From a4ddcda5d699b02735fa6badf874eda14453b99b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= Date: Tue, 1 Mar 2022 23:13:53 +0100 Subject: [PATCH 1/4] Complete pair_check & hidden results --- ...racy_ODTE_Galgo_2022-02-17_09:59:54_0.json | 0 ...racy_ODTE_Galgo_2022-02-19_11:35:08_0.json | 0 ...uracy_ODTE_bart_2022-02-21_11:01:03_0.json | 0 results/exreport_accuracy.csv | 638 ------------------ results/exreport_accuracy.txt | 39 -- src/pair_check.py | 38 +- 6 files changed, 34 insertions(+), 681 deletions(-) rename {results => hidden_results}/results_accuracy_ODTE_Galgo_2022-02-17_09:59:54_0.json (100%) rename {results => hidden_results}/results_accuracy_ODTE_Galgo_2022-02-19_11:35:08_0.json (100%) rename {results => hidden_results}/results_accuracy_ODTE_bart_2022-02-21_11:01:03_0.json (100%) delete mode 100644 results/exreport_accuracy.csv delete mode 100644 results/exreport_accuracy.txt mode change 100644 => 100755 src/pair_check.py diff --git a/results/results_accuracy_ODTE_Galgo_2022-02-17_09:59:54_0.json b/hidden_results/results_accuracy_ODTE_Galgo_2022-02-17_09:59:54_0.json similarity index 100% rename from results/results_accuracy_ODTE_Galgo_2022-02-17_09:59:54_0.json rename to hidden_results/results_accuracy_ODTE_Galgo_2022-02-17_09:59:54_0.json diff --git a/results/results_accuracy_ODTE_Galgo_2022-02-19_11:35:08_0.json b/hidden_results/results_accuracy_ODTE_Galgo_2022-02-19_11:35:08_0.json similarity index 100% rename from results/results_accuracy_ODTE_Galgo_2022-02-19_11:35:08_0.json rename to hidden_results/results_accuracy_ODTE_Galgo_2022-02-19_11:35:08_0.json diff --git a/results/results_accuracy_ODTE_bart_2022-02-21_11:01:03_0.json b/hidden_results/results_accuracy_ODTE_bart_2022-02-21_11:01:03_0.json similarity index 100% rename from results/results_accuracy_ODTE_bart_2022-02-21_11:01:03_0.json rename to hidden_results/results_accuracy_ODTE_bart_2022-02-21_11:01:03_0.json diff --git a/results/exreport_accuracy.csv b/results/exreport_accuracy.csv deleted file mode 100644 index 1b6fcea..0000000 --- a/results/exreport_accuracy.csv +++ /dev/null @@ -1,638 +0,0 @@ -classifier, dataset, accuracy, stdev -ExtraTree, balance-scale, 0.7878399999999999, 0.03687783073880566 -ExtraTree, balloons, 0.575, 0.28927591596182967 -ExtraTree, breast-cancer-wisc-diag, 0.9131858407079645, 0.027969722048511687 -ExtraTree, breast-cancer-wisc-prog, 0.6632179487179486, 0.07656410793102837 -ExtraTree, breast-cancer-wisc, 0.9403360739979445, 0.02165140889671454 -ExtraTree, breast-cancer, 0.656908650937689, 0.05914309228326181 -ExtraTree, cardiotocography-10clases, 0.6989177575255454, 0.030051929270415455 -ExtraTree, cardiotocography-3clases, 0.8819887323943664, 0.01507833257138732 -ExtraTree, conn-bench-sonar-mines-rocks, 0.6956445993031357, 0.07653320217222026 -ExtraTree, cylinder-bands, 0.6958918713116314, 0.04592870865503856 -ExtraTree, dermatology, 0.8975268419104035, 0.039527668904658364 -ExtraTree, echocardiogram, 0.7305982905982905, 0.08853188223988526 -ExtraTree, fertility, 0.8059999999999999, 0.06902173570694958 -ExtraTree, haberman-survival, 0.6500105764145954, 0.06301971546791206 -ExtraTree, heart-hungarian, 0.7604909409701931, 0.04869532741159298 -ExtraTree, hepatitis, 0.7793548387096775, 0.07805118521205993 -ExtraTree, ilpd-indian-liver, 0.6634232242852933, 0.03869963692900845 -ExtraTree, ionosphere, 0.8540885311871226, 0.04332727262995767 -ExtraTree, iris, 0.9333333333333335, 0.044221663871405324 -ExtraTree, led-display, 0.7036999999999999, 0.02880642289490313 -ExtraTree, libras, 0.6022222222222223, 0.05851558829982036 -ExtraTree, low-res-spect, 0.8043255157820492, 0.03860708555393554 -ExtraTree, lymphography, 0.7288505747126437, 0.07209829097764534 -ExtraTree, mammographic, 0.7595207253886009, 0.027055981087449776 -ExtraTree, molec-biol-promoter, 0.6611255411255411, 0.09268635844993099 -ExtraTree, musk-1, 0.7796030701754388, 0.043688902783382194 -ExtraTree, oocytes_merluccius_nucleus_4d, 0.6907929220468675, 0.025573299489788766 -ExtraTree, oocytes_merluccius_states_2f, 0.8678149210903873, 0.01875101003253329 -ExtraTree, oocytes_trisopterus_nucleus_2f, 0.6820134510298443, 0.035675489219353006 -ExtraTree, oocytes_trisopterus_states_5b, 0.8403422806701495, 0.02618600526924095 -ExtraTree, parkinsons, 0.8482051282051282, 0.06020783639564789 -ExtraTree, pima, 0.6649944826415415, 0.03466246243736563 -ExtraTree, pittsburg-bridges-MATERIAL, 0.7835930735930735, 0.07970952540707685 -ExtraTree, pittsburg-bridges-REL-L, 0.6253333333333333, 0.10873604896570042 -ExtraTree, pittsburg-bridges-SPAN, 0.5715204678362573, 0.12252433352049193 -ExtraTree, pittsburg-bridges-T-OR-D, 0.8365714285714287, 0.08634786997098468 -ExtraTree, planning, 0.5972672672672672, 0.08066489784697915 -ExtraTree, post-operative, 0.5733333333333334, 0.10566660824861103 -ExtraTree, seeds, 0.8838095238095236, 0.04354995567685211 -ExtraTree, statlog-australian-credit, 0.5704347826086956, 0.041879795179494514 -ExtraTree, statlog-german-credit, 0.6663999999999999, 0.030692670134740637 -ExtraTree, statlog-heart, 0.7507407407407407, 0.04860326215293292 -ExtraTree, statlog-image, 0.928917748917749, 0.011578447915777758 -ExtraTree, statlog-vehicle, 0.6626481030281935, 0.029721786236711036 -ExtraTree, synthetic-control, 0.8525, 0.03449033681095813 -ExtraTree, tic-tac-toe, 0.8658529668411867, 0.030146489305175026 -ExtraTree, vertebral-column-2clases, 0.747741935483871, 0.06311049392365933 -ExtraTree, wine, 0.8719999999999999, 0.06055135103670992 -ExtraTree, zoo, 0.9346666666666665, 0.06150449350322653 -STree, balance-scale, 0.97056, 0.015046806970251203 -STree, balloons, 0.86, 0.28501461950807594 -STree, breast-cancer-wisc-diag, 0.9727635460332246, 0.017313211324042233 -STree, breast-cancer-wisc-prog, 0.811128205128205, 0.05846009894763935 -STree, breast-cancer-wisc, 0.967808838643371, 0.012084225264206064 -STree, breast-cancer, 0.7342105263157894, 0.047977358056609264 -STree, cardiotocography-10clases, 0.8094129798398231, 0.023346080031830214 -STree, cardiotocography-3clases, 0.904047500690417, 0.015332156149647586 -STree, conn-bench-sonar-mines-rocks, 0.8320905923344947, 0.06095204566527709 -STree, cylinder-bands, 0.7476127926898914, 0.04110007272505122 -STree, dermatology, 0.9729211403184006, 0.019813499795909784 -STree, echocardiogram, 0.855156695156695, 0.06266151037590971 -STree, fertility, 0.88, 0.0547722557505166 -STree, haberman-survival, 0.735637228979376, 0.04346136548997221 -STree, heart-hungarian, 0.8275219170075979, 0.05052827428335672 -STree, hepatitis, 0.8245161290322581, 0.073887165430815 -STree, ilpd-indian-liver, 0.7234983790156204, 0.038488555090414656 -STree, ionosphere, 0.9532756539235413, 0.02385368651558141 -STree, iris, 0.966, 0.032991581417756315 -STree, led-display, 0.7030000000000001, 0.029120439557122058 -STree, libras, 0.7886111111111112, 0.05169130237032862 -STree, low-res-spect, 0.8975365896667254, 0.03121679591619338 -STree, lymphography, 0.8350344827586206, 0.05906491141171708 -STree, mammographic, 0.8267465457685665, 0.022926835290767923 -STree, molec-biol-promoter, 0.827142857142857, 0.09431966640910128 -STree, musk-1, 0.9163881578947368, 0.027520787090975468 -STree, oocytes_merluccius_nucleus_4d, 0.8351252989000477, 0.02209607725442717 -STree, oocytes_merluccius_states_2f, 0.9179062649450025, 0.016316580296653664 -STree, oocytes_trisopterus_nucleus_2f, 0.8009860085269921, 0.0218449453461112 -STree, oocytes_trisopterus_states_5b, 0.9224704257491143, 0.01772207930954525 -STree, parkinsons, 0.882051282051282, 0.04783271309276316 -STree, pima, 0.7692530345471521, 0.02618732989053553 -STree, pittsburg-bridges-MATERIAL, 0.8677489177489177, 0.07122264688853039 -STree, pittsburg-bridges-REL-L, 0.6672857142857144, 0.09562399199889633 -STree, pittsburg-bridges-SPAN, 0.6794736842105265, 0.10621707149419587 -STree, pittsburg-bridges-T-OR-D, 0.8628095238095238, 0.0747571882042698 -STree, planning, 0.7352702702702704, 0.06697755238925641 -STree, post-operative, 0.711111111111111, 0.07535922203472521 -STree, seeds, 0.9528571428571427, 0.0279658035429067 -STree, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211 -STree, statlog-german-credit, 0.7644, 0.028803472012936236 -STree, statlog-heart, 0.8229629629629629, 0.044003990341567836 -STree, statlog-image, 0.9559307359307361, 0.00956073126474503 -STree, statlog-vehicle, 0.7930281935259312, 0.03010396812322711 -STree, synthetic-control, 0.9833333333333334, 0.01092906420717001 -STree, tic-tac-toe, 0.9844442626527051, 0.008387465200358178 -STree, vertebral-column-2clases, 0.8529032258064515, 0.04088510843291576 -STree, wine, 0.9791587301587302, 0.022426953738041516 -STree, zoo, 0.9575238095238094, 0.04546150348723332 -ODTE, balance-scale, 0.97376, 0.019001642034308507 -ODTE, balloons, 0.7583333333333333, 0.2785129161178067 -ODTE, breast-cancer-wisc-diag, 0.9739931687626143, 0.016554866765950735 -ODTE, breast-cancer-wisc-prog, 0.810102564102564, 0.06056448251036948 -ODTE, breast-cancer-wisc, 0.971956834532374, 0.01262594441445573 -ODTE, breast-cancer, 0.7433877797943134, 0.04272794739105822 -ODTE, cardiotocography-10clases, 0.8377754211543773, 0.020035258312140675 -ODTE, cardiotocography-3clases, 0.9158537420602043, 0.013571959275117652 -ODTE, conn-bench-sonar-mines-rocks, 0.8431475029036002, 0.052081786915494255 -ODTE, cylinder-bands, 0.7673272415762422, 0.041472855015354505 -ODTE, dermatology, 0.9805997778600516, 0.012861009889409938 -ODTE, echocardiogram, 0.855156695156695, 0.06266151037590971 -ODTE, fertility, 0.88, 0.0547722557505166 -ODTE, haberman-survival, 0.7408778424114226, 0.05131929321866528 -ODTE, heart-hungarian, 0.8299123319696085, 0.05556757760909393 -ODTE, hepatitis, 0.8283870967741936, 0.07109665710428856 -ODTE, ilpd-indian-liver, 0.7272885352195698, 0.039873431660598986 -ODTE, ionosphere, 0.9504346076458754, 0.023708279806973313 -ODTE, iris, 0.964, 0.03255081497528987 -ODTE, led-display, 0.7186, 0.02703775138579389 -ODTE, libras, 0.8625, 0.047079031268550814 -ODTE, low-res-spect, 0.9093969317580672, 0.035428439542986974 -ODTE, lymphography, 0.8410574712643678, 0.05833785272114171 -ODTE, mammographic, 0.8288347366148531, 0.022955531218077037 -ODTE, molec-biol-promoter, 0.8656709956709955, 0.0973712407439469 -ODTE, musk-1, 0.9124013157894737, 0.02990026230299551 -ODTE, oocytes_merluccius_nucleus_4d, 0.8443271162123385, 0.026958181251392942 -ODTE, oocytes_merluccius_states_2f, 0.9230946915351506, 0.015908353110954166 -ODTE, oocytes_trisopterus_nucleus_2f, 0.8154464661022038, 0.022327974942331383 -ODTE, oocytes_trisopterus_states_5b, 0.9267387257551191, 0.01807476944375977 -ODTE, parkinsons, 0.9066666666666667, 0.04522134909753559 -ODTE, pima, 0.7728910958322721, 0.02900120258111359 -ODTE, pittsburg-bridges-MATERIAL, 0.8677489177489177, 0.06934417716014449 -ODTE, pittsburg-bridges-REL-L, 0.6966190476190477, 0.099521633605742 -ODTE, pittsburg-bridges-SPAN, 0.6937426900584795, 0.10948613227073402 -ODTE, pittsburg-bridges-T-OR-D, 0.8664761904761904, 0.07649172587269897 -ODTE, planning, 0.7352702702702704, 0.06697755238925641 -ODTE, post-operative, 0.711111111111111, 0.07535922203472521 -ODTE, seeds, 0.9571428571428572, 0.027355060221609648 -ODTE, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211 -ODTE, statlog-german-credit, 0.7576, 0.029227384419410526 -ODTE, statlog-heart, 0.8381481481481482, 0.046379185057354784 -ODTE, statlog-image, 0.9641991341991343, 0.007272340744212073 -ODTE, statlog-vehicle, 0.8072182387747998, 0.02404945671905629 -ODTE, synthetic-control, 0.987, 0.009741092797468319 -ODTE, tic-tac-toe, 0.9871591404886563, 0.0074727077754232615 -ODTE, vertebral-column-2clases, 0.8564516129032256, 0.037928954891305953 -ODTE, wine, 0.9814285714285714, 0.02424781771927406 -ODTE, zoo, 0.9574761904761906, 0.04557023434769017 -TBRoF, balance-scale, 0.7826380042462844, 0.18925694969056328 -TBRoF, balloons, 0.61125, 0.247725203364352 -TBRoF, breast-cancer-wisc-diag, 0.973283758495026, 0.013244810623357882 -TBRoF, breast-cancer-wisc-prog, 0.7978151260504203, 0.05694766463433709 -TBRoF, breast-cancer-wisc, 0.9627849210987725, 0.012721293409234216 -TBRoF, breast-cancer, 0.7308990931892726, 0.055966571642361095 -TBRoF, cardiotocography-10clases, 0.7833492331011966, 0.021307069831893954 -TBRoF, cardiotocography-3clases, 0.895985255615268, 0.012308158973203483 -TBRoF, conn-bench-sonar-mines-rocks, 0.7877884615384616, 0.04851951284665777 -TBRoF, cylinder-bands, 0.7170703125, 0.035382373191454965 -TBRoF, dermatology, 0.9742673992673991, 0.015317005568040842 -TBRoF, echocardiogram, 0.8331428571428572, 0.05716070028311154 -TBRoF, fertility, 0.8800000000000006, 0.05570922354457741 -TBRoF, haberman-survival, 0.7412618083670716, 0.03733056862694105 -TBRoF, heart-hungarian, 0.814958904109589, 0.03136114741864325 -TBRoF, hepatitis, 0.8127086007702183, 0.05401110717213335 -TBRoF, ilpd-indian-liver, 0.7232548928238585, 0.02812722020593907 -TBRoF, ionosphere, 0.907727969348659, 0.03151367661074491 -TBRoF, iris, 0.9774601524601525, 0.022227864533454735 -TBRoF, led-display, 0.7098000000000001, 0.026706198767918625 -TBRoF, libras, 0.7562777777777778, 0.04860581848829984 -TBRoF, low-res-spect, 0.8738299663299666, 0.029720937467041728 -TBRoF, lymphography, 0.7751351351351352, 0.06933105447844684 -TBRoF, mammographic, 0.8145772821576763, 0.023864461511594333 -TBRoF, molec-biol-promoter, 0.7795329670329672, 0.06980216133544012 -TBRoF, musk-1, 0.8714285714285717, 0.02544487451716707 -TBRoF, oocytes_merluccius_nucleus_4d, 0.831322194247349, 0.021134901304659903 -TBRoF, oocytes_merluccius_states_2f, 0.9211329823758299, 0.012866634130284658 -TBRoF, oocytes_trisopterus_nucleus_2f, 0.8083333333333332, 0.029987740012144073 -TBRoF, oocytes_trisopterus_states_5b, 0.9314473684210521, 0.015009780777726503 -TBRoF, parkinsons, 0.8935600490196077, 0.04414473458115928 -TBRoF, pima, 0.7682291666666667, 0.02576538959165189 -TBRoF, pittsburg-bridges-MATERIAL, 0.8053021978021977, 0.06860551894291693 -TBRoF, pittsburg-bridges-REL-L, 0.6569285714285714, 0.0689374239513824 -TBRoF, pittsburg-bridges-SPAN, 0.6478260869565218, 0.10917514377689 -TBRoF, pittsburg-bridges-T-OR-D, 0.8656444444444447, 0.0655148575959644 -TBRoF, planning, 0.7128439716312058, 0.05752795189294039 -TBRoF, post-operative, 0.7110227272727273, 0.08718498768060559 -TBRoF, seeds, 0.9534900284900283, 0.027705208350106637 -TBRoF, statlog-australian-credit, 0.6784883720930234, 0.03892040204721194 -TBRoF, statlog-german-credit, 0.7575999999999999, 0.0256092088700016 -TBRoF, statlog-heart, 0.8289746917585983, 0.042595090023633556 -TBRoF, statlog-image, 0.9615544640104406, 0.008699636358413849 -TBRoF, statlog-vehicle, 0.8051545290701557, 0.02813895748743846 -TBRoF, synthetic-control, 0.9816666666666667, 0.012955163345552294 -TBRoF, tic-tac-toe, 0.983303703189291, 0.008126990928514651 -TBRoF, vertebral-column-2clases, 0.8501578168666777, 0.03368363428991826 -TBRoF, wine, 0.9910573122529645, 0.013443915807807105 -TBRoF, zoo, 0.9566153846153845, 0.04584946867346845 -Wodt, balance-scale, 0.92128, 0.031368162203100125 -Wodt, balloons, 0.6849999999999998, 0.23879907872519107 -Wodt, breast-cancer-wisc-diag, 0.9662598975314391, 0.012701511719760309 -Wodt, breast-cancer-wisc-prog, 0.7116025641025641, 0.054983897290113326 -Wodt, breast-cancer-wisc, 0.9440647482014388, 0.01756316459913997 -Wodt, breast-cancer, 0.6632486388384754, 0.04925109121345335 -Wodt, cardiotocography-10clases, 0.7796334714167358, 0.01966283976162998 -Wodt, cardiotocography-3clases, 0.9014120961060482, 0.016029994123889068 -Wodt, conn-bench-sonar-mines-rocks, 0.8081997677119628, 0.0498734154479995 -Wodt, cylinder-bands, 0.7025452122596612, 0.03710659493721448 -Wodt, dermatology, 0.9641873380229546, 0.016641323865204168 -Wodt, echocardiogram, 0.746980056980057, 0.08724923657104314 -Wodt, fertility, 0.804, 0.06770524351924304 -Wodt, haberman-survival, 0.6702379693283977, 0.05088784373091166 -Wodt, heart-hungarian, 0.761928696668615, 0.052100433118643194 -Wodt, hepatitis, 0.7748387096774192, 0.09558190259697842 -Wodt, ilpd-indian-liver, 0.6778588269967581, 0.035631066332769044 -Wodt, ionosphere, 0.8794607645875252, 0.03858080219956032 -Wodt, iris, 0.9466666666666668, 0.036514837167011066 -Wodt, led-display, 0.7047, 0.02916179006851258 -Wodt, libras, 0.7855555555555556, 0.05031996388544112 -Wodt, low-res-spect, 0.855533415623347, 0.03222774975371431 -Wodt, lymphography, 0.7800459770114943, 0.07599005574288029 -Wodt, mammographic, 0.7579566062176167, 0.024906146679797566 -Wodt, molec-biol-promoter, 0.7912987012987013, 0.08633959022510546 -Wodt, musk-1, 0.8411776315789474, 0.03449014306500045 -Wodt, oocytes_merluccius_nucleus_4d, 0.74041893830703, 0.028693112692103676 -Wodt, oocytes_merluccius_states_2f, 0.902934481109517, 0.022147306742109327 -Wodt, oocytes_trisopterus_nucleus_2f, 0.7491436978322225, 0.03602040782195508 -Wodt, oocytes_trisopterus_states_5b, 0.8916705698672911, 0.020702811014803944 -Wodt, parkinsons, 0.9005128205128206, 0.05144589211891057 -Wodt, pima, 0.6881622952211188, 0.035111562938444044 -Wodt, pittsburg-bridges-MATERIAL, 0.8118614718614716, 0.08062163607707493 -Wodt, pittsburg-bridges-REL-L, 0.6259047619047619, 0.10926121729800642 -Wodt, pittsburg-bridges-SPAN, 0.5871929824561403, 0.11611933933595711 -Wodt, pittsburg-bridges-T-OR-D, 0.8375238095238096, 0.07413072965623015 -Wodt, planning, 0.5784384384384385, 0.08440752357130758 -Wodt, post-operative, 0.5566666666666666, 0.10627822791331752 -Wodt, seeds, 0.9323809523809523, 0.04161152814276852 -Wodt, statlog-australian-credit, 0.5617391304347826, 0.03785467296768582 -Wodt, statlog-german-credit, 0.6850999999999999, 0.02579709285946769 -Wodt, statlog-heart, 0.78, 0.061154866490519384 -Wodt, statlog-image, 0.9548917748917749, 0.011022906583856079 -Wodt, statlog-vehicle, 0.7296756004176818, 0.03014045315193517 -Wodt, synthetic-control, 0.9785, 0.01453826368969525 -Wodt, tic-tac-toe, 0.9449798211169285, 0.029347280139619656 -Wodt, vertebral-column-2clases, 0.8138709677419353, 0.04530047675895885 -Wodt, wine, 0.967920634920635, 0.028208046326376822 -Wodt, zoo, 0.9514285714285715, 0.043724036675722826 -RandomForest, balance-scale, 0.8488000000000001, 0.025562472493872744 -RandomForest, balloons, 0.6583333333333333, 0.24958298553119895 -RandomForest, breast-cancer-wisc-diag, 0.9602856699270299, 0.01304850246785924 -RandomForest, breast-cancer-wisc-prog, 0.7889487179487179, 0.062095973522275645 -RandomForest, breast-cancer-wisc, 0.9682384378211717, 0.012979760074633098 -RandomForest, breast-cancer, 0.7173260738052026, 0.05543339031060783 -RandomForest, cardiotocography-10clases, 0.8729086992543498, 0.01716119423915263 -RandomForest, cardiotocography-3clases, 0.9415357083678542, 0.011745284632829643 -RandomForest, conn-bench-sonar-mines-rocks, 0.833437862950058, 0.05787314265134896 -RandomForest, cylinder-bands, 0.792147344374643, 0.03975693903506292 -RandomForest, dermatology, 0.9778563495001852, 0.015194540700548658 -RandomForest, echocardiogram, 0.8292592592592591, 0.07125299167854407 -RandomForest, fertility, 0.887, 0.06067124524847005 -RandomForest, haberman-survival, 0.7084822845055527, 0.05577963957846104 -RandomForest, heart-hungarian, 0.8169783752191703, 0.047084407169907465 -RandomForest, hepatitis, 0.8225806451612903, 0.07162452681024038 -RandomForest, ilpd-indian-liver, 0.7048010610079575, 0.03367965359465586 -RandomForest, ionosphere, 0.9327565392354125, 0.02623998580282412 -RandomForest, iris, 0.9499999999999998, 0.03144660377352201 -RandomForest, led-display, 0.7132000000000001, 0.027545598559479512 -RandomForest, libras, 0.8008333333333334, 0.051039351799359346 -RandomForest, low-res-spect, 0.8941421266090636, 0.033512494391602414 -RandomForest, lymphography, 0.8472873563218392, 0.07011193134668385 -RandomForest, mammographic, 0.8082259283246978, 0.022691310103387825 -RandomForest, molec-biol-promoter, 0.8914718614718615, 0.06923296108787423 -RandomForest, musk-1, 0.8892916666666667, 0.032218163597528444 -RandomForest, oocytes_merluccius_nucleus_4d, 0.8008857006217122, 0.026005202449345177 -RandomForest, oocytes_merluccius_states_2f, 0.9224098517455765, 0.014089731590980637 -RandomForest, oocytes_trisopterus_nucleus_2f, 0.80932324506095, 0.02940827813910382 -RandomForest, oocytes_trisopterus_states_5b, 0.9135879421125322, 0.018448127398280962 -RandomForest, parkinsons, 0.906153846153846, 0.043092182889324544 -RandomForest, pima, 0.7619658772599949, 0.029215416907967737 -RandomForest, pittsburg-bridges-MATERIAL, 0.8393939393939392, 0.06465451660842308 -RandomForest, pittsburg-bridges-REL-L, 0.6780476190476192, 0.08371922895638823 -RandomForest, pittsburg-bridges-SPAN, 0.6433333333333333, 0.1000367396003129 -RandomForest, pittsburg-bridges-T-OR-D, 0.856095238095238, 0.07211284933039848 -RandomForest, planning, 0.7022522522522522, 0.06398440367639394 -RandomForest, post-operative, 0.6344444444444445, 0.08613082211681551 -RandomForest, seeds, 0.9333333333333332, 0.041785544701867246 -RandomForest, statlog-australian-credit, 0.6682608695652174, 0.03491189739487307 -RandomForest, statlog-german-credit, 0.7622999999999999, 0.02486382914999218 -RandomForest, statlog-heart, 0.8307407407407408, 0.044445987627530076 -RandomForest, statlog-image, 0.9785714285714286, 0.007182103906076649 -RandomForest, statlog-vehicle, 0.7494166376609818, 0.02747614895571888 -RandomForest, synthetic-control, 0.9860000000000001, 0.010598742063723111 -RandomForest, tic-tac-toe, 0.9884075043630017, 0.0071140588943835546 -RandomForest, vertebral-column-2clases, 0.8322580645161289, 0.04539112025635577 -RandomForest, wine, 0.9825873015873016, 0.02303907423919074 -RandomForest, zoo, 0.9594761904761906, 0.04292716501935155 -BaggingStree, balance-scale, 0.9743999999999999, 0.01893145530591877 -BaggingStree, balloons, 0.7733333333333333, 0.27235597621087326 -BaggingStree, breast-cancer-wisc-diag, 0.9759276509858718, 0.014451052166715531 -BaggingStree, breast-cancer-wisc-prog, 0.8091410256410256, 0.05525623326760874 -BaggingStree, breast-cancer-wisc, 0.9682353545734841, 0.013372407783798355 -BaggingStree, breast-cancer, 0.7394494857834241, 0.04747649879695565 -BaggingStree, cardiotocography-10clases, 0.8287906103286384, 0.019919444360845064 -BaggingStree, cardiotocography-3clases, 0.9121377520022094, 0.01484624306368222 -BaggingStree, conn-bench-sonar-mines-rocks, 0.8364227642276423, 0.05768447229690234 -BaggingStree, cylinder-bands, 0.7509347039786789, 0.041363431918714964 -BaggingStree, dermatology, 0.983054424287301, 0.01146631014778772 -BaggingStree, echocardiogram, 0.855156695156695, 0.06266151037590971 -BaggingStree, fertility, 0.88, 0.0547722557505166 -BaggingStree, haberman-survival, 0.7359545214172395, 0.047857454758329004 -BaggingStree, heart-hungarian, 0.8299006428988897, 0.050874048012859115 -BaggingStree, hepatitis, 0.8283870967741934, 0.06780028209248852 -BaggingStree, ilpd-indian-liver, 0.7269378131447097, 0.03708761542948765 -BaggingStree, ionosphere, 0.9510100603621732, 0.02418916928619684 -BaggingStree, iris, 0.9653333333333335, 0.032632635334721105 -BaggingStree, led-display, 0.7218000000000001, 0.027124896313165885 -BaggingStree, libras, 0.8544444444444445, 0.044724810139063854 -BaggingStree, low-res-spect, 0.910904602362899, 0.034998937080933226 -BaggingStree, lymphography, 0.8511724137931036, 0.060541983349731314 -BaggingStree, mammographic, 0.8272695379965458, 0.02450569044585343 -BaggingStree, molec-biol-promoter, 0.8387012987012987, 0.07620183897953593 -BaggingStree, musk-1, 0.9128135964912282, 0.029764363025043043 -BaggingStree, oocytes_merluccius_nucleus_4d, 0.8454954567192731, 0.02495631167694985 -BaggingStree, oocytes_merluccius_states_2f, 0.9220148254423719, 0.01653121938374588 -BaggingStree, oocytes_trisopterus_nucleus_2f, 0.8103038491563082, 0.020971220779186443 -BaggingStree, oocytes_trisopterus_states_5b, 0.9239998799015193, 0.01817568579552671 -BaggingStree, parkinsons, 0.901025641025641, 0.04865312826739729 -BaggingStree, pima, 0.7726398438163143, 0.028486201404109596 -BaggingStree, pittsburg-bridges-MATERIAL, 0.8658874458874459, 0.07136421080457399 -BaggingStree, pittsburg-bridges-REL-L, 0.6818095238095239, 0.09893968478062681 -BaggingStree, pittsburg-bridges-SPAN, 0.7003508771929825, 0.10417934569394204 -BaggingStree, pittsburg-bridges-T-OR-D, 0.8628095238095238, 0.0747571882042698 -BaggingStree, planning, 0.7352702702702704, 0.06697755238925641 -BaggingStree, post-operative, 0.711111111111111, 0.07535922203472521 -BaggingStree, seeds, 0.9576190476190476, 0.028686277108245766 -BaggingStree, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211 -BaggingStree, statlog-german-credit, 0.7656000000000001, 0.028716545753276115 -BaggingStree, statlog-heart, 0.8392592592592593, 0.04262317742463941 -BaggingStree, statlog-image, 0.9613419913419912, 0.007744079362039781 -BaggingStree, statlog-vehicle, 0.8039122868082146, 0.02550795916537555 -BaggingStree, synthetic-control, 0.9868333333333333, 0.009441339358845712 -BaggingStree, tic-tac-toe, 0.9862183682373472, 0.007729402310947018 -BaggingStree, vertebral-column-2clases, 0.8574193548387095, 0.03942352546439275 -BaggingStree, wine, 0.9791746031746031, 0.025059546117196532 -BaggingStree, zoo, 0.9664285714285715, 0.04138270586454204 -SVC, balance-scale, 0.9785600000000001, 0.013711542582802286 -SVC, balloons, 0.86, 0.28501461950807594 -SVC, breast-cancer-wisc-diag, 0.9753951249805931, 0.014597293231205878 -SVC, breast-cancer-wisc-prog, 0.7938589743589745, 0.05657320433780183 -SVC, breast-cancer-wisc, 0.9679527235354572, 0.011995201821390402 -SVC, breast-cancer, 0.7317543859649124, 0.04356606121838112 -SVC, cardiotocography-10clases, 0.8201826014913007, 0.022919732876958584 -SVC, cardiotocography-3clases, 0.9112899199116268, 0.012401790692530392 -SVC, conn-bench-sonar-mines-rocks, 0.8320905923344947, 0.06095204566527709 -SVC, cylinder-bands, 0.7476127926898914, 0.04110007272505122 -SVC, dermatology, 0.972373195112921, 0.015862520651510115 -SVC, echocardiogram, 0.8252991452991455, 0.07337261558656372 -SVC, fertility, 0.88, 0.0547722557505166 -SVC, haberman-survival, 0.7317133791644632, 0.045381650232207534 -SVC, heart-hungarian, 0.8234482758620689, 0.048867109055436626 -SVC, hepatitis, 0.8251612903225807, 0.07040539663356832 -SVC, ilpd-indian-liver, 0.7133952254641912, 0.03827755623741007 -SVC, ionosphere, 0.9532756539235413, 0.02385368651558141 -SVC, iris, 0.966, 0.032991581417756315 -SVC, led-display, 0.7204, 0.02523568901377571 -SVC, libras, 0.7847222222222221, 0.051012132422797105 -SVC, low-res-spect, 0.9016769529183564, 0.03269429070378195 -SVC, lymphography, 0.8471494252873564, 0.05938135729224899 -SVC, mammographic, 0.8267465457685665, 0.022926835290767923 -SVC, molec-biol-promoter, 0.827142857142857, 0.09431966640910128 -SVC, musk-1, 0.9163881578947368, 0.027520787090975468 -SVC, oocytes_merluccius_nucleus_4d, 0.8403060736489717, 0.023610853521046887 -SVC, oocytes_merluccius_states_2f, 0.9197675753228122, 0.016293604623296522 -SVC, oocytes_trisopterus_nucleus_2f, 0.7991268840449167, 0.026483084828854114 -SVC, oocytes_trisopterus_states_5b, 0.9222464420825075, 0.018202901471558224 -SVC, parkinsons, 0.8743589743589744, 0.04565254824298682 -SVC, pima, 0.7695127748068923, 0.02622048017958126 -SVC, pittsburg-bridges-MATERIAL, 0.8629004329004327, 0.06975826134555882 -SVC, pittsburg-bridges-REL-L, 0.6901904761904761, 0.0893877385352908 -SVC, pittsburg-bridges-SPAN, 0.7094736842105265, 0.10829259913995691 -SVC, pittsburg-bridges-T-OR-D, 0.8648095238095237, 0.07364850331687892 -SVC, planning, 0.7352702702702704, 0.06697755238925641 -SVC, post-operative, 0.7022222222222222, 0.0751623756679456 -SVC, seeds, 0.9328571428571428, 0.04059925269913093 -SVC, statlog-australian-credit, 0.6782608695652174, 0.03904983647915211 -SVC, statlog-german-credit, 0.7663999999999999, 0.027166891614610618 -SVC, statlog-heart, 0.8303703703703706, 0.040801301789393805 -SVC, statlog-image, 0.9554545454545453, 0.00732216243366479 -SVC, statlog-vehicle, 0.797883745214062, 0.028612590720497322 -SVC, synthetic-control, 0.9913333333333334, 0.008326663997864544 -SVC, tic-tac-toe, 0.9863236256544502, 0.007669725164798296 -SVC, vertebral-column-2clases, 0.8461290322580644, 0.03927146112240879 -SVC, wine, 0.9807936507936508, 0.02154056565547604 -SVC, zoo, 0.9366666666666668, 0.05165294968232069 -BaggingWodt, balance-scale, 0.8275199999999999, 0.031805810789854096 -BaggingWodt, balloons, 0.63, 0.24333333333333332 -BaggingWodt, breast-cancer-wisc-diag, 0.9767955286446203, 0.012651131794984151 -BaggingWodt, breast-cancer-wisc-prog, 0.8041282051282052, 0.054065511677276364 -BaggingWodt, breast-cancer-wisc, 0.9688067831449128, 0.013501536929852434 -BaggingWodt, breast-cancer, 0.7372958257713248, 0.05019104122389415 -BaggingWodt, cardiotocography-10clases, 0.8420058547362607, 0.02038235146222122 -BaggingWodt, cardiotocography-3clases, 0.9252600938967136, 0.013290240551130103 -BaggingWodt, conn-bench-sonar-mines-rocks, 0.8512891986062716, 0.05425167048127118 -BaggingWodt, cylinder-bands, 0.7726308775937559, 0.043323936964141746 -BaggingWodt, dermatology, 0.9696445760829323, 0.0188524896065306 -BaggingWodt, echocardiogram, 0.7925925925925926, 0.06690912153964222 -BaggingWodt, fertility, 0.875, 0.05852349955359812 -BaggingWodt, haberman-survival, 0.7340137493389741, 0.05579848014720707 -BaggingWodt, heart-hungarian, 0.8210461718293396, 0.05105327558392002 -BaggingWodt, hepatitis, 0.8309677419354838, 0.07456010432616086 -BaggingWodt, ilpd-indian-liver, 0.7071986442676098, 0.03516496095647541 -BaggingWodt, ionosphere, 0.9122696177062375, 0.03896221222978386 -BaggingWodt, iris, 0.9546666666666666, 0.031098410105841596 -BaggingWodt, led-display, 0.7189, 0.027116231301565492 -BaggingWodt, libras, 0.8352777777777779, 0.04347998688200422 -BaggingWodt, low-res-spect, 0.8926309292893672, 0.028538049916854567 -BaggingWodt, lymphography, 0.8715862068965516, 0.05720076110461543 -BaggingWodt, mammographic, 0.8298747841105353, 0.022169546290874684 -BaggingWodt, molec-biol-promoter, 0.8264069264069264, 0.08838486909314487 -BaggingWodt, musk-1, 0.8932872807017544, 0.031704400918302934 -BaggingWodt, oocytes_merluccius_nucleus_4d, 0.8056791009086561, 0.019752083021678597 -BaggingWodt, oocytes_merluccius_states_2f, 0.9211276901004304, 0.01605560855667252 -BaggingWodt, oocytes_trisopterus_nucleus_2f, 0.812938209331652, 0.027686364424624348 -BaggingWodt, oocytes_trisopterus_states_5b, 0.9255353389779618, 0.01698481765795473 -BaggingWodt, parkinsons, 0.9307692307692308, 0.032936493791449035 -BaggingWodt, pima, 0.7510202869026398, 0.03359040798873941 -BaggingWodt, pittsburg-bridges-MATERIAL, 0.8478354978354977, 0.0664135437533315 -BaggingWodt, pittsburg-bridges-REL-L, 0.6874761904761907, 0.08128337095961154 -BaggingWodt, pittsburg-bridges-SPAN, 0.6784795321637428, 0.11377081388352872 -BaggingWodt, pittsburg-bridges-T-OR-D, 0.8570952380952381, 0.07769860077477762 -BaggingWodt, planning, 0.6827927927927929, 0.06607719671456548 -BaggingWodt, post-operative, 0.6455555555555555, 0.07608474807008883 -BaggingWodt, seeds, 0.9371428571428573, 0.04384056871041486 -BaggingWodt, statlog-australian-credit, 0.6365217391304349, 0.03381380252080168 -BaggingWodt, statlog-german-credit, 0.7661, 0.02579321616239434 -BaggingWodt, statlog-heart, 0.8407407407407408, 0.04287347001033417 -BaggingWodt, statlog-image, 0.9749350649350649, 0.0077450472812213905 -BaggingWodt, statlog-vehicle, 0.764779672815872, 0.02887187995824511 -BaggingWodt, synthetic-control, 0.9886666666666667, 0.009539392014169472 -BaggingWodt, tic-tac-toe, 0.9527105148342061, 0.020956068837478 -BaggingWodt, vertebral-column-2clases, 0.8429032258064517, 0.04325588746295833 -BaggingWodt, wine, 0.9831111111111112, 0.020289564182456803 -BaggingWodt, zoo, 0.9574761904761906, 0.038427155222620905 -Cart, balance-scale, 0.7820799999999999, 0.03608203985364464 -Cart, balloons, 0.6833333333333335, 0.26977356760397747 -Cart, breast-cancer-wisc-diag, 0.9239217512808571, 0.02323176886368342 -Cart, breast-cancer-wisc-prog, 0.6914615384615383, 0.07229069176397018 -Cart, breast-cancer-wisc, 0.9433556012332991, 0.020857147642906682 -Cart, breast-cancer, 0.6362613430127042, 0.05493193657305148 -Cart, cardiotocography-10clases, 0.8109128969897819, 0.019011886406374308 -Cart, cardiotocography-3clases, 0.9204613090306545, 0.013845817718125228 -Cart, conn-bench-sonar-mines-rocks, 0.7253890824622532, 0.06929084790257113 -Cart, cylinder-bands, 0.712632781267847, 0.046238358140227635 -Cart, dermatology, 0.9396038504257683, 0.02387579095888003 -Cart, echocardiogram, 0.7443589743589744, 0.08299708625595158 -Cart, fertility, 0.7989999999999999, 0.08395832299420945 -Cart, haberman-survival, 0.6404970914859863, 0.055117499856839475 -Cart, heart-hungarian, 0.750613676212741, 0.04493113662535727 -Cart, hepatitis, 0.7658064516129032, 0.07293747411360528 -Cart, ilpd-indian-liver, 0.6639640436192161, 0.03825985300800366 -Cart, ionosphere, 0.8757907444668007, 0.03797437861573896 -Cart, iris, 0.9386666666666668, 0.04389887368841153 -Cart, led-display, 0.7037, 0.029644729717101474 -Cart, libras, 0.6549999999999998, 0.059210338603413376 -Cart, low-res-spect, 0.8352036677834599, 0.030503013555880365 -Cart, lymphography, 0.7683218390804598, 0.07551790968755526 -Cart, mammographic, 0.7569187176165802, 0.020852411303147095 -Cart, molec-biol-promoter, 0.7158008658008658, 0.08811757165025375 -Cart, musk-1, 0.7768969298245614, 0.043771936691364643 -Cart, oocytes_merluccius_nucleus_4d, 0.7195667144906744, 0.03068883042942922 -Cart, oocytes_merluccius_states_2f, 0.8911927307508368, 0.02477313298773685 -Cart, oocytes_trisopterus_nucleus_2f, 0.7257617246141836, 0.029837690053299604 -Cart, oocytes_trisopterus_states_5b, 0.8702630156728517, 0.025916263242401694 -Cart, parkinsons, 0.8558974358974359, 0.05822037527622443 -Cart, pima, 0.7011722264663441, 0.030849394257539655 -Cart, pittsburg-bridges-MATERIAL, 0.8006493506493506, 0.07641918889128255 -Cart, pittsburg-bridges-REL-L, 0.6171904761904762, 0.10161093599079367 -Cart, pittsburg-bridges-SPAN, 0.5575438596491228, 0.09592147935027655 -Cart, pittsburg-bridges-T-OR-D, 0.8225238095238094, 0.08877499647986477 -Cart, planning, 0.573963963963964, 0.07834935548163284 -Cart, post-operative, 0.5822222222222222, 0.10554970744033704 -Cart, seeds, 0.9161904761904762, 0.04238897730336739 -Cart, statlog-australian-credit, 0.5720289855072465, 0.04059031952408632 -Cart, statlog-german-credit, 0.6897, 0.02526677660486194 -Cart, statlog-heart, 0.7344444444444446, 0.04693777803495507 -Cart, statlog-image, 0.9613419913419914, 0.00934513626156175 -Cart, statlog-vehicle, 0.706384267316394, 0.030533108686824795 -Cart, synthetic-control, 0.9023333333333332, 0.02511307760244982 -Cart, tic-tac-toe, 0.9522851221640489, 0.018529859619663462 -Cart, vertebral-column-2clases, 0.7996774193548387, 0.04231712199258938 -Cart, wine, 0.9015873015873015, 0.0526003102936011 -Cart, zoo, 0.9555714285714286, 0.044427856881917915 -Hedge, balance-scale, 0.9268799999999999, 0.06136308787069482 -Hedge, balloons, 0.6233333333333332, 0.39234126984126966 -Hedge, breast-cancer, 0.7171203871748335, 0.3616679483449606 -Hedge, breast-cancer-wisc, 0.9672425488180886, 0.05157690357564579 -Hedge, breast-cancer-wisc-diag, 0.9346141903431143, 0.07167217360373247 -Hedge, breast-cancer-wisc-prog, 0.726897435897436, 0.30182419575091546 -Hedge, cardiotocography-10clases, 0.8224853907760284, 0.04244383988028859 -Hedge, cardiotocography-3clases, 0.9249283623308479, 0.0602439508982402 -Hedge, conn-bench-sonar-mines-rocks, 0.7111962833914053, 0.29348159104983085 -Hedge, cylinder-bands, 0.7147991623833998, 0.29943308455075124 -Hedge, dermatology, 0.9518918918918922, 0.021132385529569152 -Hedge, echocardiogram, 0.803732193732194, 0.23184391193739048 -Hedge, fertility, 0.8759999999999999, 0.1962085382961812 -Hedge, haberman-survival, 0.7156848228450555, 0.3635029471291943 -Hedge, heart-hungarian, 0.7867971946230274, 0.2657725912072866 -Hedge, hepatitis, 0.7625806451612901, 0.2589931331325924 -Hedge, ilpd-indian-liver, 0.688022399056882, 0.37862669572022073 -Hedge, ionosphere, 0.89456338028169, 0.11778274288375964 -Hedge, iris, 0.9393333333333334, 0.04973986434193759 -Hedge, led-display, 0.7229000000000001, 0.08588914246771266 -Hedge, libras, 0.6302777777777776, 0.05165805396141651 -Hedge, low-res-spect, 0.8227790513137014, 0.041615114646617435 -Hedge, lymphography, 0.748183908045977, 0.13997135664246718 -Hedge, mammographic, 0.8204010146804837, 0.2508836401655701 -Hedge, molec-biol-promoter, 0.7337229437229437, 0.27165754676111714 -Hedge, musk-1, 0.7899144736842106, 0.21840699019515383 -Hedge, oocytes_merluccius_nucleus_4d, 0.733264945002391, 0.30522643614918554 -Hedge, oocytes_merluccius_states_2f, 0.8992137733142032, 0.07844729159018868 -Hedge, oocytes_trisopterus_nucleus_2f, 0.7316975920254607, 0.3022908829038656 -Hedge, oocytes_trisopterus_states_5b, 0.8871872935807361, 0.08801089558713797 -Hedge, parkinsons, 0.8446153846153851, 0.1624820395549004 -Hedge, pima, 0.7442789236906883, 0.3262591809372487 -Hedge, pittsburg-bridges-MATERIAL, 0.8503463203463203, 0.1423410345127055 -Hedge, pittsburg-bridges-REL-L, 0.6393809523809524, 0.2928002682234352 -Hedge, pittsburg-bridges-SPAN, 0.6292982456140352, 0.30123005266829067 -Hedge, pittsburg-bridges-T-OR-D, 0.8336190476190474, 0.2180498486963409 -Hedge, planning, 0.7144144144144144, 0.4080436981633393 -Hedge, post-operative, 0.6911111111111109, 0.2864134132585191 -Hedge, seeds, 0.8942857142857144, 0.07952081943896047 -Hedge, statlog-australian-credit, 0.6427536231884058, 0.41046196215994185 -Hedge, statlog-german-credit, 0.7159, 0.32651908108216554 -Hedge, statlog-heart, 0.7948148148148146, 0.2531165005844386 -Hedge, statlog-image, 0.9604761904761905, 0.012960080161368716 -Hedge, statlog-vehicle, 0.7284852071005916, 0.148665983001905 -Hedge, synthetic-control, 0.8980000000000001, 0.03526071746856307 -Hedge, tic-tac-toe, 0.9832989746945902, 0.0324288588201445 -Hedge, vertebral-column-2clases, 0.8470967741935483, 0.19925244798629155 -Hedge, wine, 0.9791587301587302, 0.016852769344692316 -Hedge, zoo, 0.9298571428571428, 0.022397494870964197 -TBRRoF, balance-scale, 0.901701780173118, 0.022727898539488985 -TBRRoF, balloons, 0.64875, 0.23921130353453926 -TBRRoF, breast-cancer-wisc-diag, 0.9734979808923474, 0.01211458933120479 -TBRRoF, breast-cancer-wisc-prog, 0.8079471788715488, 0.05471208977777354 -TBRRoF, breast-cancer-wisc, 0.9684692187804401, 0.011827258213964388 -TBRRoF, breast-cancer, 0.7354775226702683, 0.04827367740143163 -TBRRoF, cardiotocography-10clases, 0.8288138243181649, 0.01645252887633946 -TBRRoF, cardiotocography-3clases, 0.9224819890962925, 0.011610247810599902 -TBRRoF, conn-bench-sonar-mines-rocks, 0.834423076923077, 0.05150748510916756 -TBRRoF, cylinder-bands, 0.767890625, 0.03324347930143141 -TBRRoF, dermatology, 0.9746077041238331, 0.014736301000625167 -TBRRoF, echocardiogram, 0.8499598214285714, 0.05162023787508073 -TBRRoF, fertility, 0.8810000000000003, 0.060740820180344164 -TBRRoF, haberman-survival, 0.7374089068825913, 0.044260313726551134 -TBRRoF, heart-hungarian, 0.8190036529680362, 0.03888797653544859 -TBRRoF, hepatitis, 0.8227984595635432, 0.0532198072811453 -TBRRoF, ilpd-indian-liver, 0.714239981360671, 0.033670460871576914 -TBRRoF, ionosphere, 0.9459827586206895, 0.02184731993247911 -TBRRoF, iris, 0.9658281358281358, 0.02676690588381821 -TBRRoF, led-display, 0.68436, 0.04995546157031798 -TBRRoF, libras, 0.8801666666666665, 0.03679035193712203 -TBRRoF, low-res-spect, 0.8999957912457909, 0.026355170969855124 -TBRRoF, lymphography, 0.8536486486486488, 0.05135724820856693 -TBRRoF, mammographic, 0.8280522130013831, 0.020736800209974978 -TBRRoF, molec-biol-promoter, 0.8059340659340658, 0.07345265473126247 -TBRRoF, musk-1, 0.8918907563025209, 0.028415223920829437 -TBRRoF, oocytes_merluccius_nucleus_4d, 0.8380727855344474, 0.021149025869688552 -TBRRoF, oocytes_merluccius_states_2f, 0.9260048065918971, 0.01392436830249517 -TBRRoF, oocytes_trisopterus_nucleus_2f, 0.8335307017543863, 0.025458035585743156 -TBRRoF, oocytes_trisopterus_states_5b, 0.931666666666666, 0.01590439823771617 -TBRRoF, parkinsons, 0.9318872549019607, 0.03330032511327817 -TBRRoF, pima, 0.768046875, 0.028059285918720294 -TBRRoF, pittsburg-bridges-MATERIAL, 0.8622252747252745, 0.05272285056647456 -TBRRoF, pittsburg-bridges-REL-L, 0.6864928571428571, 0.08623338832389287 -TBRRoF, pittsburg-bridges-SPAN, 0.6932608695652175, 0.08145095608868286 -TBRRoF, pittsburg-bridges-T-OR-D, 0.8713629629629633, 0.06055900326616563 -TBRRoF, planning, 0.7224751773049648, 0.05837993833973262 -TBRRoF, post-operative, 0.7113068181818182, 0.08162598674627758 -TBRRoF, seeds, 0.9474358974358972, 0.027630596849567368 -TBRRoF, statlog-australian-credit, 0.6782665062817429, 0.029157752633879357 -TBRRoF, statlog-german-credit, 0.7554400000000001, 0.024236821524035296 -TBRRoF, statlog-heart, 0.8349664719878868, 0.0410218444181296 -TBRRoF, statlog-image, 0.9756969824863881, 0.006903978139917843 -TBRRoF, statlog-vehicle, 0.7870681752441981, 0.026493453010463675 -TBRRoF, synthetic-control, 0.9910666666666664, 0.00849185901074688 -TBRRoF, tic-tac-toe, 0.9844217781558706, 0.008102161082608183 -TBRRoF, vertebral-column-2clases, 0.8515140555646883, 0.03497531619650492 -TBRRoF, wine, 0.9804743083003953, 0.020476239593149058 -TBRRoF, zoo, 0.9492615384615388, 0.04884462984878655 -TBRaF, balance-scale, 0.8975269475747183, 0.023461084398242734 -TBRaF, balloons, 0.59875, 0.23763055126492547 -TBRaF, breast-cancer-wisc-diag, 0.9693809711415347, 0.012888038813231393 -TBRaF, breast-cancer-wisc-prog, 0.8002541016406561, 0.05869323051922061 -TBRaF, breast-cancer-wisc, 0.9685330216247806, 0.011892547172713566 -TBRaF, breast-cancer, 0.732153193131391, 0.04414297240873954 -TBRaF, cardiotocography-10clases, 0.8268112132229536, 0.016587713750123762 -TBRaF, cardiotocography-3clases, 0.9193786017390813, 0.011408461529019366 -TBRaF, conn-bench-sonar-mines-rocks, 0.8092307692307693, 0.052101905394016206 -TBRaF, cylinder-bands, 0.748671875, 0.037879462930709165 -TBRaF, dermatology, 0.974489542715349, 0.015244381530713037 -TBRaF, echocardiogram, 0.8456473214285714, 0.06257980864237837 -TBRaF, fertility, 0.8804000000000003, 0.05947711861036103 -TBRaF, haberman-survival, 0.7328744939271254, 0.04650268909435261 -TBRaF, heart-hungarian, 0.819897716894977, 0.040808946053684174 -TBRaF, hepatitis, 0.8162130937098846, 0.057721855658188784 -TBRaF, ilpd-indian-liver, 0.7113035880708294, 0.03241044043427038 -TBRaF, ionosphere, 0.932176245210728, 0.027745840941158267 -TBRaF, iris, 0.9624047124047124, 0.027672604537966147 -TBRaF, led-display, 0.7121600000000001, 0.025910057029585667 -TBRaF, libras, 0.8463888888888887, 0.03918389343605342 -TBRaF, low-res-spect, 0.9000336700336697, 0.023601420494052557 -TBRaF, lymphography, 0.8422972972972973, 0.0589135421754256 -TBRaF, mammographic, 0.827950899031812, 0.021234429745443983 -TBRaF, molec-biol-promoter, 0.7957554945054945, 0.07181386667033816 -TBRaF, musk-1, 0.8739495798319328, 0.0282061820436449 -TBRaF, oocytes_merluccius_nucleus_4d, 0.8366516365300984, 0.02109662119057668 -TBRaF, oocytes_merluccius_states_2f, 0.9237768368047604, 0.015521407216536862 -TBRaF, oocytes_trisopterus_nucleus_2f, 0.8296710526315794, 0.02406624812234792 -TBRaF, oocytes_trisopterus_states_5b, 0.9273903508771925, 0.016976508458830007 -TBRaF, parkinsons, 0.9110845588235295, 0.03900626598564447 -TBRaF, pima, 0.7593229166666666, 0.028362314210191343 -TBRaF, pittsburg-bridges-MATERIAL, 0.8643956043956043, 0.0575076392003003 -TBRaF, pittsburg-bridges-REL-L, 0.6964571428571429, 0.08540119415248532 -TBRaF, pittsburg-bridges-SPAN, 0.6832608695652175, 0.08456145562451363 -TBRaF, pittsburg-bridges-T-OR-D, 0.8654074074074076, 0.059112371460567796 -TBRaF, planning, 0.7097068557919625, 0.055516738956114635 -TBRaF, post-operative, 0.7067613636363635, 0.08307013468275573 -TBRaF, seeds, 0.9433297720797718, 0.03028629022052037 -TBRaF, statlog-australian-credit, 0.6782645014701952, 0.030260627344985316 -TBRaF, statlog-german-credit, 0.74954, 0.026938015580533888 -TBRaF, statlog-heart, 0.8338795154661477, 0.03857659315818731 -TBRaF, statlog-image, 0.9680695515784999, 0.007978763699442637 -TBRaF, statlog-vehicle, 0.7753996172930158, 0.02537062422962424 -TBRaF, synthetic-control, 0.9886666666666664, 0.008239844094951121 -TBRaF, tic-tac-toe, 0.9759070469973432, 0.012840130632245466 -TBRaF, vertebral-column-2clases, 0.8492429722176555, 0.03473914078871642 -TBRaF, wine, 0.9804347826086953, 0.019004456684509077 -TBRaF, zoo, 0.9336076923076927, 0.058309997782000134 diff --git a/results/exreport_accuracy.txt b/results/exreport_accuracy.txt deleted file mode 100644 index a988153..0000000 --- a/results/exreport_accuracy.txt +++ /dev/null @@ -1,39 +0,0 @@ ---------------------------------------------------------------------- -Friedman test, objetive maximize output variable accuracy. Obtained p-value: 3.5365e-68 -Chi squared with 12 degrees of freedom statistic: 352.8361 -Test rejected: p-value: 3.5365e-68 < 0.0500 ---------------------------------------------------------------------- -Control post hoc test for output accuracy -Adjust method: Holm - -Control method: ODTE -p-values: - BaggingStree 0.4919 - TBRRoF 0.2100 - SVC 0.0072 - BaggingWodt 0.0068 - STree 0.0052 - TBRaF 0.0022 - RandomForest 0.0002 - TBRoF 0.0000 - Hedge 0.0000 - Wodt 0.0000 - Cart 0.0000 - ExtraTree 0.0000 ---------------------------------------------------------------------- -$testMultiple - classifier pvalue rank win tie loss -ODTE ODTE NA 3.091837 NA NA NA -BaggingStree BaggingStree 4.918524e-01 3.632653 27 5 17 -TBRRoF TBRRoF 2.099730e-01 4.367347 34 0 15 -SVC SVC 7.221246e-03 5.479592 35 3 11 -BaggingWodt BaggingWodt 6.791738e-03 5.561224 33 1 15 -STree STree 5.168361e-03 5.673469 36 6 7 -TBRaF TBRaF 2.170524e-03 5.897959 39 0 10 -RandomForest RandomForest 2.461390e-04 6.346939 38 0 11 -TBRoF TBRoF 5.434435e-06 7.000000 43 0 6 -Hedge Hedge 3.112186e-16 9.724490 47 0 2 -Wodt Wodt 2.308557e-19 10.367347 49 0 0 -Cart Cart 4.521871e-25 11.408163 48 0 1 -ExtraTree ExtraTree 1.549559e-31 12.448980 49 0 0 - diff --git a/src/pair_check.py b/src/pair_check.py old mode 100644 new mode 100755 index 7878d7b..f208e2e --- a/src/pair_check.py +++ b/src/pair_check.py @@ -1,8 +1,8 @@ #!/usr/bin/env python import argparse -from Experiments import Experiment, Datasets -from Results import Report -from Utils import EnvDefault +import os +from Results import Summary, StubReport +from Utils import EnvDefault, Folders, BEST_ACCURACY_STREE """Check best results of two models giving scores and win-tie-loose results """ @@ -36,7 +36,8 @@ def parse_arguments(): args = ap.parse_args() return ( args.score, - args.model1 < args.model2, + args.model1, + args.model2, ) @@ -45,3 +46,32 @@ def parse_arguments(): model1, model2, ) = parse_arguments() + +summary = Summary() +summary.acquire() +win = tie = loose = 0 +best_1 = summary.best_result(criterion="model", value=model1, score=score) +best_2 = summary.best_result(criterion="model", value=model2, score=score) +report_1 = StubReport(os.path.join(Folders.results, best_1["file"])) +report_1.report() +report_2 = StubReport(os.path.join(Folders.results, best_2["file"])) +report_2.report() +for result1, result2 in zip(report_1.lines, report_2.lines): + result = result1["score"] - result2["score"] + if result > 0: + win += 1 + elif result < 0: + loose += 1 + else: + tie += 1 +print(f"{'Model':<20} {'File':<70} {'Score':<10} Win Tie Loose") +print("=" * 20 + " " + "=" * 70 + " " + "=" * 10 + " === === =====") +print( + f"{model1:<20} {best_1['file']:<70} " + f"{report_1.accuracy / BEST_ACCURACY_STREE:10.5f}" +) +print( + f"{model2:<20} {best_2['file']:<70} " + f"{report_2.accuracy / BEST_ACCURACY_STREE:10.5f} " + f"{win:3d} {tie:3d} {loose:5d}" +) From 15b46fcc21a53651d0a35f0f8ec35fefdcdcc057 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= Date: Wed, 2 Mar 2022 12:06:03 +0100 Subject: [PATCH 2/4] Benchmark only with best experiments results --- src/Results.py | 279 +++++++++++++++++++++++++++------------------- src/benchmark.py | 13 ++- src/pair_check.py | 9 +- 3 files changed, 172 insertions(+), 129 deletions(-) diff --git a/src/Results.py b/src/Results.py index 768cc5b..52564f4 100644 --- a/src/Results.py +++ b/src/Results.py @@ -5,7 +5,6 @@ import json import abc import shutil import subprocess -from tqdm import tqdm import xlsxwriter from Experiments import Datasets, BestResults from Utils import Folders, Files, Symbols, BEST_ACCURACY_STREE, TextColor @@ -456,49 +455,56 @@ class SQL(BaseReport): class Benchmark: - @staticmethod - def get_result_file_name(score): - return os.path.join(Folders.results, Files.exreport(score)) + def __init__(self, score): + self._score = score + self._results = [] + self._models = [] + self._report = {} + self._datasets = set() - @staticmethod - def _process_dataset(results, data): - model = data["model"] - for record in data["results"]: - dataset = record["dataset"] - if (model, dataset) in results: - if record["score"] > results[model, dataset][0]: - results[model, dataset] = ( - record["score"], - record["score_std"], - ) - else: - results[model, dataset] = ( - record["score"], - record["score_std"], + def get_result_file_name(self): + return os.path.join(Folders.results, Files.exreport(self._score)) + + def compile_results(self): + summary = Summary() + summary.acquire(given_score=self._score) + self._models = summary.get_models() + for model in self._models: + best = summary.best_result( + criterion="model", value=model, score=self._score + ) + file_name = os.path.join(Folders.results, best["file"]) + with open(file_name) as fi: + experiment = json.load(fi) + for result in experiment["results"]: + dataset = result["dataset"] + record = { + "model": model, + "dataset": dataset, + "score": result["score"], + "score_std": result["score_std"], + "file_name": file_name, + } + self._results.append(record) + if model not in self._report: + self._report[model] = {} + self._report[model][dataset] = record + self._datasets.add(dataset) + self._datasets = sorted(self._datasets) + + def save_results(self): + # build Files.exreport + result_file_name = self.get_result_file_name() + with open(result_file_name, "w") as f: + f.write(f"classifier, dataset, {self._score}, stdev, file_name\n") + for record in self._results: + f.write( + f"{record['model']}, {record['dataset']}, " + f"{record['score']}, {record['score_std']}, " + f"{record['file_name']}\n" ) - @staticmethod - def compile_results(score): - # build Files.exreport - result_file_name = Benchmark.get_result_file_name(score) - results = {} - init_suffix, end_suffix = Files.results_suffixes(score=score) - all_files = list(os.walk(Folders.results)) - for root, _, files in tqdm(all_files, desc="files"): - for name in files: - if name.startswith(init_suffix) and name.endswith(end_suffix): - file_name = os.path.join(root, name) - with open(file_name) as fp: - data = json.load(fp) - Benchmark._process_dataset(results, data) - - with open(result_file_name, "w") as f: - f.write(f"classifier, dataset, {score}, stdev\n") - for (model, dataset), (accuracy, stdev) in results.items(): - f.write(f"{model}, {dataset}, {accuracy}, {stdev}\n") - - @staticmethod - def exreport(score): + def exreport(self): def end_message(message, file): length = 100 print("*" * length) @@ -515,74 +521,67 @@ class Benchmark: os.remove(Files.exreport_pdf) except FileNotFoundError: pass - except OSError as e: - print("Error: %s : %s" % (Folders.report, e.strerror)) + except OSError as os_error: + print("Error: %s : %s" % (Folders.report, os_error.strerror)) # Compute Friedman & Holm Tests fout = open( - os.path.join(Folders.results, Files.exreport_output(score)), "w" + os.path.join(Folders.results, Files.exreport_output(self._score)), + "w", ) ferr = open( - os.path.join(Folders.results, Files.exreport_err(score)), "w" + os.path.join(Folders.results, Files.exreport_err(self._score)), "w" ) result = subprocess.run( - ["Rscript", os.path.join(Folders.src, Files.benchmark_r), score], + [ + "Rscript", + os.path.join(Folders.src, Files.benchmark_r), + self._score, + ], stdout=fout, stderr=ferr, ) fout.close() ferr.close() if result.returncode != 0: - end_message("Error computing benchmark", Files.exreport_err(score)) + end_message( + "Error computing benchmark", Files.exreport_err(self._score) + ) else: - end_message("Benchmark Ok", Files.exreport_output(score)) + end_message("Benchmark Ok", Files.exreport_output(self._score)) Files.open(Files.exreport_pdf) - @staticmethod - def build_results(score): - # Build results data structure - file_name = Benchmark.get_result_file_name(score) - results = {} - with open(file_name) as f: - data = f.read().splitlines() - data = data[1:] - for line in data: - model, dataset, accuracy, stdev = line.split(", ") - if model not in results: - results[model] = {} - results[model][dataset] = (accuracy, stdev) - return results + def report(self): + print(f"{'Dataset':30s} ", end="") + lines = "=" * 30 + " " + for model in self._models: + print(f"{model:^13s} ", end="") + lines += "=" * 13 + " " + print(f"\n{lines}") + for dataset in self._datasets: + print(f"{dataset:30s} ", end="") + for model in self._models: + result = self._report[model][dataset] + print(f"{float(result['score']):.5f}±", end="") + print(f"{float(result['score_std']):.3f} ", end="") + print("") + d_name = next(iter(self._datasets)) + print(f"\n{'Model':30s} {'File Name':75s} Score") + print("=" * 30 + " " + "=" * 75 + " ========") + for model in self._models: + file_name = self._report[model][d_name]["file_name"] + report = StubReport(file_name) + report.report() + print(f"{model:^30s} {file_name:75s} {report.score:8.5f}") - @staticmethod - def report(score): - def show(results): - datasets = results[list(results)[0]] - print(f"{'Dataset':30s} ", end="") - lines = "=" * 30 + " " - for model in results: - print(f"{model:^13s} ", end="") - lines += "=" * 13 + " " - print(f"\n{lines}") - for dataset, _ in datasets.items(): - print(f"{dataset:30s} ", end="") - for model in results: - print(f"{float(results[model][dataset][0]):.5f}±", end="") - print(f"{float(results[model][dataset][1]):.3f} ", end="") - print("") + def get_excel_file_name(self): + return os.path.join( + Folders.exreport, Files.exreport_excel(self._score) + ) - print(f"* Score is: {score}") - show(Benchmark.build_results(score)) - - @staticmethod - def get_excel_file_name(score): - return os.path.join(Folders.exreport, Files.exreport_excel(score)) - - @staticmethod - def excel(score): - results = Benchmark.build_results(score) - book = xlsxwriter.Workbook(Benchmark.get_excel_file_name(score)) + def excel(self): + book = xlsxwriter.Workbook(self.get_excel_file_name()) sheet = book.add_worksheet("Benchmark") normal = book.add_format({"font_size": 14}) - bold = book.add_format({"bold": True, "font_size": 14}) decimal = book.add_format({"num_format": "0.000000", "font_size": 14}) merge_format = book.add_format( { @@ -592,22 +591,30 @@ class Benchmark: "font_size": 14, } ) + merge_format_normal = book.add_format( + { + "valign": "vcenter", + "font_size": 14, + } + ) row = row_init = 4 def header(): nonlocal row sheet.merge_range(0, 0, 1, 0, "Benchmark of Models", merge_format) - sheet.write(1, 2, f"Score is {score}", bold) + sheet.merge_range( + 0, 1, 1, 2, f"Score is {self._score}", merge_format + ) sheet.set_row(1, 20) # Set columns width sheet.set_column(0, 0, 40) - for column in range(2 * len(results)): + for column in range(2 * len(self._results)): sheet.set_column(column + 1, column + 1, 15) # Set report header # Merge 2 rows sheet.merge_range(row, 0, row + 1, 0, "Dataset", merge_format) column = 1 - for model in results: + for model in self._models: # Merge 2 columns sheet.merge_range( row, column, row, column + 1, model, merge_format @@ -615,42 +622,73 @@ class Benchmark: column += 2 row += 1 column = 1 - for _ in range(len(results)): + for _ in range(len(self._results)): sheet.write(row, column, "Score", merge_format) sheet.write(row, column + 1, "Stdev", merge_format) column += 2 def body(): nonlocal row - datasets = results[list(results)[0]] - for dataset, _ in datasets.items(): + for dataset in self._datasets: row += 1 sheet.write(row, 0, f"{dataset:30s}", normal) column = 1 - for model in results: + for model in self._models: sheet.write( row, column, - float(results[model][dataset][0]), + float(self._report[model][dataset]["score"]), decimal, ) column += 1 sheet.write( row, column, - float(results[model][dataset][1]), + float(self._report[model][dataset]["score_std"]), decimal, ) column += 1 def footer(): + nonlocal row for c in range(row_init, row + 1): sheet.set_row(c, 20) + def models_files(): + nonlocal row + row += 2 + # Set report header + # Merge 2 rows + sheet.merge_range(row, 0, row + 1, 0, "Model", merge_format) + sheet.merge_range(row, 1, row + 1, 5, "File", merge_format) + sheet.merge_range(row, 6, row + 1, 6, "Score", merge_format) + row += 1 + d_name = next(iter(self._datasets)) + for model in self._models: + file_name = self._report[model][d_name]["file_name"] + report = StubReport(file_name) + report.report() + row += 1 + sheet.write( + row, + 0, + model, + normal, + ) + sheet.merge_range( + row, 1, row, 5, file_name, merge_format_normal + ) + sheet.write( + row, + 6, + report.score, + decimal, + ) + header() body() footer() - + models_files() book.close() @@ -667,6 +705,7 @@ class StubReport(BaseReport): def footer(self, accuracy: float) -> None: self.accuracy = accuracy + self.score = accuracy / BEST_ACCURACY_STREE class Summary: @@ -674,8 +713,12 @@ class Summary: self.results = Files().get_all_results() self.data = [] self.datasets = {} + self.models = set() - def acquire(self) -> None: + def get_models(self): + return self.models + + def acquire(self, given_score="any") -> None: """Get all results""" for result in self.results: ( @@ -686,22 +729,24 @@ class Summary: time, stratified, ) = Files().split_file_name(result) - report = StubReport(os.path.join(Folders.results, result)) - report.report() - entry = dict( - score=score, - model=model, - title=report.title, - platform=platform, - date=date, - time=time, - stratified=stratified, - file=result, - metric=report.accuracy / BEST_ACCURACY_STREE, - duration=report.duration, - ) - self.datasets[result] = report.lines - self.data.append(entry) + if given_score in ("any", score): + self.models.add(model) + report = StubReport(os.path.join(Folders.results, result)) + report.report() + entry = dict( + score=score, + model=model, + title=report.title, + platform=platform, + date=date, + time=time, + stratified=stratified, + file=result, + metric=report.score, + duration=report.duration, + ) + self.datasets[result] = report.lines + self.data.append(entry) def list_results( self, diff --git a/src/benchmark.py b/src/benchmark.py index 743105a..26ec0d7 100755 --- a/src/benchmark.py +++ b/src/benchmark.py @@ -27,10 +27,11 @@ def parse_arguments(): (score, excel) = parse_arguments() -benchmark = Benchmark() -benchmark.compile_results(score) -benchmark.report(score) -benchmark.exreport(score) +benchmark = Benchmark(score) +benchmark.compile_results() +benchmark.save_results() +benchmark.report() +benchmark.exreport() if excel: - benchmark.excel(score) - Files.open(benchmark.get_excel_file_name(score)) + benchmark.excel() + Files.open(benchmark.get_excel_file_name()) diff --git a/src/pair_check.py b/src/pair_check.py index f208e2e..63147e9 100755 --- a/src/pair_check.py +++ b/src/pair_check.py @@ -2,7 +2,7 @@ import argparse import os from Results import Summary, StubReport -from Utils import EnvDefault, Folders, BEST_ACCURACY_STREE +from Utils import EnvDefault, Folders """Check best results of two models giving scores and win-tie-loose results """ @@ -66,12 +66,9 @@ for result1, result2 in zip(report_1.lines, report_2.lines): tie += 1 print(f"{'Model':<20} {'File':<70} {'Score':<10} Win Tie Loose") print("=" * 20 + " " + "=" * 70 + " " + "=" * 10 + " === === =====") -print( - f"{model1:<20} {best_1['file']:<70} " - f"{report_1.accuracy / BEST_ACCURACY_STREE:10.5f}" -) +print(f"{model1:<20} {best_1['file']:<70} {report_1.score:10.5f}") print( f"{model2:<20} {best_2['file']:<70} " - f"{report_2.accuracy / BEST_ACCURACY_STREE:10.5f} " + f"{report_2.score:10.5f} " f"{win:3d} {tie:3d} {loose:5d}" ) From efbdd5031f531cae5fe12aea85defc704dfa331b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= Date: Wed, 2 Mar 2022 13:01:44 +0100 Subject: [PATCH 3/4] Fix report title in exreport --- src/benchmark.r | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/benchmark.r b/src/benchmark.r index b447c9f..588b3dd 100644 --- a/src/benchmark.r +++ b/src/benchmark.r @@ -7,7 +7,7 @@ csv_file <- glue("results/exreport_{args[1]}.csv") destination <- "exreport/" results <- read.csv(csv_file) library(exreport) -experiment <- expCreate(results, method="classifier", problem="dataset", name="Stree") +experiment <- expCreate(results, method="classifier", problem="dataset", name="Ranking") testScore <- testMultipleControl(experiment, args[1], "max") summary(testScore) table1 <- tabularTestSummary(testScore, columns = c("pvalue", "rank", "wtl")) @@ -16,7 +16,7 @@ plot1 <- plotExpSummary(experiment, args[1], columns = 3) plot2 <- plotCumulativeRank(testScore) plot3 <- plotRankDistribution(testScore) -report <- exreport("Stree Report") +report <- exreport("Ranking Report") # Add the experiment object for reference: report <- exreportAdd(report, experiment) # Now add the test: @@ -31,7 +31,7 @@ report <- exreportAdd(report, list(plot1, plot2, table1, plot3)) # We have decided to generate the table at this point of the tutorial to discusse some special formating parameters of this function. Concretely, some of the tabular outputs generated by exreport have some properties that are only useful when rendering the objets in a graphic report, and have no effect in the object representation in the R console. In this case, we will tell the function to boldface the method that maximices the result for each column, and to split the table into to pieces when rendering. # We create the table: -table2 <- tabularExpSummary(experiment, args[1], digits=4, format="f", boldfaceColumns="max", tableSplit=2) +table2 <- tabularExpSummary(experiment, args[1], digits=4, format="f", boldfaceColumns="max", rowsAsMethod=FALSE) # And add it to the report: report <- exreportAdd(report, table2) # Now that we have finished adding elements to the report it is time to render it. We want to generate an HTML report, so we call the appropiate function, by default it renders and opens the report in your browser using a temporary file, but you can optionally specify a folder in which the report will be saved for future use. From fc2f575b7b206aefe224b93674a9a5e71194d85c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= Date: Wed, 2 Mar 2022 13:02:17 +0100 Subject: [PATCH 4/4] Hide unneeded results --- ...cy_BaggingStree_Galgo_2022-01-17_11:00:47_0.json | 0 ...cy_BaggingStree_Galgo_2022-01-17_18:54:42_0.json | 0 ...cy_BaggingStree_Galgo_2022-01-18_11:20:22_0.json | 0 ...cy_BaggingStree_Galgo_2022-02-22_19:25:59_0.json | 0 ...cy_BaggingStree_Galgo_2022-02-23_22:35:27_0.json | 0 ...acy_BaggingStree_bart_2022-02-20_16:22:48_0.json | 0 ...acy_BaggingStree_bart_2022-02-20_16:22:48_0.xlsx | Bin ...acy_BaggingStree_bart_2022-02-21_10:54:13_0.json | 0 ...acy_BaggingStree_bart_2022-02-21_10:54:13_0.xlsx | Bin ...acy_BaggingStree_bart_2022-02-21_18:42:34_0.json | 0 ...acy_BaggingStree_bart_2022-02-24_10:37:56_0.json | 0 ...acy_BaggingStree_bart_2022-02-24_14:24:49_0.json | 0 ...acy_BaggingStree_bart_2022-03-01_11:51:41_0.json | 0 ...s_accuracy_Cart_Galgo_2021-12-17_22:34:48_0.json | 0 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