todo

results/df_imputed.csv

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+50;28.0;0.0120381402643926;0.0003363372193979;0.0024243959714784;0.0005271237614375;0.685589519650655;0.866847826086957;0.0020457241101644;0.0895089454370568;0.0199865979729016;0.0330852039996661;0.0180689513732146;0.0223292828654985;79.1;25.8;25.4;9.0;66.0;112.0;51.0;MAN;ST;Initial
+51;28.0;0.0221815561501189;0.0077847882902291;0.0416602224099979;0.0080556351965035;0.705240174672489;0.869565217391304;0.0360069206411386;0.0400074301071161;0.0376318042074121;0.0044108610296818;0.0127239145840875;0.0360974895403457;77.7;25.4;23.2;8.0;71.0;109.0;47.0;MAN;ST;Final
+52;29.0;0.0404981466331001;0.0122387328454535;0.193340948330285;0.0128209178631972;0.589519650655022;0.741847826086957;0.0050381396968411;0.0087994078449807;0.0328367541504408;0.002056611342976;0.0557710279073856;0.0336683457431653;87.9;26.2;16.6;7.0;82.0;123.0;51.0;MAN;ST;Final
+53;29.0;0.08619965612888336;0.0002381528342809;0.0412148466367366;0.0004998458976586;0.552401746724891;0.736413043478261;0.0014363594816048;0.033299341797828674;0.0023227719534166;0.0441872881787738;0.0076446808648219;0.0018915043674954;94.7;28.9;25.5;12.0;86.0;140.0;60.0;MAN;ST;Initial
+54;30.0;0.065986840612533;0.0319057950997071;0.106786847232821;0.0323147144755002;0.497816593886463;0.66304347826087;0.117901174115061;0.0562652898110519;0.117535039382003;0.03316639108796;0.0023812580778497;0.113093819578092;85.4;26.6;29.0;12.0;86.0;134.0;60.0;MAN;ST;Final
+55;30.0;0.087434783577919;0.0063438356664147;0.07174292206764221;0.0064884635345264;0.646288209606987;0.8125;0.0040914482203288;0.0473205122545238;0.0104610705392388;0.0083665370720591;0.0316002513853846;0.0119999695226482;86.5;33.4;39.0;19.0;96.0;134.0;59.0;MAN;ST;Initial
+56;32.0;0.0507846680539426;0.0688734391169728;0.538319292294922;0.0701345772270452;0.558951965065502;0.682065217391304;0.0057998454825406;0.0382999985839884;0.0145673513890989;0.0377600798148024;0.0861201024420721;0.0207281389925678;99.9;30.8;28.8;15.0;107.0;154.0;61.0;MAN;ST;Initial
+57;32.0;0.164926469073301;0.0010671456495164;0.191717397646975;0.0016950886953967;0.414847161572052;0.478260869565217;0.0051578363203081;0.039553966373205185;0.0515449616724398;0.0262003874785116;0.271762143608473;0.0691881205640523;100.5;31.0;26.1;13.0;101.0;146.0;80.0;MAN;ST;Final
+58;33.0;0.0298646897419735;0.038223433701549;0.257989144482181;0.0389224313236512;0.657205240174673;0.826086956521739;0.0042329078662444;0.0150472307913571;0.0200457675448645;0.0071080264722077;0.0268238989494205;0.0195456240892966;89.8;29.7;28.3;14.0;76.0;118.0;72.0;MAN;ST;Initial
+59;33.0;0.0416110900008945;0.0079367458916777;0.262525262437751;0.0086607664289323;0.62882096069869;0.809782608695652;0.0031774012774894;0.0309527493337577;0.0200428377030688;0.0125828554288471;0.0387357471229076;0.0212312790346778;89.2;29.5;28.5;14.0;97.0;130.0;80.0;MAN;ST;Final
+60;34.0;0.0793363817239481;0.0023703184175882;0.0395252644300979;0.0026594856713754;0.565502183406114;0.684782608695652;0.0026659702499483;0.0272179027238625;0.0206697984579807;0.0421450788470592;0.0540245363519378;0.0237504880473042;92.3;31.9;28.5;15.0;85.0;133.0;55.0;MAN;ST;Initial
+61;34.0;0.0169402402948279;0.0008780346482165;0.0979199938318588;0.001263319217568;0.694323144104804;0.888586956521739;0.0026442072274997;0.013616176357661;0.014950380469765;0.0081073685384796;0.0386005578605948;0.0155773488879811;92.3;31.9;28.3;15.0;88.0;118.0;60.0;MAN;ST;Final
+62;35.0;0.030882003394036;0.0558340505204851;0.0995000359764293;0.0562064315899608;0.683406113537118;0.858695652173913;0.027551986419874;0.0434477218568093;0.109119071089209;0.012898730114102364;0.0293481887974597;0.105757375536413;94.3;29.1;26.0;12.0;75.0;111.0;60.0;MAN;ST;Final
+63;35.0;0.0851970948053306;0.0045312735338061;0.0700577342823357;0.0048834074039447;0.650655021834061;0.769021739130435;0.005386348056018;0.219033659335165;0.0483004110938259;0.0318801021801339;0.0681056727754911;0.058120110137969;98.3;30.3;30.2;13.0;82.0;112.0;56.0;MAN;ST;Initial
+64;36.0;0.266494240388194;0.0024102216526547;0.0694219473776887;0.0028270840441796;0.777292576419214;0.989130434782609;0.0665295596252408;0.0834496772801979;0.0211275974309128;0.0061713581441644;0.0344645739292154;0.0237033924828258;86.4;26.7;28.7;11.0;81.0;114.0;80.0;MAN;ST;Initial
+65;36.0;0.0343310992436051;0.0057698070135756;0.164433217513388;0.0062938527054145;0.657205240174673;0.847826086956522;0.0478895308980511;0.030474329171176;0.0525300637086076;0.0467664264063222;0.033633120357990265;0.049641775451218;85.8;26.5;26.9;11.0;81.0;120.0;81.0;MAN;ST;Final
+66;37.0;0.0244680003745071;0.0323364595024875;0.344564297978596;0.0332106976355996;0.685589519650655;0.858695652173913;0.0021763022448557;0.0097335582604239;0.0111277390727372;0.0047081261606062;0.0303031157324677;0.011027897234085;91.6;30.3;40.5;8.0;83.0;117.0;69.0;MAN;ST;Initial
+67;37.0;0.0206596176219684;0.000688970656945;0.0836668234418652;0.0010468677238198;0.665938864628821;0.847826086956522;0.0025136290928084;0.0357724963668061;0.0099228939919132;0.0256822189250524;0.0204834745777279;0.0106826299674312;88.0;29.1;24.9;9.0;85.0;146.0;88.0;MAN;ST;Final
+68;38.0;0.111784868086647;0.027290716957773;0.0624185060903162;0.0276282354234283;0.456331877729258;0.584239130434783;0.0031556382550408;0.020364736642818;0.0144229487009754;0.0379941167820468;0.0791620251475197;0.0193810548763443;83.7;25.8;25.6;9.0;71.0;118.0;70.0;MAN;ST;Initial
+69;38.0;0.0315295460300811;0.0300713181836045;0.1465342938899994;0.030221346406333;0.611353711790393;0.771739130434783;0.0159414139435685;0.0095724014685713;0.0353773335632839;0.0032806258482978;0.0316104243855348;0.0342607180929034;83.3;26.3;26.2;9.0;80.0;132.0;79.0;MAN;ST;Final
+70;39.0;0.0929130264583023;0.003228355459058;0.0669242895034921;0.003577433683818;0.624454148471616;0.842391304347826;0.0055713337468307;0.032236606052656;0.035976237340222;0.0134853162228474;0.113165926079617;0.0423879208690004;88.5;26.1;25.1;10.0;89.0;125.0;61.0;MAN;ST;Final
+71;39.0;0.0376975427273246;0.0034251726597775;0.0404741542429429;0.0037008776788431;0.626637554585153;0.785326086956522;0.0034712020805449;0.0081531937161793;0.0135853565137919;0.0154117608172598;0.0302662176060178;0.0136252486323726;87.6;26.2;27.1;10.0;85.0;122.0;63.0;MAN;ST;Initial
+72;40.0;0.102888926038234;0.0590990134535776;0.0214686025389167;0.0593383776931313;0.462882096069869;0.554347826086957;0.0034603205693206;0.0087735459425891;0.0224386312158125;0.086037229166155;0.0098637245116295;0.0225524847963741;80.2;26.2;20.6;9.0;74.0;136.0;80.0;MAN;ST;Initial
+73;40.0;0.032296284164321;0.0191823857044013;0.167386630673118;0.0197065148104174;0.650655021834061;0.820652173913043;0.0049837321407197;0.0930908665647227;0.0500787086564919;0.0033602193945899;0.0461634151169999;0.0525724397733532;80.4;26.3;15.5;8.0;66.0;106.0;74.0;MAN;ST;Final
+74;41.0;0.0623554776752288;0.0219055417375335;0.12916069921304;0.0223622015227017;0.537117903930131;0.679347826086957;0.0019369089979216;0.0448228594641941;0.0062595290588285;0.0141814006396636;0.357395646008091;0.0336825349889542;94.8;28.3;28.1;13.0;81.0;121.0;79.0;MAN;ST;Initial
+75;41.0;0.0384837260224814;0.0021320250448643;0.336107273770764;0.0030058187112852;0.554585152838428;0.709239130434783;0.0159522954547928;0.0122957164280777;0.0451741350365447;0.0112060168599068;0.972681954915516;0.117968268404946;94.4;28.2;26.5;13.0;80.0;138.0;74.0;MAN;ST;Final
+76;42.0;0.0984156731752492;0.0154578350638266;0.102490167357121;0.0158760533350346;0.574235807860262;0.692934782608696;0.004178500310123;0.0472651542722852;0.0537205148530197;0.0116744562516162;0.0407171187644665;0.0541574965639497;103.7;30.0;27.0;14.0;96.0;149.0;70.0;MAN;ST;Initial
+77;42.0;0.03471268340945244;0.0818426648518961;0.0087431849473245;0.0820074050222488;0.679039301310044;0.845108695652174;0.003046823142798;0.04351440444588661;0.0412187109530117;0.0065683417951656;0.0185161053534221;0.0388323614216472;103.4;29.9;24.7;12.0;112.0;145.0;69.0;MAN;ST;Final
+78;43.0;0.0452315828428794;0.0838593433196807;0.13366737389024;0.0842951528210224;0.532751091703057;0.671195652173913;0.0068553520712956;0.0233520433556844;0.0506626188659772;0.0429925765800138;0.0664392588436791;0.0532739881108947;94.2;32.6;31.8;16.0;108.0;162.0;72.0;MAN;ST;Final
+79;43.0;0.117861293097985;0.0288365298936495;0.0417997015160499;0.0291339807235139;0.587336244541485;0.78804347826087;0.0051687178315324;0.0183653783536386;0.0384185325446488;0.006702293818411;0.0992452451481739;0.0429137067899072;95.9;33.2;33.9;18.0;107.0;150.0;79.0;MAN;ST;Initial
+80;44.0;0.0957314377083294;0.0079431349433579;0.0634544092670395;0.0082844050055712;0.521834061135371;0.630434782608696;0.0028618374519853;0.0158972426523315;0.0074089109532018;0.0156895887168169;0.107678089885387;0.014106875532489;92.3;29.8;29.7;15.0;94.0;143.0;55.0;MAN;ST;Initial
+81;44.0;0.0356428234811793;0.0098717206447441;0.0675170469238123;0.0101988490666798;0.665938864628821;0.831521739130435;0.0079543847049478;0.0109315901225145;0.0613441362472031;0.0226450511901601;0.205954847855056;0.0734029905781956;91.1;29.7;29.1;15.0;60.0;136.0;70.0;MAN;ST;Final
+82;51.0;0.0726663472750304;0.0017377584775122;0.0596397899631605;0.0020654102286413;0.681222707423581;0.8125;0.0035691356815634;0.10079997724174;0.0015561979612237;0.0050422839276982;0.0335752975392264;0.0055414998742925;66.4;28.7;36.8;8.0;83.0;121.0;80.0;WOMAN;SU;Initial
+83;51.0;0.0426631837851977;0.023067383291035;0.219223667777577;0.0236984819165888;0.7882096069869;0.888586956521739;0.278664620942557;0.101152055378645;0.162569790544836;0.0104769974621116;0.0889933615851929;0.163964898237139;65.8;28.5;41.0;8.0;80.0;120.0;80.0;WOMAN;SU;Final
+84;52.0;0.272669466429062;0.0011727822875755;0.0348435362147153;0.0015224771128791;0.7242938876152039;0.8713510036468506;0.2217937856912613;0.13884854316711426;0.0171113965449101;0.0939470125135971;0.0374643402358509;0.0195336810743677;75.4;27.0;41.8;7.0;72.0;108.0;79.0;WOMAN;SU;Initial
+85;52.0;0.0851435371571832;0.0371320353809345;0.0739260986636218;0.0374794432118824;0.34061135371179;0.472826086956522;0.0401310133951403;0.0277509836885827;0.0935902538196619;0.0410583176735918;0.0543344012100275;0.0934594694780702;77.0;27.6;38.7;7.0;78.0;108.0;73.0;WOMAN;SU;Final
+86;53.0;0.0412345799430572;0.0900320520416094;0.0993878629718923;0.0903947933450038;0.703056768558952;0.763586956521739;0.235889400319916;0.108729268110778;0.155050728812431;0.0160090501119502;0.0277998006063585;0.152408395453107;71.8;28.0;37.8;7.0;70.0;109.0;79.0;WOMAN;SU;Final
+87;53.0;0.0634157396789514;0.0178352672816676;0.0197947715670905;0.0180729044584506;0.72707423580786;0.790760869565217;0.0328948084309949;0.0945344622977286;0.0016161087583244;0.0497264223147481;0.008313157432305;0.0046734377685143;69.9;27.0;34.4;6.0;59.0;78.0;76.0;WOMAN;SU;Initial
+88;54.0;0.0400833721706261;0.0400170182842302;0.315386809393836;0.0408349703679197;0.62882096069869;0.760869565217391;0.0076714654131165;0.0156280433563069;0.0740450452006787;0.0220877056402448;0.606269297800941;0.117184885188487;71.2;28.2;37.0;8.0;82.0;114.0;67.0;WOMAN;SU;Final
+89;54.0;0.049945354820723;0.0080291395239912;0.107621393441872;0.0084431476652782;0.62882096069869;0.698369565217391;0.0482921468133494;0.0590796505270835;0.0424921057350495;0.0820327516002845;0.0483238637053084;0.0465033895609543;68.9;25.6;24.8;6.0;75.0;101.0;69.0;WOMAN;SU;Initial
+90;55.0;0.0681188502005413;0.0014064030826439;0.299022241959759;0.0022162047925704;0.524017467248908;0.733695652173913;0.0396848714349449;0.0289504159403363;0.127591825074721;0.0531924769691272;0.08500462025403976;0.20072804291714;74.2;25.6;37.8;6.0;79.0;113.0;60.0;WOMAN;SU;Final
+91;55.0;0.0415769775136639;0.128987320501353;0.0524746325323538;0.129240436085207;0.591703056768559;0.758152173913043;0.0199675730965516;0.0232771181616965;0.005191650517714;0.0101183621244211;0.656522508807204;0.0552664144321781;73.6;25.5;37.4;6.0;76.0;110.0;72.0;WOMAN;SU;Initial
+92;56.0;0.15220917735442;0.0220433233834553;0.0483039291127514;0.0223688772562787;0.59825327510917;0.739130434782609;0.0182809388567884;0.0419050992376516;0.0951333511997158;0.0276782415517836;0.0801027475076495;0.0971008192457126;82.0;30.1;39.8;8.0;85.0;125.0;68.46609497070312;WOMAN;SU;Final
+93;56.0;0.0407585699148205;0.0492353899203213;0.0053765297959747;0.0494236864979533;0.683406113537118;0.847826086956522;0.0070076932284355;0.0260545575744591;0.0084117233106145;0.0049717056446353;0.0280608090048332;0.0088716962813634;81.0;29.4;39.5;7.0;68.0;103.0;79.0;WOMAN;SU;Initial
+94;57.0;0.153902137324668;0.012700011022388935;0.0727982541756912;0.012980551458895206;0.526200873362445;0.676630434782609;0.0186617917496382;0.0097919884502896;0.0904085686523824;0.0255445105183268;0.0931809793461937;0.0923545155200598;80.3;30.2;43.2;8.0;92.0;125.0;59.0;WOMAN;SU;Final
+95;57.0;0.0291644135820902;0.0135821556892273;0.0096287764465121;0.0137885692601388;0.635371179039301;0.790760869565217;0.0016648712173146;0.0127112366388883;0.0055902378273903;0.02177143655717373;0.0274977899148814;0.005514763466479;79.0;29.4;35.0;7.0;90.0;124.0;79.0;WOMAN;SU;Initial
+96;58.0;0.14056402963405;0.0064775773209295;0.0714764539250823;0.0068517411603919;0.569868995633188;0.744565217391304;0.0371821238533608;0.0114634625813762;0.0779927620540136;0.02713241754936;0.0194801985243383;0.0747996489054554;73.8;29.2;40.4;7.0;79.0;109.0;81.0;WOMAN;SU;Final
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+98;60.0;0.0194137190096008;0.004404067624328;0.0029204698132672;0.0045969463818871;0.713973799126637;0.866847826086957;0.0458220437654381;0.134499830207532;0.0667875239596586;0.013088042165838;0.0242431417460158;0.0686441308014138;89.1;32.7;42.6;9.0;81.0;120.0;79.0;WOMAN;SU;Final
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+149;86.0;0.0437637757203068;0.0153687729510915;0.10432719991242;0.015771083048304;0.541484716157205;0.714673913043478;0.0014798855265019;0.0201464556631979;0.0142109794078154;0.0072401722351149;0.0612116034476883;0.0168672491721091;94.3;27.9;28.9;12.0;102.0;149.0;70.0;MAN;SU;Initial
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+151;87.0;0.16936905682086945;0.0122783576645082;0.192355674520487;0.0128407969450555;0.578602620087336;0.698369565217391;0.023297315531181;0.195731387656071;0.227764957744218;0.098951970349224;0.261788471249032;0.245957352577695;78.3;24.4;19.4;8.0;126.0;143.0;72.0;MAN;SU;Initial
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+158;91.0;0.0297598068836809;0.0201072611622546;0.0313256094202047;0.0203552195974623;0.639737991266376;0.815217391304348;0.0188032513955538;0.0146603554813275;0.055175352947481;0.0219426628010699;0.0486810471871521;0.0552564587815948;79.8;24.4;23.5;10.0;100.0;155.0;80.0;MAN;SU;Final
+159;91.0;0.0252867939556219;0.0051190984553149;0.167976812711771;0.0056473011980768;0.633187772925764;0.866847826086957;0.0024918660703598;0.0129451371890692;0.0406521361937733;0.0192917993427168;0.0631695127411903;0.0423741022402876;80.0;24.2;19.9;8.0;90.0;134.0;88.0;MAN;SU;Initial
+160;92.0;0.0919891205159227;0.0010342141112604;0.08809118717908859;0.00121713859897;0.694323144104804;0.804347826086957;0.187651661062688;0.124822634048379;0.0296791616248831;0.0344283630295088;0.0050124609723251;0.0319218272521123;98.7;27.3;29.0;10.0;80.0;118.0;60.0;MAN;SU;Final
+161;92.0;0.0376778471995291;0.0042164539633666;0.0387869171747656;0.0044884389908143;0.65938864628821;0.8125;0.0017845678407817;0.0078217102251176;0.0302805821919921;0.0110201625732117;0.0684728351233373;0.032447495086742;97.8;27.1;30.0;9.0;83.0;136.0;66.0;MAN;SU;Initial
+162;93.0;0.0710692778193462;0.0095593627319652;0.0094904634108907;0.0097821119737399;0.48471615720524;0.595108695652174;0.0016322266836418;0.0797405190164066;0.0451853061976135;0.0679893764235656;0.0373591902331935;0.0485783822147449;92.5;29.9;26.3;12.0;93.0;126.0;79.0;MAN;SU;Initial
+163;93.0;0.179174222226163;0.0547366385603848;0.22433455288410187;0.0549302885726268;0.67467248908297;0.8125;0.351124604185029;0.104472227610558;0.166165390748009;0.14197813087471;0.0958524620401802;0.171875944754689;93.5;30.5;27.9;12.0;80.0;120.0;79.0;MAN;SU;Final
+164;94.0;0.0121524014486854;0.0173469038447944;0.47665105411625;0.0184893442557066;0.700873362445415;0.853260869565217;0.0023395249132199;0.0237477632212184;0.0251119977854453;0.0089355200857999;0.0256709820393701;0.0246744896888397;67.9;24.1;25.0;8.0;75.0;119.0;70.0;MAN;SU;Initial
+165;94.0;0.0477902384561961;0.0176372810301857;0.0628177440959696;0.0179563003575221;0.956331877729258;0.842391304347826;0.308621421342996;0.0890258611520737;0.128604142389583;0.025020012632012367;0.139501771878967;0.134743305853002;69.4;24.6;24.9;7.0;76.0;120.0;59.0;MAN;SU;Final
+166;101.0;0.0318533121321269;0.0066915268397634;0.0530285308412566;0.0069892408531175;0.64410480349345;0.842391304347826;0.0019477905091459;0.0119457175334199;0.0302156599101467;0.0635032091700636;0.0417671284564333;0.0319599321177794;75.8;25.3;36.2;6.0;86.0;120.0;80.0;WOMAN;SA;Initial
+167;101.0;0.0392139001604695;0.0189994998839622;0.0644574136605014;0.0193182245806291;0.668122270742358;0.823369565217391;0.0104027247304106;0.017664458876414;0.0646105792547899;0.0037113981658473;0.0287865010137179;0.0622934567380702;77.5;25.9;24.8;6.0;81.0;121.0;80.0;WOMAN;SA;Final
+168;103.0;0.0487127256529294;0.0183589290312115;0.143680209002008;0.0188413807238205;0.620087336244541;0.83695652173913;0.0026659702499483;0.0111138877646156;0.0200266107568024;0.0157864657048419;0.164096403911227;0.0304283096700525;78.5;27.8;40.5;7.0;76.0;99.0;65.0;WOMAN;SA;Initial
+169;103.0;0.0946745347935634;0.0210418209411776;0.0749173773647767;0.0214008397080861;0.552401746724891;0.741847826086957;0.0645600060936463;0.0255558569813158;0.408803626874991;0.0111476035962049;0.0442502728276182;0.392993698048176;79.9;28.7;41.4;7.0;70.0;112.0;64.0;WOMAN;SA;Final
+170;104.0;0.306563550285796;0.0465773742680174;0.241703916257382;0.0473397665048277;0.358078602620087;0.456521739130435;0.0488253408633391;0.0141759781413644;0.0766306839489177;0.0481458608771758;0.153491846254996;0.0847475839242944;83.0;31.6;42.9;9.0;88.0;130.0;91.0;WOMAN;SA;Final
+171;104.0;0.225895894843825;0.0396406035518884;0.245871099184092;0.0403850700401092;0.451965065502183;0.5625;0.00218718375608;0.0251012911321247;0.026901940186124;0.111524304854822;0.39462675292191;0.0584263673848773;84.0;32.0;40.9;9.0;82.0;136.0;89.0;WOMAN;SA;Initial
+172;105.0;0.0953196050566807;0.0062960845563278;0.99474511103201;0.0085194568450858;0.513100436681223;0.652173913043478;0.0079108586600507;0.02888716571033001;0.0312702861562295;0.0240581363308804;0.351473128171343;0.0560168650128492;58.0;25.1;31.0;5.0;64.0;98.0;69.0;WOMAN;SA;Final
+173;105.0;0.182954355249215;0.0603780826570842;0.8469640612602234;0.0626228135301213;0.445414847161572;0.657608695652174;0.124114517024124;0.0107210243441826;0.0408112590491468;0.125714443512033;0.0874847820268364;0.0474399774867622;57.9;25.1;30.8;5.0;85.0;127.0;71.0;WOMAN;SA;Initial
+174;106.0;0.117467570317233;0.0787845243142488;0.314041645028215;0.0796126962794198;0.550218340611354;0.9375;0.0150491300231776;0.0249788158537689;0.11720735013858;0.0038698635935692;0.0420045856656632;0.113881464598017;95.2;32.9;44.9;9.0;79.0;123.0;50.0;WOMAN;SA;Final
+175;106.0;0.0359265413145774;0.0433319561502047;0.142312748181403;0.0437974541797252;0.587336244541485;0.782608695652174;0.0049946136519439;0.0261088478293968;0.132902968580085;0.0033097100605484;0.0119275331244696;0.126543506709241;93.9;32.5;46.0;9.0;84.0;116.0;54.0;WOMAN;SA;Initial
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+177;107.0;0.0963676512431107;0.0759467601923277;0.298754004911136;0.0767375464417737;0.441048034934498;0.5625;0.0045811162254213;0.03208572790026665;0.0648588426613835;0.0759573312054668;0.206283924018866;0.0782057081522884;70.8;26.3;41.2;7.0;71.0;105.0;80.0;WOMAN;SA;Initial
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+179;108.0;0.0647999451279257;0.125075917547607;0.167330663006052;0.12557094688962;0.609170305676856;0.779891304347826;0.0026659702499483;0.0444357138531849;0.0259439345164175;0.0286776274052528;0.108100117832098;0.0332958733530949;64.8;28.1;35.2;7.0;83.0;113.0;60.0;WOMAN;SA;Initial
+180;109.0;0.170725059872892;0.0178956154731036;0.0736445279338494;0.0182806204464092;0.620087336244541;0.771739130434783;0.0030794676764709;0.0082229981883481;0.0480001328955815;0.0036627202263907;0.0761958602087335;0.0497926846257416;80.7;29.3;38.9;8.0;79.0;136.0;80.0;WOMAN;SA;Final
+181;109.0;0.0700656404580358;0.0055981038903717;0.0707697595401788;0.0059458249833165;0.54585152838428;0.668478260869565;0.0060392387294747;0.014860588125884533;0.0223979890944644;0.0015826976947513;0.0484035197279988;0.023049876599727;80.1;29.1;38.0;7.0;85.0;142.0;80.0;WOMAN;SA;Initial
+182;110.0;0.155917392491549;0.0256670174494331;0.200780758053883;0.0263005833459171;0.585152838427948;0.744565217391304;0.0730802293822566;0.0114353096198377;0.271087203399696;0.0352268618999806;0.0740390800255255;0.263880760764327;93.3;33.5;47.5;10.0;86.0;123.0;89.0;WOMAN;SA;Final
+183;110.0;0.110525105408368;0.0089641232413476;0.181543856490999;0.0095489139723595;0.51528384279476;0.614130434782609;0.0242875330525903;0.0377823253729285;0.0603343190338937;0.0417913837290524;0.0259131537256949;0.0598339198445731;92.7;34.0;49.3;10.0;81.0;121.0;81.0;WOMAN;SA;Initial
+184;111.0;0.137895588144398;0.0367825938440067;0.152500152206542;0.0373078609020919;0.558951965065502;0.692934782608696;0.0065615512682401;0.0124765808003019;0.0922652253793669;0.0089135825022039;0.0058948438188763;0.0868823408930194;86.7;31.1;43.4;9.0;80.0;120.0;79.03202056884766;WOMAN;SA;Final
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+186;112.0;0.178112885731253;0.0032630076409173;0.212156723928542;0.0039361457767139;0.430131004366812;0.491847826086957;0.0246466229229915;0.0223162350391513;0.143749878993645;0.0172661279279147;0.2891900102097;0.158979852522051;66.9;25.5;38.8;7.0;76.0;131.0;69.0;WOMAN;SA;Final
+187;112.0;0.0471678576914661;0.0008927600085875;0.147813544320632;0.0013897163837337;0.698689956331878;0.834239130434783;0.0078564511039293;0.0108264570475597;0.0197439417754246;0.0043957077812712;0.0877704667230649;0.023814467440599;65.3;25.5;38.5;7.0;73.0;105.0;80.0;WOMAN;SA;Initial
+188;113.0;0.275506018845845;0.0590161793514623;0.403077853299468;0.0600886711382357;0.578602620087336;0.779891304347826;0.255639343191983;0.128691489102959;0.216283975645527;0.0528201347666497;0.31449541109301;0.235293053032589;75.3;28.0;40.2;9.0;96.0;136.0;80.0;WOMAN;SA;Final
+189;113.0;0.223428497957815;0.001754251521539;0.0987275236002025;0.0022150886938672;0.637554585152838;0.796195652173913;0.0083352375977975;0.0156369528182078;0.0028203326588044;0.0126949579262421;0.111478737680859;0.0099235997135185;74.6;27.8;38.0;9.0;87.47425842285156;128.1151123046875;69.6730728149414;WOMAN;SA;Initial
+190;114.0;0.0591405165851105;0.0051856847266591;0.0477679507811178;0.0054831615371331;0.65938864628821;0.817934782608696;0.0211101317751009;0.017763203590548;0.109647219549202;0.0072083279793799;0.0207582440333308;0.104812405719939;83.7;28.0;36.4;6.0;61.0;114.0;71.0;WOMAN;SA;Final
+191;114.0;0.0560258274299344;0.0384448381004223;0.18696404993534088;0.0386004592651659;0.696506550218341;0.83695652173913;0.183538449819911;0.224331603799372;0.338517166161962;0.151634342881434;0.0350670753206517;0.336574625094337;84.9;27.9;35.4;6.0;73.0;122.0;69.0;WOMAN;SA;Initial
+192;115.0;0.0398888892655983;0.0138775459128164;0.158526667857135;0.014388542138964;0.646288209606987;0.815217391304348;0.0088357871141144;0.0169799420654224;0.108512825292992;0.0045237869373032;0.620357305397732;0.150777991055106;65.6;24.8;26.1;8.0;71.0;101.0;70.0;WOMAN;SA;Final
+193;115.0;0.11894133687019348;0.0065280862976429;0.0385805171381614;0.0067820407751752;0.600436681222707;0.744565217391304;0.008313474575349;0.0134478927922451;0.0134490934009995;0.0057655604357308;0.0236898979475813;0.0128956150227182;65.2;25.2;39.6;6.0;73.0;117.0;79.0;WOMAN;SA;Initial
+194;116.0;0.0437709424722112;0.0125495545143367;0.0441373645004574;0.0128313401091342;0.648471615720524;0.820652173913043;0.0065833142906886;0.0285753453445014;0.0424125253236434;3.84745118362665e-05;0.0874191388090091;0.0459885004595909;87.9;34.3;45.3;9.0;80.0;130.0;80.0;WOMAN;SA;Initial
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results/predicts/df_pred-test_prueba_RFXGB_little.csv

@@ -1,13 +1,14 @@
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scripts/RFfit.py

@@ -0,0 +1,92 @@
+# -*- coding: utf-8 -*-
+"""
+@author: dres2
+"""
+
+import pandas as pd
+import numpy as np
+from xgboost import XGBRFRegressor
+from sklearn.metrics import mean_squared_error
+import optuna
+import time
+
+def RFfit(X_train, X_test, X_val,  y_train, y_test,  y_val):
+    '''
+    function to train a Random Forest 
+    '''
+    # Búsqueda bayesiana de hiperparámetros con optuna
+    # ==============================================================================
+    def objective(trial):
+        params = {
+            'n_estimators': trial.suggest_int('n_estimators', 10, 1000, step=10),
+            'max_depth': trial.suggest_int('max_depth', 1, 50),
+            'reg_lambda': trial.suggest_float('reg_lambda', 1e-10, 1e+10, log=True),
+            'reg_alpha': trial.suggest_float('reg_alpha', 1e-10, 1e+10, log=True),
+            'gamma': trial.suggest_float('gamma', 1e-10, 1e+10, log=True),
+            'subsample': trial.suggest_float('subsample', 0.1, 1),
+            'colsample_bynode': trial.suggest_float('colsample_bynode', 0.1, 1),
+            }
+
+        model = XGBRFRegressor(
+                        tree_method        = 'hist',
+                        grow_policy        = 'depthwise',
+                        n_jobs             = -1,
+                        learning_rate=0.01, 
+                        random_state       = 42,
+                        enable_categorical = True,
+                        missing = np.nan, 
+                        multi_strategy="multi_output_tree", 
+                        **params
+                )
+        model.fit(X_train, y_train)
+        predictions = model.predict(X_val)
+        score = mean_squared_error(y_val, predictions, squared=False)
+        return score
+
+    study = optuna.create_study(direction='minimize')
+    study.optimize(objective, n_trials=100, show_progress_bar=True, timeout=100*10)
+
+    print('Mejores hiperparámetros:', study.best_params)
+    print('Mejor score:', study.best_value)
+
+    # Random Forest XGBoost con los mejores hiperparámetros encontrados
+    # ==============================================================================
+    rf_XGB = XGBRFRegressor(
+                    tree_method        = 'hist',
+                    grow_policy        = 'depthwise',
+                    n_jobs             = -1,
+                    learning_rate      =  0.3, 
+                    random_state       = 42,
+                    enable_categorical = True,
+                    missing = np.nan,
+                    multi_strategy="multi_output_tree", 
+                    **study.best_params
+                 )
+
+    # Entrenamiento del modelo
+    start = time.time()
+    rf_XGB .fit(
+            X = pd.concat([X_train, X_val]),
+            y = pd.concat([y_train, y_val])
+    )
+    end = time.time()
+    tiempo_entrenamiento_xgboost = end - start
+
+    # Predicciones test
+    start = time.time()
+    predicciones = rf_XGB.predict(X=X_test)
+    end = time.time()
+    tiempo_prediccion_xgboost = end - start
+
+    # Error de test del modelo
+    rmse_rf_xgboost = mean_squared_error(
+                          y_true  = y_test,
+                          y_pred  = predicciones,
+                          squared = False
+                      )
+                     
+    print(f"Tiempo entrenamiento: {tiempo_entrenamiento_xgboost:.2f} segundos")
+    print(f"Tiempo predicción:    {tiempo_prediccion_xgboost:.2f} segundos")
+    print(f"RMSE:                 {rmse_rf_xgboost:.2f}")
+    
+    return (rf_XGB)

scripts/XGBFfit.py

@@ -1,31 +1,32 @@
-from lightgbm import LGBMRegressor
+from xgboost import XGBRegressor
 from sklearn.metrics import mean_squared_error
 import numpy as np
 import optuna
 import time
 
-def LGBMRfit(X_train, X_test, X_val,  y_train, y_test,  y_val):
+def XGBfit(X_train, X_test, X_val,  y_train, y_test,  y_val):
     # Búsqueda bayesiana de hiperparámetros con optuna
     # ==============================================================================
     def objective(trial):
         params = {
-            'n_estimators': trial.suggest_int('n_estimators', 50, 1000, step=100),
-            'num_leaves': trial.suggest_int('num_leaves', 5, 256),
-            'reg_lambda': trial.suggest_float('reg_lambda', 1e-5, 1e+3, log=True),
-            'reg_alpha': trial.suggest_float('reg_alpha', 1e-5, 1e+3, log=True),
+            'n_estimators': trial.suggest_int('n_estimators', 10, 1000, step=10),
+            'max_depth': trial.suggest_int('max_depth', 3, 12),
+            'scale_pos_weight': trial.suggest_int('scale_pos_weight', 1, 5),
+            'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),
+            'reg_lambda': trial.suggest_float('reg_lambda', 0, 0.1, log=True),
+            'reg_alpha': trial.suggest_float('reg_alpha', 0, 0.1, log=True),
             'min_samples_leaf': trial.suggest_int('min_samples_leaf', 1, 1000),
-            'colsample_bytree': trial.suggest_float('colsample_bytree', 0.1, 1),
             'colsample_bynode': trial.suggest_float('colsample_bynode', 0.1, 1),
-            'subsample': trial.suggest_float('subsample', 0.1, 1),
+            'subsample': trial.suggest_float('subsample', 0.6, 1),
         }
 
-        model = LGBMRegressor(
-                    boosting_type  = 'rf',
-                    learning_rate  = 1.0,
-                    subsample_freq = 1,
+        model = XGBRegressor(
+                    tree_method = 'gpu_hist',
+                    eval_metric = 'rmse',
                     n_jobs         = -1,
                     random_state   = 42,
                     verbose        = 2,
+                    early_stopping_rounds = 10,
                     missing = np.nan, 
                     **params
                 )
@@ -40,39 +41,39 @@ def LGBMRfit(X_train, X_test, X_val,  y_train, y_test,  y_val):
     print('Mejores hiperparámetros:', study.best_params)
     print('Mejor score:', study.best_value)
         
-    # Random Forest LightGBM con los mejores hiperparámetros encontrados
+    # XGBoost con los mejores hiperparámetros encontrados
     # ==============================================================================
-    rf_lgbm = LGBMRegressor(
-                    boosting_type  = 'rf',
-                    learning_rate  = 1.0,
-                    subsample_freq = 1,
+    xgb_xgb = XGBRegressor(
+                    tree_method = 'gpu_hist',
+                    eval_metric = 'rmse',
                     n_jobs         = -1,
                     random_state   = 42,
                     verbose        = 2,
+                    early_stopping_rounds = 10,
                     missing = np.nan, 
                     **study.best_params
-              )
+            )
 
     # Entrenamiento del modelo
     start = time.time()
-    rf_lgbm.fit(X_train, y_train, categorical_feature='auto')
+    xgb_xgb.fit(X_train, y_train, categorical_feature='auto')
     end = time.time()
-    tiempo_entrenamiento_lgbm = end - start
+    tiempo_entrenamiento_xgb_xgb = end - start
 
     # Predicciones test
     start = time.time()
-    predicciones = rf_lgbm.predict(X=X_test)
+    predicciones = xgb_xgb.predict(X=X_test)
     end = time.time()
-    tiempo_prediccion_lgbm = end - start
+    tiempo_prediccion_xgb_xgb = end - start
 
     # Error de test del modelo
-    rmse_rf_lgbm = mean_squared_error(
+    rmse_xgb_xgb = mean_squared_error(
                     y_true  = y_test,
                     y_pred  = predicciones,
                     squared = False
                    )
 
-    print(f"Tiempo entrenamiento: {tiempo_entrenamiento_lgbm:.2f} segundos")
-    print(f"Tiempo predicción:    {tiempo_prediccion_lgbm:.2f} segundos")
-    print(f"RMSE:                 {rmse_rf_lgbm:.2f}")
-    return rf_lgbm
+    print(f"Tiempo entrenamiento: {tiempo_entrenamiento_xgb_xgb:.2f} segundos")
+    print(f"Tiempo predicción:    {tiempo_prediccion_xgb_xgb:.2f} segundos")
+    print(f"RMSE:                 {rmse_xgb_xgb:.2f}")
+    return xgb_xgb

scripts/fitEnsemble.py

@@ -1,76 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@author: dres2
-"""
-
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pickle
-import re
-import os
-from utils import encodingSplitting
-from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
-
-from xgboost import XGBRFRegressor
-from lightgmb import LGBMRegressor
-import optuna
-
-paths = ["plasmFlav_ord.csv", "plasmAnt_ord.csv", "urineFlav_ord.csv", "urineAnt_ord.csv"]
-paths = ["../data/" + s for s in paths]
-
-def fitRF():
-    '''
-    function to train a Random Forest 
-    '''
-    
-    param_grid_light = {
-    'bootstrap': [False, True],
-    'max_depth': [80, 90, 100, 110],
-    'max_features': [0.2, 0.5, 0.7, 0.9, 2, 3],
-    'min_samples_leaf': [3, 4, 5],
-    'min_samples_split': [8, 10, 12],
-    'n_estimators': [100, 200, 300, 1000]
-    }
-    
-
-    print(" ----------------- SETTING UP TRAINING ----------------- ")
-    
-    X_train, X_test, y_train, y_test = encodingSplitting(df, full = full)
-    
-    model = RandomForestRegressor()
-    
-    cv = RepeatedKFold(n_splits=3, n_repeats=3, random_state=42)
-    
-    grid_search = GridSearchCV(estimator = model, param_grid = param_grid_light, cv = cv, n_jobs = -1, verbose = 2)
-
-    print(" ----------------- STARTING TRAINING ----------------- ")
-
-    grid_search.fit(X_train, y_train)
-
-    print(" ----------------- TRAINING ENDED ----------------- ")
-
-    if (full):    
-        filename = os.path.join("..", "models","RF_Full_5050"+df_name+".pkl")
-                
-    else: 
-        filename = os.path.join("..", "models","RF_Met_5050"+df_name+".pkl")
-    
-    best_model = grid_search.best_estimator_
-    
-    with open(filename, 'wb') as file:
-        pickle.dump(best_model, file)
-    
-    print(" ----------------- MODEL SAVED ----------------- ")
-
-    if (eval):
-        evaluateNPlot(filename, df, df_name, y_test=y_test, X_test=X_test, full = full,  pipeline = True,  plot = plot)
-    else:
-        print(" ----------------- ENDED SCRIPT ----------------- ")
-
-
-
-    print("lalala")
-
-fitRF()

scripts/pruebas.ipynb

@@ -1,5 +1,55 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "732da83a-0ddd-4168-b117-2a4d0d7558e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting optuna\n",
+      "  Downloading optuna-3.6.1-py3-none-any.whl.metadata (17 kB)\n",
+      "Collecting alembic>=1.5.0 (from optuna)\n",
+      "  Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB)\n",
+      "Collecting colorlog (from optuna)\n",
+      "  Downloading colorlog-6.8.2-py3-none-any.whl.metadata (10 kB)\n",
+      "Requirement already satisfied: numpy in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from optuna) (1.26.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from optuna) (23.1)\n",
+      "Requirement already satisfied: sqlalchemy>=1.3.0 in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from optuna) (2.0.25)\n",
+      "Requirement already satisfied: tqdm in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from optuna) (4.65.0)\n",
+      "Requirement already satisfied: PyYAML in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from optuna) (6.0.1)\n",
+      "Collecting Mako (from alembic>=1.5.0->optuna)\n",
+      "  Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB)\n",
+      "Requirement already satisfied: typing-extensions>=4 in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from alembic>=1.5.0->optuna) (4.9.0)\n",
+      "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from sqlalchemy>=1.3.0->optuna) (3.0.1)\n",
+      "Requirement already satisfied: colorama in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from colorlog->optuna) (0.4.6)\n",
+      "Requirement already satisfied: MarkupSafe>=0.9.2 in c:\\users\\dres2\\anaconda3\\lib\\site-packages (from Mako->alembic>=1.5.0->optuna) (2.1.3)\n",
+      "Downloading optuna-3.6.1-py3-none-any.whl (380 kB)\n",
+      "   ---------------------------------------- 0.0/380.1 kB ? eta -:--:--\n",
+      "   ----------------- --------------------- 174.1/380.1 kB 10.9 MB/s eta 0:00:01\n",
+      "   ---------------------------------------- 380.1/380.1 kB 6.0 MB/s eta 0:00:00\n",
+      "Downloading alembic-1.13.1-py3-none-any.whl (233 kB)\n",
+      "   ---------------------------------------- 0.0/233.4 kB ? eta -:--:--\n",
+      "   ---------------------------------------- 233.4/233.4 kB 7.2 MB/s eta 0:00:00\n",
+      "Downloading colorlog-6.8.2-py3-none-any.whl (11 kB)\n",
+      "Downloading Mako-1.3.5-py3-none-any.whl (78 kB)\n",
+      "   ---------------------------------------- 0.0/78.6 kB ? eta -:--:--\n",
+      "   ---------------------------------------- 78.6/78.6 kB 4.3 MB/s eta 0:00:00\n",
+      "Installing collected packages: Mako, colorlog, alembic, optuna\n",
+      "Successfully installed Mako-1.3.5 alembic-1.13.1 colorlog-6.8.2 optuna-3.6.1\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install optuna"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -484,7 +534,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

scripts/pruebas_retrain.py

@@ -3,16 +3,20 @@
 import numpy as np
 import pandas as pd
 from utils import fullRead, scaling
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import IterativeImputer
+
 # Gráficos
 # ==============================================================================
 from plotting import plotTestVsPredicted, plotResiduals
+
 # Modelado
 # ==============================================================================
-import xgboost
-from xgboost import XGBRFRegressor
-from sklearn.metrics import mean_squared_error
+from xgboost import XGBRegressor
 from sklearn.model_selection import train_test_split
-import sklearn
+from XGBFfit import XGBfit
+from RFfit import RFfit
+
 import optuna
 import time
 
@@ -22,28 +26,44 @@ import warnings
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 warnings.filterwarnings("ignore", category=FutureWarning)
 optuna.logging.set_verbosity(optuna.logging.WARNING)
-import sys
-print(f"XGBoost version: {xgboost.__version__}")
-print(f"Optuna version: {optuna.__version__}")
-print(f"Scikit-learn version: {sklearn.__version__}")
 
+import sys
 
 paths = ["plasmFlav_ord.csv", "plasmAnt_ord.csv", "urineFlav_ord.csv", "urineAnt_ord.csv"]
 paths = ["../data/" + s for s in paths]
 
-df, df_name = fullRead(paths[2], sep = ",", full = False)
+df, df_name = fullRead(paths[2], sep = ",", full = True)
 #Sweetener: object, Sex: object, Weight: object, BMI: object, Fat: object, CVRI: object, Bpmin: object, Bpmax: object, Frec: object
-full = True
+full = False
+
 if not full:
     df[['Weight', 'BMI', "Fat", "CVRI", "Bpmin", "Bpmax", "Frec"]] = df[['Weight', 'BMI', "Fat", "CVRI", "Bpmin", "Bpmax", "Frec"]].apply(pd.to_numeric)
-df.replace([0,1],   np.nan,  inplace = True)
-df.dropna(inplace = True)
+
+# testing imputation
+
+df['HE.G'].where(df['HE.G'] > 2, np.nan, inplace=True)
+
+df.replace([0,1,0.0,1.1], np.nan,  inplace = True)
+
+# df.dropna(inplace = True)
+
+iimp = IterativeImputer(
+    estimator = XGBRegressor(),
+    random_state = 42,
+    verbose = 2,
+)
+
+iimp.set_output(transform="pandas")
+df_categorical = df[['Sex', 'Sweetener', 'Time']]
+df_imp = iimp.fit_transform(df.drop(['Sex', 'Sweetener', 'Time'],axis = 1))
+df = pd.concat([df_imp, df_categorical], axis = 1)
+
 df[['Sex', 'Sweetener']] = df[['Sex', 'Sweetener']].astype("category")
-print(df)
-print(df.any([1]))
-sys.exit("julaii!")
+
+df.to_csv("../results/df_imputed.csv", sep = ";")
+
 #df = scaling(df)
-df = scaling(df[df["numVol"].duplicated(keep=False)])
+#df = scaling(df[df["numVol"].duplicated(keep=False)])
 
 X = df[df["Time"] == "Initial"].set_index("numVol").drop(["Time"], axis=1) 
 y = df[df["Time"] == "Final"].set_index("numVol").drop(["Time", "Sweetener", "Sex"], axis = 1)
@@ -51,7 +71,7 @@ y = df[df["Time"] == "Final"].set_index("numVol").drop(["Time", "Sweetener", "Se
 X_train, X_test, y_train, y_test = train_test_split(
     X,
     y,
-    train_size = 0.8,
+    train_size = 0.9,
     random_state = 42,
     shuffle = True
 )
@@ -59,7 +79,7 @@ X_train, X_test, y_train, y_test = train_test_split(
 X_train, X_val, y_train, y_val = train_test_split(
     X_train,
     y_train,
-    train_size   = 0.8,
+    train_size   = 0.9,
     random_state = 42,
     shuffle      = True
 )
@@ -68,88 +88,54 @@ print("Observaciones en train:", X_train.shape)
 print("Observaciones en validation:", X_val.shape)
 print("Observaciones en test:", X_test.shape)
 
-# Búsqueda bayesiana de hiperparámetros con optuna
-# ==============================================================================
-def objective(trial):
-    params = {
-        'n_estimators': trial.suggest_int('n_estimators', 100, 1000, step=100),
-        'max_depth': trial.suggest_int('max_depth', 3, 30),
-        'reg_lambda': trial.suggest_float('reg_lambda', 1e-5, 1e+3, log=True),
-        'reg_alpha': trial.suggest_float('reg_alpha', 1e-5, 1e+3, log=True),
-        'gamma': trial.suggest_float('gamma', 1e-5, 1e+3, log=True),
-        'subsample': trial.suggest_float('subsample', 0.5, 1),
-        'colsample_bynode': trial.suggest_float('colsample_bynode', 0.5, 1),
-        }
+print(" ----------------- STARTING RF ----------------- ")
 
-    model = XGBRFRegressor(
-                    tree_method        = 'hist',
-                    grow_policy        = 'depthwise',
-                    n_jobs             = -1,
-                    learning_rate=1, 
-                    random_state       = 42,
-                    enable_categorical = True,
-                    missing = np.nan, 
-                    multi_strategy="multi_output_tree", 
-                    **params
-            )
-    model.fit(X_train, y_train)
-    predictions = model.predict(X_val)
-    score = mean_squared_error(y_val, predictions, squared=False)
-    return score
 
-study = optuna.create_study(direction='minimize')
-study.optimize(objective, n_trials=50, show_progress_bar=True, timeout=60*10)
+modelname = "rf_XGB"
 
-print('Mejores hiperparámetros:', study.best_params)
-print('Mejor score:', study.best_value)
-
-# Random Forest XGBoost con los mejores hiperparámetros encontrados
-# ==============================================================================
-prueba_RFXGB = XGBRFRegressor(
-                tree_method        = 'hist',
-                grow_policy        = 'depthwise',
-                n_jobs             = -1,
-                learning_rate=1, 
-                random_state       = 42,
-                enable_categorical = True,
-                missing = np.nan,
-                multi_strategy="multi_output_tree", 
-                **study.best_params
-             )
-
-# Entrenamiento del modelo
 start = time.time()
-prueba_RFXGB.fit(
-        X = pd.concat([X_train, X_val]),
-        y = pd.concat([y_train, y_val])
-)
+
+rf_XGB = RFfit(X_train, X_test, X_val, y_train, y_test, y_val)
+
 end = time.time()
-tiempo_entrenamiento_xgboost = end - start
 
-# Predicciones test
-start = time.time()
-predicciones = prueba_RFXGB.predict(X=X_test)
-end = time.time()
-tiempo_prediccion_xgboost = end - start
+tiempo_entrenamiento_RF = end - start
 
-# Error de test del modelo
-rmse_rf_xgboost = mean_squared_error(
-                      y_true  = y_test,
-                      y_pred  = predicciones,
-                      squared = False
-                  )
-                 
-print(f"Tiempo entrenamiento: {tiempo_entrenamiento_xgboost:.2f} segundos")
-print(f"Tiempo predicción:    {tiempo_prediccion_xgboost:.2f} segundos")
-print(f"RMSE:                 {rmse_rf_xgboost:.2f}")
-
-# saving predicted
-print(" ----------------- PREDICTING AND JOINING PRED + TEST ----------------- ")
-
-modelname = "prueba_RFXGB_little"
+print(f"Tiempo entrenamiento: {tiempo_entrenamiento_RF:.2f} segundos")
 
 # make predict
-y_pred = prueba_RFXGB.predict(X_test)
+y_pred = rf_XGB.predict(X_test)
+# pred_prueba_DF = pd.DataFrame(pred_prueba, index = X_test.index-1, columns=y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0) 
+df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0)
+df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("../results/predicts/df_pred-test_"+modelname+".csv", sep = ",")
+
+# plotting
+metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) 
+plotTestVsPredicted (metabs=metabs,  df_predTest=df_predTest,   modelname=modelname)
+plotResiduals(metabs=metabs,  df_predTest=df_predTest,   modelname=modelname)
+
+
+print(" ----------------- STARTING XGB ----------------- ")
+
+
+modelname = "xgb_XGB"
+
+start = time.time()
+
+xgb_XGB = XGBfit(X_train, X_test, X_val, y_train, y_test, y_val)
+
+end = time.time()
+
+tiempo_entrenamiento_XGB = end - start
+
+print(f"Tiempo entrenamiento: {tiempo_entrenamiento_XGB:.2f} segundos")
+
+# saving predicted
+print(" ----------------- PREDICTING AND JOINING PRED + TEST ----------------- ")
+
+
+# make predict
+y_pred = xgb_XGB.predict(X_test)
 # pred_prueba_DF = pd.DataFrame(pred_prueba, index = X_test.index-1, columns=y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0) 
 df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0)
 df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("../results/predicts/df_pred-test_"+modelname+".csv", sep = ",")