF',1,0,'{}',0.020666,187612,0.0361602);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.978128,'dermatology','TBRaF',1,0,'{}',0.0137608,141583,0.714745);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.839821,'echocardiogram','TBRaF',1,0,'{}',0.0599146,0.273034,0.0293564);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.876,'fertility','TBRaF',1,0,'{}',0.0466115,0.24055,0.0217635);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.733114,'haberman-survival','TBRaF',1,0,'{}',0.0450299,0.450113,0.0427755);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.821607,'heart-hungarian','TBRaF',1,0,'{}',0.0349804,0.826003,0.0330775);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.816431,'hepatitis','TBRaF',1,0,'{}',0.0746159,0.412975,0.0340097);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.714522,'ilpd-indian-liver','TBRaF',1,0,'{}',0.0420083,133141,0.0652264);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.936188,'ionosphere','TBRaF',1,0,'{}',0.0313079,0.658064,0.0344485);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.960949,'iris','TBRaF',1,0,'{}',0.0367069,0.209043,0.0243583);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.7122,'led-display','TBRaF',1,0,'{}',0.0144207,331435,0.0872926);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.855,'libras','TBRaF',1,0,'{}',0.0377184,252853,0.0727154);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.895253,'low-res-spect','TBRaF',1,0,'{}',0.0164526,260787,0.13843);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.837838,'lymphography','TBRaF',1,0,'{}',0.0626212,0.544017,0.038001);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.831024,'mammographic','TBRaF',1,0,'{}',0.0203123,263569,0.0958739);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.816621,'molec-biol-promoter','TBRaF',1,0,'{}',0.0896387,0.216461,0.0123003);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.881513,'musk-1','TBRaF',1,0,'{}',0.0231183,14804,0.0423494);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.837735,'oocytes_merluccius_nucleus_4d','TBRaF',1,0,'{}',0.0253044,34712,0.775161);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.925444,'oocytes_merluccius_states_2f','TBRaF',1,0,'{}',0.0188085,41035,0.189899);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.827851,'oocytes_trisopterus_nucleus_2f','TBRaF',1,0,'{}',0.0269572,297983,0.0558168);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.928509,'oocytes_trisopterus_states_5b','TBRaF',1,0,'{}',0.0149313,29922,0.151926);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.907843,'parkinsons','TBRaF',1,0,'{}',0.0501492,0.422776,0.0247379);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.764583,'pima','TBRaF',1,0,'{}',0.0238137,269342,0.0690004);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.861813,'pittsburg-bridges-MATERIAL','TBRaF',1,0,'{}',0.0622277,0.277335,0.0340988);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.700143,'pittsburg-bridges-REL-L','TBRaF',1,0,'{}',0.0617154,0.494063,0.0271107);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.708696,'pittsburg-bridges-SPAN','TBRaF',1,0,'{}',0.089327,0.440551,0.0219081);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.87237,'pittsburg-bridges-T-OR-D','TBRaF',1,0,'{}',0.069001,0.233546,0.024066);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.714468,'planning','TBRaF',1,0,'{}',0.0697879,0.711834,0.0397762);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.708333,'post-operative','TBRaF',1,0,'{}',0.0870909,0.273825,0.0350431);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.94391,'seeds','TBRaF',1,0,'{}',0.0284646,0.413964,0.0266677);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.678151,'statlog-australian-credit','TBRaF',1,0,'{}',0.0327147,0.625115,0.107057);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.7528,'statlog-german-credit','TBRaF',1,0,'{}',0.0268046,423104,0.0719579);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.83501,'statlog-heart','TBRaF',1,0,'{}',0.0402007,0.738742,0.0372339);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.968223,'statlog-image','TBRaF',1,0,'{}',0.00572038,317869,199838);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.776598,'statlog-vehicle','TBRaF',1,0,'{}',0.0357477,374977,0.0753526);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.988333,'synthetic-control','TBRaF',1,0,'{}',0.00888523,137269,0.033371);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.975981,'tic-tac-toe','TBRaF',1,0,'{}',0.0127253,30795,0.0561163);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.845611,'vertebral-column-2clases','TBRaF',1,0,'{}',0.0408165,0.732599,0.0305218);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.98083,'wine','TBRaF',1,0,'{}',0.0133092,0.282677,0.0104738);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:40:09','crossval',0.946769,'zoo','TBRaF',1,0,'{}',0.0385195,0.333227,0.0293076);

52
data/ESWA/results_TBRoF.sql Normal file
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@@ -0,0 +1,52 @@
--
-- TBRoF
--
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.782049,'balance-scale','TBRoF',1,0,'{}',0.165039,191999,0.888319);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.6,'balloons','TBRoF',1,0,'{}',0.318301,0.0945516,0.0317163);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.971164,'breast-cancer-wisc-diag','TBRoF',1,0,'{}',0.013496,101219,0.188329);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.803341,'breast-cancer-wisc-prog','TBRoF',1,0,'{}',0.056903,0.564749,0.0914801);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.961436,'breast-cancer-wisc','TBRoF',1,0,'{}',0.0155321,155881,0.30061);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.723702,'breast-cancer','TBRoF',1,0,'{}',0.0616484,0.834729,0.271552);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.783448,'cardiotocography-10clases','TBRoF',1,0,'{}',0.0161869,41973,484605);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.895115,'cardiotocography-3clases','TBRoF',1,0,'{}',0.0185059,258994,530755);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.796154,'conn-bench-sonar-mines-rocks','TBRoF',1,0,'{}',0.0538172,0.843827,0.109639);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.714844,'cylinder-bands','TBRoF',1,0,'{}',0.0389802,194509,0.464629);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.970531,'dermatology','TBRoF',1,0,'{}',0.0157392,106912,0.0901046);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.825268,'echocardiogram','TBRoF',1,0,'{}',0.0441591,0.311842,0.0811893);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.88,'fertility','TBRoF',1,0,'{}',0.0745513,0.0980062,0.0154864);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.743219,'haberman-survival','TBRoF',1,0,'{}',0.0420406,163565,0.469856);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.813014,'heart-hungarian','TBRoF',1,0,'{}',0.0466896,0.63616,0.118379);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.808344,'hepatitis','TBRoF',1,0,'{}',0.0645135,0.574301,0.21777);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.711911,'ilpd-indian-liver','TBRoF',1,0,'{}',0.038764,153988,0.322007);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.90433,'ionosphere','TBRoF',1,0,'{}',0.0303405,200361,0.505734);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.97474,'iris','TBRoF',1,0,'{}',0.0250567,0.232732,0.0198281);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.6814,'led-display','TBRoF',1,0,'{}',0.097266,551895,0.562322);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.762778,'libras','TBRoF',1,0,'{}',0.0485361,130429,21116);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.863678,'low-res-spect','TBRoF',1,0,'{}',0.0209091,800181,0.88479);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.766216,'lymphography','TBRoF',1,0,'{}',0.0583466,0.645312,0.0846287);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.81604,'mammographic','TBRoF',1,0,'{}',0.0277715,427548,0.412096);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.736538,'molec-biol-promoter','TBRoF',1,0,'{}',0.104285,0.308432,0.0405886);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.866807,'musk-1','TBRoF',1,0,'{}',0.0287601,3109,0.579935);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.823507,'oocytes_merluccius_nucleus_4d','TBRoF',1,0,'{}',0.0245473,598154,0.863981);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.920166,'oocytes_merluccius_states_2f','TBRoF',1,0,'{}',0.0165272,59664,0.832241);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.807237,'oocytes_trisopterus_nucleus_2f','TBRoF',1,0,'{}',0.0217897,372732,0.401537);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.930702,'oocytes_trisopterus_states_5b','TBRoF',1,0,'{}',0.0160742,489326,0.565728);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.878002,'parkinsons','TBRoF',1,0,'{}',0.0545301,0.619312,0.0746759);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.767708,'pima','TBRoF',1,0,'{}',0.0312226,143769,0.143854);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.792308,'pittsburg-bridges-MATERIAL','TBRoF',1,0,'{}',0.0921668,0.406356,0.0446203);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.611857,'pittsburg-bridges-REL-L','TBRoF',1,0,'{}',0.113804,0.76446,0.0680861);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.636957,'pittsburg-bridges-SPAN','TBRoF',1,0,'{}',0.119091,0.731129,0.0847695);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.863037,'pittsburg-bridges-T-OR-D','TBRoF',1,0,'{}',0.0832874,0.208244,0.0339748);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.704421,'planning','TBRoF',1,0,'{}',0.0674753,0.240795,0.145842);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.711364,'post-operative','TBRoF',1,0,'{}',0.102059,0.0934878,0.0101939);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.934188,'seeds','TBRoF',1,0,'{}',0.0909744,0.410396,0.0538859);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.678335,'statlog-australian-credit','TBRoF',1,0,'{}',0.0312903,0.682229,0.217045);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.7514,'statlog-german-credit','TBRoF',1,0,'{}',0.0223003,356689,0.494854);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.826801,'statlog-heart','TBRoF',1,0,'{}',0.0407774,0.493484,0.0934234);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.961298,'statlog-image','TBRoF',1,0,'{}',0.00773378,301575,115945);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.788538,'statlog-vehicle','TBRoF',1,0,'{}',0.0852794,479306,0.769822);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.980333,'synthetic-control','TBRoF',1,0,'{}',0.0119404,224574,0.15732);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.982247,'tic-tac-toe','TBRoF',1,0,'{}',0.0122867,16288,0.239083);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.845627,'vertebral-column-2clases','TBRoF',1,0,'{}',0.0408004,0.610367,0.0614021);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.987648,'wine','TBRoF',1,0,'{}',0.0153501,0.209809,0.0170114);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-09','00:48:19','crossval',0.952385,'zoo','TBRoF',1,0,'{}',0.0455876,0.355625,0.00956);

52
data/ESWA/results_base_RRoF.sql Normal file
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@@ -0,0 +1,52 @@
--
-- Base RRoF
--
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.797369,'balance-scale','baseRRoF',1,0,'{}',0.058467,0.056173,0.0404587);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.625,'balloons','baseRRoF',1,0,'{}',0.190221,0.00330475,0.000990778);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.940946,'breast-cancer-wisc-diag','baseRRoF',1,0,'{}',0.0212105,0.0296062,0.00588306);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.703902,'breast-cancer-wisc-prog','baseRRoF',1,0,'{}',0.0667437,0.0191964,0.00236345);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.952484,'breast-cancer-wisc','baseRRoF',1,0,'{}',0.0154129,0.051149,0.0105423);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.719554,'breast-cancer','baseRRoF',1,0,'{}',0.0526763,0.0273944,0.0119587);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.657922,'cardiotocography-10clases','baseRRoF',1,0,'{}',0.0524926,0.502717,0.126206);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.876762,'cardiotocography-3clases','baseRRoF',1,0,'{}',0.0112342,0.627032,0.282809);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.734615,'conn-bench-sonar-mines-rocks','baseRRoF',1,0,'{}',0.0538895,0.0193592,0.00260686);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.654297,'cylinder-bands','baseRRoF',1,0,'{}',0.0499199,0.0584003,0.0126956);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.851879,'dermatology','baseRRoF',1,0,'{}',0.0774006,0.0322685,0.00427929);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.793482,'echocardiogram','baseRRoF',1,0,'{}',0.0879076,0.00767857,0.00261677);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.832,'fertility','baseRRoF',1,0,'{}',0.0717892,0.00881917,0.00140421);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.729706,'haberman-survival','baseRRoF',1,0,'{}',0.0487396,0.0127047,0.00574701);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.770639,'heart-hungarian','baseRRoF',1,0,'{}',0.053471,0.0146634,0.00465382);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.77991,'hepatitis','baseRRoF',1,0,'{}',0.0671092,0.0136452,0.00159511);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.696055,'ilpd-indian-liver','baseRRoF',1,0,'{}',0.0231133,0.0482305,0.0101052);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.878506,'ionosphere','baseRRoF',1,0,'{}',0.0325688,0.0262894,0.00293877);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.909217,'iris','baseRRoF',1,0,'{}',0.0546734,0.00826875,0.00241578);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.5002,'led-display','baseRRoF',1,0,'{}',0.16598,0.0834546,0.0215293);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.684444,'libras','baseRRoF',1,0,'{}',0.0786005,0.0762693,0.00805703);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.826734,'low-res-spect','baseRRoF',1,0,'{}',0.0437699,0.0586663,0.00444502);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.708108,'lymphography','baseRRoF',1,0,'{}',0.0565566,0.0177934,0.00199152);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.80708,'mammographic','baseRRoF',1,0,'{}',0.0259152,0.0431632,0.00868914);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.638736,'molec-biol-promoter','baseRRoF',1,0,'{}',0.108143,0.00983929,0.00140181);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.756723,'musk-1','baseRRoF',1,0,'{}',0.0425421,0.0401557,0.00192906);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.729155,'oocytes_merluccius_nucleus_4d','baseRRoF',1,0,'{}',0.026248,0.108816,0.00532225);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.880828,'oocytes_merluccius_states_2f','baseRRoF',1,0,'{}',0.0219842,0.102759,0.024178);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.712719,'oocytes_trisopterus_nucleus_2f','baseRRoF',1,0,'{}',0.0333267,0.093124,0.00443858);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.866667,'oocytes_trisopterus_states_5b','baseRRoF',1,0,'{}',0.0233995,0.0830202,0.0114035);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.854289,'parkinsons','baseRRoF',1,0,'{}',0.0629868,0.0129451,0.00232781);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.726302,'pima','baseRRoF',1,0,'{}',0.0387727,0.062338,0.0289431);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.803846,'pittsburg-bridges-MATERIAL','baseRRoF',1,0,'{}',0.0712916,0.00857422,0.00225875);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.601357,'pittsburg-bridges-REL-L','baseRRoF',1,0,'{}',0.0786889,0.0140277,0.00294606);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.584783,'pittsburg-bridges-SPAN','baseRRoF',1,0,'{}',0.0930101,0.0108635,0.00273294);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.849259,'pittsburg-bridges-T-OR-D','baseRRoF',1,0,'{}',0.0577165,0.00722582,0.00266615);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.61227,'planning','baseRRoF',1,0,'{}',0.0876011,0.0209287,0.007585);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.705303,'post-operative','baseRRoF',1,0,'{}',0.086102,0.00269023,0.00129621);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.790705,'seeds','baseRRoF',1,0,'{}',0.180289,0.0103721,0.00346276);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.678214,'statlog-australian-credit','baseRRoF',1,0,'{}',0.0315745,0.0091094,0.000591205);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.6848,'statlog-german-credit','baseRRoF',1,0,'{}',0.0202552,0.114948,0.00756476);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.76883,'statlog-heart','baseRRoF',1,0,'{}',0.0546972,0.0141939,0.0031457);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.879948,'statlog-image','baseRRoF',1,0,'{}',0.0810351,0.506874,0.202689);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.663137,'statlog-vehicle','baseRRoF',1,0,'{}',0.0347429,0.113744,0.0125771);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.885,'synthetic-control','baseRRoF',1,0,'{}',0.0266557,0.0499058,0.00479801);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.824842,'tic-tac-toe','baseRRoF',1,0,'{}',0.0352731,0.0833412,0.012665);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.803313,'vertebral-column-2clases','baseRRoF',1,0,'{}',0.0458269,0.0246671,0.00566697);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.828063,'wine','baseRRoF',1,0,'{}',0.136358,0.0100478,0.00276311);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:39:38','crossval',0.845154,'zoo','baseRRoF',1,0,'{}',0.0862973,0.0103013,0.00116166);

52
data/ESWA/results_base_RaF.sql Normal file
View File

@@ -0,0 +1,52 @@
--
-- Base RaF
--
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.788139,'balance-scale','baseRaF',1,0,'{}',0.175399,0.0531906,0.0387208);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.625,'balloons','baseRaF',1,0,'{}',0.222131,0.00184033,0.0012073);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.953228,'breast-cancer-wisc-diag','baseRaF',1,0,'{}',0.0163919,0.0107458,0.0016314);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.710464,'breast-cancer-wisc-prog','baseRaF',1,0,'{}',0.0714578,0.00518408,0.00147653);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.934215,'breast-cancer-wisc','baseRaF',1,0,'{}',0.0266307,0.0123473,0.00230302);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.644096,'breast-cancer','baseRaF',1,0,'{}',0.0518223,0.0118358,0.00162178);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.728229,'cardiotocography-10clases','baseRaF',1,0,'{}',0.0173198,0.431309,0.0804245);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.872983,'cardiotocography-3clases','baseRaF',1,0,'{}',0.0185093,0.432781,0.0658666);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.725,'conn-bench-sonar-mines-rocks','baseRaF',1,0,'{}',0.0820887,0.00428136,0.000752719);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.642188,'cylinder-bands','baseRaF',1,0,'{}',0.0323213,0.0178885,0.00150941);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.954,'dermatology','baseRaF',1,0,'{}',0.028694,0.0113351,0.00078796);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.697098,'echocardiogram','baseRaF',1,0,'{}',0.0724735,0.00488536,0.00142448);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.79,'fertility','baseRaF',1,0,'{}',0.106128,0.00275558,0.000802812);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.714491,'haberman-survival','baseRaF',1,0,'{}',0.0527132,0.0155133,0.00412764);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.776621,'heart-hungarian','baseRaF',1,0,'{}',0.0558441,0.0131795,0.00145566);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.768549,'hepatitis','baseRaF',1,0,'{}',0.0627836,0.00425792,0.00133006);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.658306,'ilpd-indian-liver','baseRaF',1,0,'{}',0.0468364,0.0396437,0.00471406);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.848391,'ionosphere','baseRaF',1,0,'{}',0.0425361,0.00529335,0.00104925);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.953534,'iris','baseRaF',1,0,'{}',0.0236,0.00274398,0.000563343);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.6732,'led-display','baseRaF',1,0,'{}',0.0339188,0.0837362,0.0068326);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.590556,'libras','baseRaF',1,0,'{}',0.0790025,0.127775,0.0240925);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.733636,'low-res-spect','baseRaF',1,0,'{}',0.0524552,0.0410959,0.00897058);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.695946,'lymphography','baseRaF',1,0,'{}',0.0593915,0.00475246,0.000937836);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.75569,'mammographic','baseRaF',1,0,'{}',0.032263,0.0626184,0.00550619);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.639835,'molec-biol-promoter','baseRaF',1,0,'{}',0.0955089,0.0011949,0.00015005);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.77521,'musk-1','baseRaF',1,0,'{}',0.0271103,0.0202152,0.00422566);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.772783,'oocytes_merluccius_nucleus_4d','baseRaF',1,0,'{}',0.0353748,0.0480226,0.00807228);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.891969,'oocytes_merluccius_states_2f','baseRaF',1,0,'{}',0.0194857,0.0475845,0.00754526);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.74057,'oocytes_trisopterus_nucleus_2f','baseRaF',1,0,'{}',0.0287175,0.0333703,0.00293244);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.906579,'oocytes_trisopterus_states_5b','baseRaF',1,0,'{}',0.0185044,0.0299981,0.00418996);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.827941,'parkinsons','baseRaF',1,0,'{}',0.0676886,0.00474085,0.00101539);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.685677,'pima','baseRaF',1,0,'{}',0.0293304,0.0469974,0.00298769);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.768132,'pittsburg-bridges-MATERIAL','baseRaF',1,0,'{}',0.0740949,0.00492078,0.00125145);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.573786,'pittsburg-bridges-REL-L','baseRaF',1,0,'{}',0.0863999,0.0076706,0.00187105);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.571739,'pittsburg-bridges-SPAN','baseRaF',1,0,'{}',0.126386,0.00753147,0.00159705);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.803407,'pittsburg-bridges-T-OR-D','baseRaF',1,0,'{}',0.0986764,0.00238962,0.000941915);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.613144,'planning','baseRaF',1,0,'{}',0.0599842,0.00957998,0.00164111);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.49072,'post-operative','baseRaF',1,0,'{}',0.117636,0.00542032,0.00112316);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.93052,'seeds','baseRaF',1,0,'{}',0.0377479,0.0052242,0.00100866);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.678291,'statlog-australian-credit','baseRaF',1,0,'{}',0.030839,0.0113963,0.0035361);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.672,'statlog-german-credit','baseRaF',1,0,'{}',0.0368668,0.0587169,0.00545287);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.745187,'statlog-heart','baseRaF',1,0,'{}',0.0466497,0.00900943,0.00111242);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.942258,'statlog-image','baseRaF',1,0,'{}',0.0116565,0.500883,0.271411);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.768526,'statlog-vehicle','baseRaF',1,0,'{}',0.0320319,0.0358268,0.00307129);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.885667,'synthetic-control','baseRaF',1,0,'{}',0.0300701,0.0271531,0.00357264);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.964097,'tic-tac-toe','baseRaF',1,0,'{}',0.0128756,0.0292446,0.00718939);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.810858,'vertebral-column-2clases','baseRaF',1,0,'{}',0.0363136,0.0136081,0.00216191);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.952619,'wine','baseRaF',1,0,'{}',0.0321276,0.00283508,0.000572741);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:15','crossval',0.859385,'zoo','baseRaF',1,0,'{}',0.0984949,0.00476124,0.000706802);

52
data/ESWA/results_base_RoF.sql Normal file
View File

@@ -0,0 +1,52 @@
--
-- Base RoF
--
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.767583,'balance-scale','baseRoF',1,0,'{}',0.203255,0.0446322,0.0545407);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.675,'balloons','baseRoF',1,0,'{}',0.257774,0.00230893,0.00107712);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.972242,'breast-cancer-wisc-diag','baseRoF',1,0,'{}',0.0131518,0.0177623,0.00299349);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.783633,'breast-cancer-wisc-prog','baseRoF',1,0,'{}',0.0607377,0.0332747,0.0116981);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.960812,'breast-cancer-wisc','baseRoF',1,0,'{}',0.010349,0.0332904,0.00682684);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.722333,'breast-cancer','baseRoF',1,0,'{}',0.0369547,0.0148639,0.00494574);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.777798,'cardiotocography-10clases','baseRoF',1,0,'{}',0.0199397,0.812814,0.137849);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.893694,'cardiotocography-3clases','baseRoF',1,0,'{}',0.0189301,0.507345,0.0816501);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.786538,'conn-bench-sonar-mines-rocks','baseRoF',1,0,'{}',0.053272,0.0319204,0.00526368);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.715234,'cylinder-bands','baseRoF',1,0,'{}',0.0316052,0.0382499,0.019126);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.976456,'dermatology','baseRoF',1,0,'{}',0.017583,0.0183773,0.00096312);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.826161,'echocardiogram','baseRoF',1,0,'{}',0.0566044,0.00666127,0.00152563);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.88,'fertility','baseRoF',1,0,'{}',0.0535085,0.0032558,0.00141667);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.732777,'haberman-survival','baseRoF',1,0,'{}',0.0461519,0.0260083,0.00642227);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.806292,'heart-hungarian','baseRoF',1,0,'{}',0.0292422,0.0118435,0.00345694);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.794833,'hepatitis','baseRoF',1,0,'{}',0.0520409,0.00962171,0.00293465);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.704268,'ilpd-indian-liver','baseRoF',1,0,'{}',0.0394383,0.0323293,0.0058233);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.912165,'ionosphere','baseRoF',1,0,'{}',0.0312381,0.0361893,0.00726605);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.977304,'iris','baseRoF',1,0,'{}',0.024722,0.00467289,0.000694593);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.7058,'led-display','baseRoF',1,0,'{}',0.0330989,0.106641,0.00429973);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.748333,'libras','baseRoF',1,0,'{}',0.0516492,0.22779,0.0346553);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.867643,'low-res-spect','baseRoF',1,0,'{}',0.0227084,0.141473,0.0178801);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.771622,'lymphography','baseRoF',1,0,'{}',0.069309,0.0110059,0.00142268);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.815809,'mammographic','baseRoF',1,0,'{}',0.0213706,0.0823246,0.00838469);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.651648,'molec-biol-promoter','baseRoF',1,0,'{}',0.0747578,0.00914322,0.00114168);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.856303,'musk-1','baseRoF',1,0,'{}',0.0337128,0.144454,0.0303389);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.825408,'oocytes_merluccius_nucleus_4d','baseRoF',1,0,'{}',0.0248569,0.110147,0.0221553);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.921528,'oocytes_merluccius_states_2f','baseRoF',1,0,'{}',0.0202779,0.117209,0.0160898);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.805702,'oocytes_trisopterus_nucleus_2f','baseRoF',1,0,'{}',0.0233865,0.0707077,0.00991037);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.932018,'oocytes_trisopterus_states_5b','baseRoF',1,0,'{}',0.0162558,0.0968036,0.0125905);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.911581,'parkinsons','baseRoF',1,0,'{}',0.0266905,0.0106399,0.00138008);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.772135,'pima','baseRoF',1,0,'{}',0.0263347,0.0273879,0.00352025);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.816758,'pittsburg-bridges-MATERIAL','baseRoF',1,0,'{}',0.06753,0.00732728,0.000908743);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.627214,'pittsburg-bridges-REL-L','baseRoF',1,0,'{}',0.0869763,0.0132576,0.00120841);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.632609,'pittsburg-bridges-SPAN','baseRoF',1,0,'{}',0.0919342,0.0137112,0.00153695);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.87437,'pittsburg-bridges-T-OR-D','baseRoF',1,0,'{}',0.0575295,0.00422873,0.000965931);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.710165,'planning','baseRoF',1,0,'{}',0.0602237,0.00391673,0.00158102);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.709091,'post-operative','baseRoF',1,0,'{}',0.0800951,0.00196808,0.0007889);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.949644,'seeds','baseRoF',1,0,'{}',0.0411416,0.00794183,0.0010996);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.678271,'statlog-australian-credit','baseRoF',1,0,'{}',0.0332535,0.0124906,0.00557268);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.7596,'statlog-german-credit','baseRoF',1,0,'{}',0.0251279,0.0651643,0.00937727);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.830554,'statlog-heart','baseRoF',1,0,'{}',0.0438866,0.00845302,0.00176189);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.961124,'statlog-image','baseRoF',1,0,'{}',0.00954905,0.565189,0.239254);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.805401,'statlog-vehicle','baseRoF',1,0,'{}',0.0266276,0.0921317,0.00659751);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.98,'synthetic-control','baseRoF',1,0,'{}',0.0110289,0.0381931,0.00304846);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.983295,'tic-tac-toe','baseRoF',1,0,'{}',0.0045061,0.0308545,0.00203696);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.841328,'vertebral-column-2clases','baseRoF',1,0,'{}',0.0406997,0.0125325,0.00288834);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.989872,'wine','baseRoF',1,0,'{}',0.0134836,0.00354218,0.000355459);
replace into results (date, time, type, accuracy, dataset, classifier, norm, stand, parameters, accuracy_std, time_spent, time_spent_std) values ('2021-03-08','18:38:52','crossval',0.931154,'zoo','baseRoF',1,0,'{}',0.044867,0.00618482,0.000234567);

10
experiment.py
View File

@@ -20,7 +20,15 @@ def parse_arguments() -> Tuple[str, str, str, str, str, bool, bool, dict]:
"-m",
"--model",
type=str,
choices=["stree", "adaBoost", "bagging", "odte", "oc1", "cart"],
choices=[
"stree",
"wodt",
"adaBoost",
"bagging",
"odte",
"oc1",
"cart",
],
required=False,
default="stree",
)

8
experimentation/Database.py
View File

@@ -345,8 +345,8 @@ class Outcomes(BD):
float(results["test_score"].std()),
],
[
float(results["score_time"].mean()),
float(results["score_time"].std()),
float(results["fit_time"].mean()),
float(results["fit_time"].std()),
],
parameters,
)
@@ -441,8 +441,8 @@ class Hyperparameters(BD):
float(outcomes["test_score_std"]),
]
time_spent = [
float(outcomes["score_time"]),
float(outcomes["score_time_std"]),
float(outcomes["fit_time"]),
float(outcomes["fit_time_std"]),
]
self.mirror(
grid_type,

26
experimentation/Models.py
View File

@@ -11,6 +11,7 @@ from sklearn.svm import LinearSVC # type: ignore
from sklearn.tree import DecisionTreeClassifier # type: ignore
from odte import Odte
from sklearn_oblique_tree.oblique import ObliqueTree
from wodt import TreeClassifier
class ModelBase(ABC):
@@ -95,6 +96,31 @@ class ModelOc1(ModelBase):
self._param_grid = [self._rbf]
class ModelWodt(ModelBase):
def __init__(self, random_state: Optional[int] = None) -> None:
self._clf = TreeClassifier()
super().__init__(random_state)
self._model_name = "wodt"
self._linear = {
"random_state": [self._random_state],
}
self._rbf = {}
self._poly = {}
self._param_grid = [
self._linear,
self._poly,
self._rbf,
]
def select_params(self, kernel: str) -> None:
if kernel == "linear":
self._param_grid = [self._linear]
elif kernel == "poly":
self._param_grid = [self._poly]
else:
self._param_grid = [self._rbf]
class ModelStree(ModelBase):
def __init__(self, random_state: Optional[int] = None) -> None:
self._clf = Stree()

2
setup.py
View File

@@ -1,6 +1,6 @@
import setuptools
__version__ = "0.1.0"
__version__ = "0.2.0"
__author__ = "Ricardo Montañana Gómez"

125
testwodt.py Normal file
View File

@@ -0,0 +1,125 @@
import argparse
from wodt import TreeClassifier
from sklearn.model_selection import cross_val_score
import numpy as np
import random
from experimentation.Sets import Datasets
def parse_arguments():
ap = argparse.ArgumentParser()
ap.add_argument(
"-S",
"--set-of-files",
type=str,
choices=["aaai", "tanveer"],
required=False,
default="aaai",
)
ap.add_argument(
"-d",
"--dataset",
type=str,
required=False,
help="Dataset name",
)
ap.add_argument(
"-n",
"--normalize",
default=False,
type=bool,
required=False,
help="Normalize dataset (True/False)",
)
ap.add_argument(
"-s",
"--standardize",
default=False,
type=bool,
required=False,
help="Standardize dataset (True/False)",
)
ap.add_argument(
"-p",
"--paper-norm",
default=False,
type=bool,
required=False,
help="[-1, 1] normalization like on paper (True/False)",
)
ap.add_argument(
"-r",
"--random-set",
default=0,
type=int,
required=False,
help="Set of random seeds: {0, 1}",
)
args = ap.parse_args()
return (
args.set_of_files,
args.dataset,
args.normalize,
args.standardize,
args.paper_norm,
args.random_set,
)
def normalize_paper(data):
min_data = data.min()
return 2 * (data - min_data) / (data.max() - min_data) - 1
def process_dataset(dataset, verbose):
X, y = dt.load(dataset)
if paper_norm:
X = normalize_paper(X)
scores = []
if verbose:
print(f"* Processing dataset [{dataset}] from Set: {set_of_files}")
print(f"X.shape: {X.shape}")
print(f"{X[:4]}")
print(f"Random seeds: {random_seeds}")
print(f"[-1, 1]: {paper_norm} norm: {normalize} std: {standardize}")
for random_state in random_seeds:
random.seed(random_state)
np.random.seed(random_state)
clf = TreeClassifier(random_state=random_state)
res = cross_val_score(clf, X, y, cv=5)
scores.append(res)
if verbose:
print(
f"Random seed: {random_state:5d} Accuracy: {res.mean():6.4f}"
f"±{res.std():6.4f}"
)
return scores
(
set_of_files,
dataset,
normalize,
standardize,
paper_norm,
random_set,
) = parse_arguments()
random_seeds = (
[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
if random_set == 0
else [32, 24, 56, 18, 2, 94, 1256, 84, 156, 42]
)
dt = Datasets(normalize, standardize, set_of_files)
if dataset == "all":
print(
f"* Process all datasets set: {set_of_files} [-1, 1]: {paper_norm} "
f"norm: {normalize} std: {standardize}"
)
print(f"5 Fold Cross Validation with 10 random seeds {random_seeds}")
for dataset in dt:
print(f"- {dataset[0]:20s} ", end="")
scores = process_dataset(dataset[0], verbose=False)
print(f"{np.mean(scores):6.4f}±{np.std(scores):6.4f}")
else:
scores = process_dataset(dataset, verbose=True)
print(f"* Accuracy: {np.mean(scores):6.4f}±{np.std(scores):6.4f}")

289
wodt/WODT.py Normal file
View File

@@ -0,0 +1,289 @@
########################
"""import"""
import numpy as np
import random
from scipy.optimize import minimize
from sklearn.base import BaseEstimator, ClassifierMixin
"""global var"""
epsilonepsilon = 1e-220
epsilon = 1e-50
"""class"""
class SplitQuestion(object):
"""docstring for SplitQuestion"""
def __init__(self, attrIDs=[0], paras=[0], threshold=0):
super(SplitQuestion, self).__init__()
self.attrIDs = attrIDs
self.paras = paras
self.threshold = threshold
# we only consider continuous attributes for simplicity
def test_forOneInstance(self, x):
return np.dot(x[self.attrIDs], self.paras) <= self.threshold
def test(self, X):
return np.dot(X[:, self.attrIDs], self.paras) <= self.threshold
class Node(object):
"""docstring for RBNode"""
def __init__(self, depth, split, sample_ids, X, Y, class_num):
super(Node, self).__init__()
self.sample_ids = sample_ids
self.split = split
self.depth = depth
self.X = X
self.Y = Y
self.class_num = class_num
self.is_leaf = False
# after grow_stump, set the node as an internal node
def find_best_split(self, max_features="sqrt"):
feature_num = self.X.shape[1]
subset_feature_num = feature_num
if max_features == "sqrt":
subset_feature_num = int(np.sqrt(feature_num))
if max_features == "all":
subset_feature_num = feature_num
if max_features == "log":
subset_feature_num = int(np.log2(feature_num))
if isinstance(max_features, int):
subset_feature_num = max_features
if isinstance(max_features, float):
subset_feature_num = int(feature_num * max_features)
# ### get random subset of features
# ### feature 0 is threshold
feature_ids = range(feature_num)
subset_feature_ids = random.sample(feature_ids, subset_feature_num)
self.split.attrIDs = subset_feature_ids
subset_feature_ids = np.array(subset_feature_ids)
X = self.X
subFeatures_X = X[
self.sample_ids[:, None], subset_feature_ids[None, :]
]
Y = self.Y[self.sample_ids]
class_num = self.class_num
# ##############################
# define func and func_gradient for optimization
def func(a):
paras = a[1:]
threshold = a[0]
p = sigmoid(np.dot(subFeatures_X, paras) - threshold)
w_R = p
w_L = 1 - w_R
w_R_sum = w_R.sum()
w_L_sum = w_L.sum()
w_R_eachClass = np.array(
[sum(w_R[Y == k]) for k in range(class_num)]
)
w_L_eachClass = np.array(
[sum(w_L[Y == k]) for k in range(class_num)]
)
fun = (
w_L_sum * np.log2(w_L_sum + epsilonepsilon)
+ w_R_sum * np.log2(w_R_sum + epsilonepsilon)
- np.sum(
w_R_eachClass * np.log2(w_R_eachClass + epsilonepsilon)
)
- np.sum(
w_L_eachClass * np.log2(w_L_eachClass + epsilonepsilon)
)
)
# fun = w_L.sum() * compute_entropy(Y, w_L) + w_R.sum()
# * compute_entropy(Y, w_R)
return fun
def func_gradient(a):
paras = a[1:]
threshold = a[0]
p = sigmoid(np.dot(subFeatures_X, paras) - threshold)
w_R = p
w_L = 1 - w_R
w_R_eachClass = np.array(
[sum(w_R[Y == k]) for k in range(class_num)]
)
w_L_eachClass = np.array(
[sum(w_L[Y == k]) for k in range(class_num)]
)
la = np.log2(
w_L_eachClass[Y] * w_R.sum() + epsilonepsilon
) - np.log2(w_R_eachClass[Y] * w_L.sum() + epsilonepsilon)
beta = la * p * (1 - p)
jac = np.zeros(a.shape)
jac[0] = -np.sum(beta)
jac[1:] = np.dot(subFeatures_X.T, beta)
return jac
################################################
initial_a = np.random.rand(subset_feature_num + 1) - 0.5
result = minimize(
func,
initial_a,
method="L-BFGS-B",
jac=func_gradient,
options={"maxiter": 10, "disp": False},
)
##########################################
self.split.paras = result.x[1:]
self.split.threshold = result.x[0]
return 1
def grow_stump(self):
L_bool = self.split.test(self.X[self.sample_ids])
L_sample_ids = self.sample_ids[L_bool]
R_sample_ids = self.sample_ids[~L_bool]
# if len(R_sample_ids) * len(L_sample_ids) == 0 :
# print('some branch is 0 sample')
LChild = Node(
self.depth + 1,
SplitQuestion(),
L_sample_ids,
self.X,
self.Y,
self.class_num,
)
RChild = Node(
self.depth + 1,
SplitQuestion(),
R_sample_ids,
self.X,
self.Y,
self.class_num,
)
if len(L_sample_ids) == 0:
LChild.is_leaf = True
LChild.class_distribution = compute_class_distribution(
self.Y[self.sample_ids], self.class_num
)
if len(R_sample_ids) == 0:
RChild.is_leaf = True
RChild.class_distribution = compute_class_distribution(
self.Y[self.sample_ids], self.class_num
)
self.LChild = LChild
self.RChild = RChild
class TreeClassifier(BaseEstimator, ClassifierMixin):
"""docstring for TreeClassifier"""
def __init__(
self,
max_depth=50,
min_samples_split=2,
max_features="all",
random_state=None,
):
# super(TreeClassifier, self).__init__()
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.max_features = max_features
self.random_state = random_state
def fit(self, X, Y):
self.X = X
self.Y = Y
self.classNum = self.Y.max() + 1
self.sampleNum = self.X.shape[0]
if self.random_state is not None:
random.seed(self.random_state)
###########
self.root_node = Node(
1,
SplitQuestion(),
np.arange(self.sampleNum, dtype=np.uint32),
self.X,
self.Y,
self.classNum,
)
self.leaf_num = 1
self.tree_depth = self.bulid_subtree(self.root_node)
def bulid_subtree(self, node):
if node.is_leaf:
return node.depth
# stopping conditions
is_leaf = (
node.depth >= self.max_depth
or len(node.sample_ids) < self.min_samples_split
or is_all_equal(self.Y[node.sample_ids])
)
if is_leaf or node.find_best_split(self.max_features) < 0:
node.is_leaf = True
node.class_distribution = compute_class_distribution(
self.Y[node.sample_ids], self.classNum
)
return node.depth
node.grow_stump()
node.is_leaf = False
self.leaf_num += 1
L_subtree_depth = self.bulid_subtree(node.LChild)
R_subtree_depth = self.bulid_subtree(node.RChild)
return max(L_subtree_depth, R_subtree_depth)
def predict_forOneInstance(self, x):
present_node = self.root_node
while not (present_node.is_leaf):
if present_node.split.test_forOneInstance(x):
present_node = present_node.LChild
else:
present_node = present_node.RChild
return np.argmax(present_node.class_distribution)
def predict(self, X):
m = X.shape[0]
Y_predicted = np.zeros((m,), dtype=int)
for i in range(m):
x = X[i]
Y_predicted[i] = self.predict_forOneInstance(x)
return Y_predicted
def score(
self, X: np.array, y: np.array, sample_weight: np.array = None
) -> float:
y_pred = self.predict(X)
return np.mean(y_pred == y)
####################
"""function"""
def sigmoid(z):
# because that -z is too big will arise runtimeWarning in np.exp()
if isinstance(z, float) and (z < -500):
z = -500
elif not (isinstance(z, float)):
z[z < -500] = (-500) * np.ones(sum(z < -500))
return 1 / (np.exp(-z) + 1)
def is_all_equal(x):
x_min, x_max = x.min(), x.max()
return x_min == x_max
def compute_class_distribution(Y, class_num):
sample_num = len(Y)
ratio_each_class = [sum(Y == k) / sample_num for k in range(class_num)]
return np.array(ratio_each_class)

5
wodt/__init__.py Normal file
View File

@@ -0,0 +1,5 @@
from .WODT import TreeClassifier
__all__ = [
"TreeClassifier",
]