R Under development (unstable) (2024-08-17 r87027 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(testthat) > library(rmcfs) Loading required package: rJava ####################### # rmcfs version 1.3.6 # ####################### If used please cite the following paper: M. Draminski, J. Koronacki (2018), rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery, Journal of Statistical Software, vol 85(12), 1-28, doi:10.18637/jss.v085.i12. > test_check("rmcfs") X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456 X27 X28 X29 X30 A1 A2 B1 B2 C1 C2 class 60 0.22730045 0.32639993 0.53409931 0.3797047 0 0 B B 0 0 B 61 0.16826293 0.29429765 0.81091625 0.7799683 0 0 0 0 C C C 62 0.14976251 0.82289798 0.65090651 0.9615772 0 0 0 0 0 0 C 63 0.64809716 0.41434742 0.77098111 0.4439984 0 0 0 0 C C C 64 0.48557255 0.01017122 0.03455832 0.2765765 0 0 0 0 0 0 C 65 0.69324432 0.05240839 0.55866028 0.6528749 0 0 0 0 C C C 66 0.28966946 0.73037038 0.12008737 0.7547240 0 0 0 0 0 0 C 67 0.97867166 0.07339495 0.68629963 0.5887064 0 0 0 0 C C C 68 0.28812633 0.07035663 0.91412566 0.3668981 0 0 0 0 C C C 69 0.52576452 0.63717197 0.56034976 0.5093910 0 0 0 0 0 0 C 70 0.07782227 0.41330587 0.57557604 0.1442291 0 0 0 0 C C C class: 'data.frame' size: 70 x 37 X1 X2 X3 X4 X5 X6 X7 1 0.53509718 0.7440799 0.70465551 0.58037379 0.62995162 0.45447665 0.33625348 2 0.24689369 0.8384462 0.91905136 0.09076585 0.45587724 0.49380877 0.14465036 3 0.92234682 0.7993568 0.61013655 0.65609916 0.25400483 0.52620712 0.86807104 4 0.75348962 0.8936403 0.05585486 0.35750658 0.05748599 0.38301289 0.05432263 5 0.90350731 0.8311370 0.73615214 0.23402548 0.43530320 0.92889790 0.61388308 6 0.65154816 0.3707501 0.90396792 0.91643034 0.77113498 0.43341785 0.78563030 7 0.02077916 0.8719798 0.70181488 0.03055500 0.12915545 0.24476248 0.57944665 8 0.09587903 0.8007616 0.57769951 0.43907404 0.98391626 0.87625727 0.52716948 9 0.94958696 0.1772845 0.56583925 0.97369032 0.81417693 0.18265992 0.41051341 10 0.88817659 0.1632777 0.36717551 0.39914081 0.98168090 0.09032375 0.73052316 X8 X9 X10 1 0.74771232 0.9503260 0.6835156 2 0.71771862 0.9001683 0.5493564 3 0.09516487 0.6939464 0.6202255 4 0.47922473 0.8362239 0.2962362 5 0.12714205 0.4631887 0.2844997 6 0.26423701 0.6290997 0.6019828 7 0.22704179 0.4391008 0.1676721 8 0.18450321 0.9699211 0.9173871 9 0.01725596 0.2795489 0.9909104 10 0.93482027 0.9794688 0.7476684 X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class 60 0.82457067 0.748439821 0.5923369 0.53276926 0 0 B B 0 0 B 61 0.52929045 0.952518457 0.9101575 0.11142191 0 0 0 0 C C C 62 0.43723560 0.362614568 0.3939510 0.77589348 0 0 0 0 0 0 C 63 0.05642579 0.346293530 0.9642003 0.06293980 0 0 0 0 C C C 64 0.24777458 0.454230331 0.2550940 0.70528527 0 0 0 0 C C C 65 0.39808635 0.668704697 0.1407718 0.04925588 0 0 0 0 C C C 66 0.11121328 0.529091748 0.5252544 0.78131666 0 0 0 0 0 0 C 67 0.42162257 0.002821188 0.4540667 0.78413926 0 0 0 0 0 0 C 68 0.23918923 0.861873356 0.5217224 0.55257574 0 0 0 0 0 0 C 69 0.85053229 0.556885670 0.0856974 0.85510841 0 0 0 0 C C C 70 0.22702517 0.901919867 0.5975495 0.20987204 0 0 0 0 C C C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.6 [2024.08.18] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ################################################## Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'. ************************** *** MCFS-ID Experiment *** ************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.52G total: 0.01G max: 0.53G Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:09 CEST 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.609 s. Prediction Summary on a Random Subsample (st): Accuracy = 80.46% WeightedAccuracy = 70.92% Cutoff RI (based on linear regression angle) = 0.0451185 Cutoff RI (based on k-means clustering) = 0.3971068 Cutoff RI (based on mean cutoff value) = 0.0451185 Important attributes (based on mean cutoff value) = 6 *** MCFS-ID Processing is done. Time: 0.718 s. *** Reading results... Done. Selected 6 nodes and 12 edges. X1 X2 X3 X4 X5 X6 X7 1 0.00731726 0.210829145 0.26581341 0.9935353 0.6996898 0.4071475 0.658076967 2 0.44516473 0.879898797 0.54714820 0.6025050 0.8548440 0.7671705 0.995597235 3 0.61748454 0.002867355 0.07699878 0.9577662 0.3782795 0.9416312 0.845622191 4 0.97213449 0.551908112 0.13350587 0.6346550 0.2754321 0.7651898 0.879890635 5 0.01174287 0.432889736 0.54912370 0.6531175 0.9884439 0.6506731 0.482919763 6 0.64908226 0.282310375 0.96777118 0.9726032 0.1340252 0.9233384 0.094897597 7 0.33699583 0.035116713 0.28218827 0.3457315 0.9779529 0.2700020 0.028267485 8 0.40686925 0.845707183 0.33484566 0.7548935 0.6422022 0.9600516 0.009439711 9 0.45143930 0.410762742 0.53944875 0.7761724 0.6005168 0.4413249 0.823091849 10 0.80896959 0.678957293 0.44180257 0.2863976 0.1621068 0.2624218 0.043040400 X8 X9 X10 1 0.11778846 0.74553645 0.86656088 2 0.18630473 0.01134493 0.06957998 3 0.06949483 0.92505929 0.65047483 4 0.33862319 0.86951255 0.24473135 5 0.86035392 0.63861688 0.32988457 6 0.78154252 0.29508899 0.27432886 7 0.27720222 0.93557533 0.97983500 8 0.72316136 0.66306316 0.91337424 9 0.63332123 0.61821985 0.67417603 10 0.15633472 0.56120914 0.32653603 X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class 60 0.39140436 0.32415402 0.95359086 0.24317646 0 0 B B 0 0 B 61 0.35394072 0.56077016 0.72693553 0.88773717 0 0 0 0 0 0 C 62 0.00305126 0.09131426 0.24735647 0.11277799 0 0 0 0 C C C 63 0.30429271 0.77488374 0.61328846 0.74236210 0 0 0 0 C C C 64 0.53121408 0.41306151 0.12878719 0.04428458 0 0 0 0 0 0 C 65 0.86390985 0.82362715 0.05236629 0.72064903 0 0 0 0 C C C 66 0.25920859 0.36593320 0.56732137 0.86323042 0 0 0 0 C C C 67 0.09397494 0.88312386 0.74001145 0.03987649 0 0 0 0 0 0 C 68 0.08388960 0.24535551 0.75075425 0.72113176 0 0 0 0 C C C 69 0.25774418 0.59452489 0.35284745 0.32683484 0 0 0 0 C C C 70 0.08654001 0.36588722 0.12569689 0.72644488 0 0 0 0 0 0 C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.6 [2024.08.18] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ################################################## *************************************************** *** MCFS-ID Cutoff Permutation Experiment #1/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Pearson's correlation of shuffled decision: -0.0099 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:10 CEST 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.28 s. Prediction Summary on a Random Subsample (st): Accuracy = 50.17% WeightedAccuracy = 33.46% Cutoff RI (based on linear regression angle) = 0.0172690 Cutoff RI (based on k-means clustering) = 0.0395634 Cutoff RI (based on mean cutoff value) = 0.0199889 Important attributes (based on mean cutoff value) = 5 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #2/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Pearson's correlation of shuffled decision: 0.1400 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:11 CEST 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.234 s. Prediction Summary on a Random Subsample (st): Accuracy = 50.89% WeightedAccuracy = 34.45% Cutoff RI (based on linear regression angle) = 0.0188682 Cutoff RI (based on k-means clustering) = 0.0385477 Cutoff RI (based on mean cutoff value) = 0.0194199 Important attributes (based on mean cutoff value) = 5 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #3/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Pearson's correlation of shuffled decision: -0.1599 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:11 CEST 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.172 s. Prediction Summary on a Random Subsample (st): Accuracy = 51.31% WeightedAccuracy = 31.88% Cutoff RI (based on linear regression angle) = 0.0083842 Cutoff RI (based on k-means clustering) = 0.0098207 Cutoff RI (based on mean cutoff value) = 0.0062872 Important attributes (based on mean cutoff value) = 6 ************************** *** MCFS-ID Experiment *** ************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:11 CEST 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.14 s. Prediction Summary on a Random Subsample (st): Accuracy = 78.91% WeightedAccuracy = 68.54% Cutoff RI (based on linear regression angle) = 0.0220053 Cutoff RI (based on k-means clustering) = 0.4270678 Cutoff RI (based on mean cutoff value) = 0.0220053 Important attributes (based on mean cutoff value) = 6 *** Calculation of cutoff RI (based on permutations) *** Max RI (raw data) = 0.78618485 Max RI (after permutations) = [0.057576038, 0.044359643, 0.016236143] Anderson-Darling normality test p-value = 0.4620343 Confidence Interval: -0.0130573 ; 0.0918385 Cutoff RI (based on permutations) = 0.0918385 Important attributes (based on permutations) = 6 *** Calculation of cutoff ID *** Anderson-Darling normality test p-value = 0.4879800 Confidence Interval: 4.9582703 ; 9.7604580 Cutoff ID (based on permutations) = 9.7604580 *** Final Important attributes (based on permutations) = 6 *** MCFS-ID Processing is done. Time: 1.0 s. *** Reading results... Done. ##### MCFS-ID result (s = auto, t = 5, m = auto) ##### Target feature: 'class' Top 6 features: position attribute RI 1 A1 0.7861849 2 A2 0.7705875 3 B2 0.4344144 4 B1 0.4270679 5 C1 0.2573944 6 C2 0.2442681 ################################# Cutoff values: method minRI size minID criticalAngle 0.02200539 7 NA kmeans 0.42706785 4 NA permutations 0.09183859 6 9.760458 mean 0.02200539 6 NA ################################# Confusion matrix obtained on randomly selected (st) datasets: Confusion Matrix: A B C A 13479 418 243 B 2116 4647 307 C 1425 814 1801 TPR (sensitivity/recall): TPR 1 95.3 % 2 65.7 % 3 44.6 % Accuracy: 78.9 % wAccuracy: 68.5 % ################################# MCFS-ID execution time: 1 secs method minRI size minID 1 criticalAngle 0.02200539 7 NA 2 kmeans 0.42706785 4 NA 3 permutations 0.09183859 6 9.760458 4 mean 0.02200539 6 NA [1] 6 projection distance commonPart mAvg beta1 1 30 0.875 1 0 0 2 40 0.625 1 0 0 3 50 0.250 1 0 0 4 60 0.375 1 0 0 5 70 0.125 1 0 0 6 80 0.125 1 0 0 7 90 0.250 1 0 0 8 100 0.375 1 0 0 9 110 0.000 1 0 0 10 120 0.125 1 0 0 11 130 0.125 1 0 0 12 140 0.000 1 0 0 13 150 0.250 1 0 0 14 160 0.250 1 0 0 15 170 0.125 1 0 0 16 180 0.125 1 0 0 17 190 0.375 1 0 0 18 200 0.250 1 0 0 position attribute projections classifiers nodes RI 11 1 A1 46 0.9304348 0.9304348 0.786184850 12 2 A2 48 0.9416667 0.9416667 0.770587500 14 3 B2 50 0.9200000 0.9200000 0.434414420 13 4 B1 51 0.8823530 0.8823530 0.427067850 15 5 C1 55 0.7745454 0.7745454 0.257394400 16 6 C2 56 0.7571428 0.7571428 0.244268090 10 7 X10 44 0.3136364 0.5500000 0.022005394 6 8 X6 52 0.3846154 0.5153846 0.017985187 8 9 X8 55 0.2727273 0.3454545 0.014340771 4 10 X4 56 0.1928572 0.3071429 0.008099830 7 11 X7 40 0.1500000 0.2250000 0.006532601 1 12 X1 63 0.1841270 0.2888889 0.005781665 9 13 X9 54 0.1444445 0.2222222 0.005775066 2 14 X2 50 0.1600000 0.3000000 0.005644807 5 15 X5 40 0.1500000 0.2600000 0.005008572 3 16 X3 48 0.0625000 0.0875000 0.001991920 position edge_a edge_b weight 1 1 B1 C1 19.054710 2 2 A1 B2 18.859510 3 3 B2 C2 17.831297 4 4 A2 B1 15.816913 5 5 B2 C1 13.134384 6 6 B1 C2 12.190277 7 7 A2 B2 9.156206 8 8 A1 C2 8.537681 9 9 A1 B1 8.210845 10 10 A1 C1 6.137092 11 11 A2 C1 6.079792 12 12 A2 C2 5.835807 13 13 X6 X1 3.530129 14 14 B2 X8 3.248156 15 15 X10 X2 3.068004 16 16 X6 X2 2.585952 17 17 C2 X6 2.424878 18 18 B2 X10 2.072237 19 19 X10 X4 2.067072 20 20 X7 X2 2.049466 21 21 A2 X6 1.929618 22 22 X6 X10 1.861249 23 23 C2 X10 1.856235 24 24 C1 X10 1.758056 25 25 X4 X10 1.756928 26 26 X5 X1 1.576373 27 27 X6 X4 1.496008 28 28 B1 X10 1.490862 29 29 B1 X8 1.479085 30 30 X4 X6 1.425213 31 31 B2 A1 1.422222 32 32 X6 X8 1.414832 33 33 X2 X10 1.371073 34 34 B1 X6 1.368352 35 35 A1 X6 1.352818 36 36 C2 B2 1.336196 37 37 X1 X6 1.304323 38 38 X2 X8 1.299954 39 39 X8 X9 1.295505 40 40 X7 X1 1.280761 41 41 X4 X9 1.280458 42 42 X1 X8 1.276276 43 43 X1 X10 1.244036 44 44 X4 X2 1.237359 45 45 X4 X8 1.225507 46 46 X10 X6 1.207493 47 47 X8 X1 1.142952 48 48 X10 X9 1.100707 49 49 C2 X8 1.081548 50 50 X1 X2 1.075525 Selected 6 nodes and 17 edges. Selected 6 nodes and 12 edges. X1 X2 X3 X4 X5 X6 X7 1 0.03358539 0.38982462 0.13132496 0.444813395 0.07498160 0.41523706 0.7053306 2 0.66839876 0.96132609 0.04211883 0.305250265 0.59073816 0.49490051 0.3882839 3 0.43455328 0.56036897 0.75105861 0.993445246 0.66346530 0.79200489 0.6616169 4 0.78013449 0.09832316 0.34626418 0.009692959 0.63540106 0.99234987 0.1326890 5 0.34146845 0.93833557 0.49774420 0.061143424 0.02020194 0.72344784 0.2979759 6 0.21272271 0.76992954 0.07904540 0.570274731 0.10228839 0.08026426 0.9336295 7 0.25674223 0.23508806 0.17864565 0.392376134 0.19951154 0.02945676 0.3177363 8 0.63459078 0.50569588 0.86078870 0.021898214 0.94320957 0.47700799 0.5618296 9 0.52559896 0.77727399 0.93952259 0.846525087 0.58753183 0.93854431 0.9394342 10 0.87792593 0.43502875 0.87904425 0.521139663 0.29430394 0.44510650 0.8815169 X8 X9 X10 1 0.7175351 0.9609848 0.2943328 2 0.7283432 0.8862388 0.5011538 3 0.4422318 0.4617859 0.9802661 4 0.4467386 0.3950327 0.9217611 5 0.5531552 0.3131409 0.1384937 6 0.1022949 0.7588188 0.7616608 7 0.3808513 0.4776080 0.2755373 8 0.2357796 0.4841690 0.8737771 9 0.4769490 0.1847203 0.5970275 10 0.4790298 0.9901388 0.4278057 X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class 60 0.2733181 0.67423080 0.11570185 0.05103789 0 0 B B 0 0 B 61 0.4625091 0.62451957 0.57466998 0.38090011 0 0 0 0 C C C 62 0.4134256 0.29968790 0.79884154 0.35553155 0 0 0 0 C C C 63 0.6013196 0.29910137 0.96744154 0.77436315 0 0 0 0 C C C 64 0.6280889 0.33229820 0.01541382 0.98448822 0 0 0 0 C C C 65 0.4792754 0.48511817 0.08346686 0.96848583 0 0 0 0 0 0 C 66 0.9100958 0.93055448 0.06496006 0.21038681 0 0 0 0 C C C 67 0.1574688 0.63673548 0.56327824 0.29152335 0 0 0 0 0 0 C 68 0.3722628 0.01638103 0.96026476 0.59918354 0 0 0 0 0 0 C 69 0.9049485 0.62452932 0.41978631 0.23325240 0 0 0 0 0 0 C 70 0.5573595 0.65456282 0.55324508 0.38721694 0 0 0 0 C C C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.6 [2024.08.18] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ################################################## Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'. ************************** *** MCFS-ID Experiment *** ************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:13 CEST 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.125 s. Prediction Summary on a Random Subsample (st): Accuracy = 76.76% WeightedAccuracy = 65.92% Cutoff RI (based on linear regression angle) = 0.0274656 Cutoff RI (based on k-means clustering) = 0.4009718 Cutoff RI (based on mean cutoff value) = 0.0274656 Important attributes (based on mean cutoff value) = 6 *** MCFS-ID Processing is done. Time: 0.157 s. *** Reading results... Done. Selected 6 nodes and 12 edges. X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456 X97 X98 X99 X100 A1 A2 B1 B2 C1 C2 class 60 0.22378404 0.51307358 0.3994570 0.02202729 0 0 B B 0 0 B 61 0.69587201 0.77525486 0.9689147 0.55346841 0 0 0 0 C C C 62 0.14077874 0.86028153 0.9072225 0.07312673 0 0 0 0 0 0 C 63 0.03859708 0.15871952 0.8472281 0.86110955 0 0 0 0 C C C 64 0.63851974 0.05270203 0.2219598 0.63500276 0 0 0 0 0 0 C 65 0.25135768 0.17265011 0.4159029 0.90253739 0 0 0 0 C C C 66 0.33987375 0.18344931 0.9690545 0.04770292 0 0 0 0 0 0 C 67 0.60909188 0.48718376 0.5835705 0.90106259 0 0 0 0 C C C 68 0.55731016 0.83232775 0.4722076 0.51572658 0 0 0 0 C C C 69 0.66711758 0.06494922 0.6722287 0.36741652 0 0 0 0 0 0 C 70 0.31903838 0.79192239 0.3533426 0.35998763 0 0 0 0 C C C class: 'data.frame' size: 70 x 107Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.6 [2024.08.18] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ################################################## Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'. ************************** *** MCFS-ID Experiment *** ************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 107 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Numeric target detected - using M5 model MCFS-ID param: ID-Graph is ON MCFS-ID param: finalCV is ON Starting MCFS-ID Procedure: projectionSize(m) = 10, projections(s) = 212, splits(t) = 5 Start time: Mon Aug 19 10:53:13 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=====> ] 9% Time: 00:00 ETA: --:-- [============> ] 19% Time: 00:00 ETA: --:-- [======================> ] 33% Time: 00:00 ETA: --:-- [================================> ] 47% Time: 00:01 ETA: --:-- [======================================> ] 57% Time: 00:01 ETA: --:-- [================================================> ] 71% Time: 00:01 ETA: --:-- [==========================================================> ] 85% Time: 00:01 ETA: --:-- [====================================================================> ] 99% Time: 00:01 ETA: --:-- [=====================================================================>] 100% Time: 00:01 1060 trees built within 1.9 s. Prediction Summary on a Random Subsample (st): pearson: mean = 0.19714685679046492 median = 0.0 stdev = 0.3785735601222462 MAE: mean = 0.5822268205674695 median = 0.6394927501678467 stdev = 0.16084495727069972 RMSE: mean = 0.6723319524165355 median = 0.707329511642456 stdev = 0.1559868227285496 SMAPE: mean = 0.18326054566350333 median = 0.20413188636302948 stdev = 0.049676804451907905 Cutoff RI (based on linear regression angle) = 0.0061645 Cutoff RI (based on k-means clustering) = 0.4609433 Cutoff RI (based on mean cutoff value) = 0.0082593 Important attributes (based on mean cutoff value) = 9 *** Running CV experiment on input data limited to the top [2, 5, 7, 9, 11, 14, 18] attributes *** Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 *** MCFS-ID Processing is done. Time: 2.2 s. *** Reading results... Done. X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456 X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class 60 0.73271802 0.05737268 0.91933177 0.4484275 0 0 B B 0 0 B 61 0.87080555 0.85166764 0.07943969 0.7771450 0 0 0 0 C C C 62 0.57217026 0.21264535 0.50737425 0.1582198 0 0 0 0 0 0 C 63 0.01103607 0.53946203 0.82017162 0.8668086 0 0 0 0 C C C 64 0.90631526 0.13648759 0.59839542 0.2061456 0 0 0 0 0 0 C 65 0.77065363 0.32486514 0.42415353 0.1779497 0 0 0 0 C C C 66 0.38250462 0.62107629 0.55931027 0.1648911 0 0 0 0 0 0 C 67 0.09404589 0.25598225 0.78909447 0.5652690 0 0 0 0 C C C 68 0.04965358 0.63487580 0.16771526 0.7271810 0 0 0 0 C C C 69 0.82116232 0.48567211 0.97045173 0.8759190 0 0 0 0 0 0 C 70 0.82932430 0.93817692 0.47350310 0.7084244 0 0 0 0 C C C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.6 [2024.08.18] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ################################################## *************************************************** *** MCFS-ID Cutoff Permutation Experiment #1/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.00G used: 0.53G total: 0.00G max: 0.53G Pearson's correlation of shuffled decision: -0.0399 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:15 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.531 s. Prediction Summary on a Random Subsample (st): Accuracy = 50.94% WeightedAccuracy = 35.38% Cutoff RI (based on linear regression angle) = 0.0177116 Cutoff RI (based on k-means clustering) = 0.0365365 Cutoff RI (based on mean cutoff value) = 0.0211711 Important attributes (based on mean cutoff value) = 5 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #2/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.52G total: 0.01G max: 0.53G Pearson's correlation of shuffled decision: 0.2599 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:16 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.312 s. Prediction Summary on a Random Subsample (st): Accuracy = 51.68% WeightedAccuracy = 35.42% Cutoff RI (based on linear regression angle) = 0.0213335 Cutoff RI (based on k-means clustering) = 0.0320616 Cutoff RI (based on mean cutoff value) = 0.0213335 Important attributes (based on mean cutoff value) = 6 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #3/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.52G total: 0.01G max: 0.53G Pearson's correlation of shuffled decision: 0.0 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:16 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.28 s. Prediction Summary on a Random Subsample (st): Accuracy = 50.35% WeightedAccuracy = 34.41% Cutoff RI (based on linear regression angle) = 0.0273366 Cutoff RI (based on k-means clustering) = 0.0199981 Cutoff RI (based on mean cutoff value) = 0.0165373 Important attributes (based on mean cutoff value) = 6 ************************** *** MCFS-ID Experiment *** ************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done MEMORY Status - free: 0.01G used: 0.52G total: 0.01G max: 0.53G Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: finalCV is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Mon Aug 19 10:53:17 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.233 s. Prediction Summary on a Random Subsample (st): Accuracy = 79.01% WeightedAccuracy = 69.00% Cutoff RI (based on linear regression angle) = 0.0181059 Cutoff RI (based on k-means clustering) = 0.4120377 Cutoff RI (based on mean cutoff value) = 0.0181059 Important attributes (based on mean cutoff value) = 6 *** Calculation of cutoff RI (based on permutations) *** Max RI (raw data) = 0.72376907 Max RI (after permutations) = [0.055015575, 0.05414359, 0.035604194] Anderson-Darling normality test p-value = 0.0813281 Confidence Interval: 0.0210180 ; 0.0754908 Cutoff RI (based on permutations) = 0.0754908 Important attributes (based on permutations) = 6 *** Calculation of cutoff ID *** Anderson-Darling normality test p-value = 0.6287300 Confidence Interval: 9.7308185 ; 12.8343097 Cutoff ID (based on permutations) = 12.8343097 *** Final Important attributes (based on permutations) = 6 *** Running CV experiment on input data limited to the top [2, 3, 5, 6, 8, 9, 12] attributes *** Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper *** MCFS-ID Processing is done. Time: 7.1 s. *** Reading results... Done. Checking input data... Error: The names of the following attributes: MCFS,contrast_1attr_abds, MCFS'contrast_2attr_abds, MCFS#contrast_3attr_abds, MCFS()contrast_attr_abds, MCFS[]contrast_attr_abds, MCFS{}contrast_attr_abds, MCFS{}'#contrast,attr.abds contain forbidden characters. Please run fix.data() function before running mcfs(). Fixing names... Fixing values... Fixing types... Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.6 [2024.08.18] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ################################################## *************************************************** *** MCFS-ID Cutoff Permutation Experiment #1/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done MEMORY Status - free: 0.00G used: 0.52G total: 0.01G max: 0.53G Pearson's correlation of shuffled decision: 0.0799 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Mon Aug 19 10:53:23 CEST 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:00 ETA: --:-- [===========================> ] 41% Time: 00:00 ETA: --:-- [==================================> ] 51% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 73% Time: 00:00 ETA: --:-- [========================================================> ] 83% Time: 00:00 ETA: --:-- [=================================================================> ] 96% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1570 trees built within 0.843 s. Prediction Summary on a Random Subsample (st): Accuracy = 42.69% WeightedAccuracy = 33.74% Cutoff RI (based on linear regression angle) = 0.0326759 Cutoff RI (based on k-means clustering) = 0.0164270 Cutoff RI (based on mean cutoff value) = 0.0211142 Important attributes (based on mean cutoff value) = 54 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #2/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done MEMORY Status - free: 0.02G used: 0.51G total: 0.02G max: 0.53G Pearson's correlation of shuffled decision: 0.2199 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Mon Aug 19 10:53:24 CEST 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:00 ETA: --:-- [===========================> ] 41% Time: 00:00 ETA: --:-- [==================================> ] 51% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 73% Time: 00:00 ETA: --:-- [========================================================> ] 83% Time: 00:00 ETA: --:-- [=================================================================> ] 96% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1570 trees built within 0.625 s. Prediction Summary on a Random Subsample (st): Accuracy = 42.84% WeightedAccuracy = 33.65% Cutoff RI (based on linear regression angle) = 0.0320854 Cutoff RI (based on k-means clustering) = 0.0136517 Cutoff RI (based on mean cutoff value) = 0.0178122 Important attributes (based on mean cutoff value) = 75 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #3/3 *** *************************************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done MEMORY Status - free: 0.02G used: 0.50G total: 0.03G max: 0.53G Pearson's correlation of shuffled decision: -0.1199 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Mon Aug 19 10:53:25 CEST 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:00 ETA: --:-- [===========================> ] 41% Time: 00:00 ETA: --:-- [==================================> ] 51% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 73% Time: 00:00 ETA: --:-- [========================================================> ] 83% Time: 00:00 ETA: --:-- [=================================================================> ] 96% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1570 trees built within 0.625 s. Prediction Summary on a Random Subsample (st): Accuracy = 41.94% WeightedAccuracy = 32.95% Cutoff RI (based on linear regression angle) = 0.0272028 Cutoff RI (based on k-means clustering) = 0.0133993 Cutoff RI (based on mean cutoff value) = 0.0173876 Important attributes (based on mean cutoff value) = 73 ************************** *** MCFS-ID Experiment *** ************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done MEMORY Status - free: 0.02G used: 0.51G total: 0.02G max: 0.53G Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Mon Aug 19 10:53:26 CEST 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:00 ETA: --:-- [===========================> ] 41% Time: 00:00 ETA: --:-- [==================================> ] 51% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 73% Time: 00:00 ETA: --:-- [========================================================> ] 83% Time: 00:00 ETA: --:-- [=================================================================> ] 96% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1570 trees built within 0.639 s. Prediction Summary on a Random Subsample (st): Accuracy = 46.90% WeightedAccuracy = 37.93% Cutoff RI (based on linear regression angle) = 0.0306562 Cutoff RI (based on k-means clustering) = 0.3508316 Cutoff RI (based on mean cutoff value) = 0.0394586 Important attributes (based on mean cutoff value) = 14 *** Calculation of cutoff RI (based on permutations) *** Max RI (raw data) = 0.6696969 Max RI (after permutations) = [0.06497993, 0.07040654, 0.059140034] Anderson-Darling normality test p-value = 0.6283348 Confidence Interval: 0.0508452 ; 0.0788390 Cutoff RI (based on permutations) = 0.0788390 Important attributes (based on permutations) = 6 *** Calculation of cutoff ID *** Anderson-Darling normality test p-value = 0.1113444 Confidence Interval: 0.2776522 ; 2.4266361 Cutoff ID (based on permutations) = 2.4266361 *** Final Important attributes (based on permutations) = 6 *** MCFS-ID Processing is done. Time: 3.2 s. *** Reading results... Done. X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456 X97 X98 X99 X100 A1 A2 B1 B2 C1 C2 class 60 0.22378404 0.51307358 0.3994570 0.02202729 0 0 B B 0 0 B 61 0.69587201 0.77525486 0.9689147 0.55346841 0 0 0 0 C C C 62 0.14077874 0.86028153 0.9072225 0.07312673 0 0 0 0 0 0 C 63 0.03859708 0.15871952 0.8472281 0.86110955 0 0 0 0 C C C 64 0.63851974 0.05270203 0.2219598 0.63500276 0 0 0 0 0 0 C 65 0.25135768 0.17265011 0.4159029 0.90253739 0 0 0 0 C C C 66 0.33987375 0.18344931 0.9690545 0.04770292 0 0 0 0 0 0 C 67 0.60909188 0.48718376 0.5835705 0.90106259 0 0 0 0 C C C 68 0.55731016 0.83232775 0.4722076 0.51572658 0 0 0 0 C C C 69 0.66711758 0.06494922 0.6722287 0.36741652 0 0 0 0 0 0 C 70 0.31903838 0.79192239 0.3533426 0.35998763 0 0 0 0 C C C class: 'data.frame' size: 70 x 107Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.6 [2024.08.18] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ################################################## **************************************************** *** Running Phase I - Initial MCFS-ID filtering *** ************************** *** MCFS-ID Experiment *** ************************** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 107 attributes to load... Done MEMORY Status - free: 0.02G used: 0.51G total: 0.02G max: 0.53G Nominal target detected - using J48 model MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Adding Contrast Attributes... Data size: attributes: 118 objects: 70 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 10, projections(s) = 530, splits(t) = 5 Start time: Mon Aug 19 10:53:27 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=> ] 4% Time: 00:00 ETA: --:-- [=========> ] 15% Time: 00:00 ETA: --:-- [================> ] 25% Time: 00:00 ETA: --:-- [========================> ] 36% Time: 00:00 ETA: --:-- [================================> ] 47% Time: 00:00 ETA: --:-- [======================================> ] 57% Time: 00:00 ETA: --:-- [==============================================> ] 68% Time: 00:01 ETA: --:-- [======================================================> ] 79% Time: 00:01 ETA: --:-- [=============================================================> ] 89% Time: 00:01 ETA: --:-- [=====================================================================>] 100% Time: 00:01 ETA: --:-- [=====================================================================>] 100% Time: 00:01 2650 trees built within 1.4 s. Prediction Summary on a Random Subsample (st): Accuracy = 56.29% WeightedAccuracy = 45.76% Cutoff RI (based on linear regression angle) = 0.0229338 Cutoff RI (based on k-means clustering) = 0.4893485 Cutoff RI (based on contrast attributes) = 0.0251361 Cutoff RI (based on mean cutoff value) = 0.0251361 Important attributes (based on mean cutoff value) = 12 *** MCFS-ID Processing is done. Time: 1.5 s. *** *************************************************** *** Running Phase II - Final MCFS-ID filtering *** *************************************************** *** MCFS-ID Cutoff Permutation Experiment #1/3 *** *************************************************** Loading data: 'input.adx'... 70 objects and 14 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Pearson's correlation of shuffled decision: -0.1307 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 163, splits(t) = 5 Start time: Mon Aug 19 10:53:28 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 12% Time: 00:00 ETA: --:-- [================> ] 25% Time: 00:00 ETA: --:-- [========================> ] 37% Time: 00:00 ETA: --:-- [=================================> ] 49% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 74% Time: 00:00 ETA: --:-- [===========================================================> ] 86% Time: 00:00 ETA: --:-- [===================================================================> ] 98% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 815 trees built within 0.218 s. Prediction Summary on a Random Subsample (st): Accuracy = 49.67% WeightedAccuracy = 32.84% Cutoff RI (based on linear regression angle) = 0.0165680 Cutoff RI (based on k-means clustering) = 0.0155275 Cutoff RI (based on mean cutoff value) = 0.0136276 Important attributes (based on mean cutoff value) = 6 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #2/3 *** *************************************************** Loading data: 'input.adx'... 70 objects and 14 attributes to load... Done MEMORY Status - free: 0.01G used: 0.51G total: 0.02G max: 0.53G Pearson's correlation of shuffled decision: -0.0230 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 163, splits(t) = 5 Start time: Mon Aug 19 10:53:28 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 12% Time: 00:00 ETA: --:-- [================> ] 25% Time: 00:00 ETA: --:-- [========================> ] 37% Time: 00:00 ETA: --:-- [=================================> ] 49% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 74% Time: 00:00 ETA: --:-- [===========================================================> ] 86% Time: 00:00 ETA: --:-- [===================================================================> ] 98% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 815 trees built within 0.234 s. Prediction Summary on a Random Subsample (st): Accuracy = 51.44% WeightedAccuracy = 34.29% Cutoff RI (based on linear regression angle) = 0.0193759 Cutoff RI (based on k-means clustering) = 0.0600299 Cutoff RI (based on mean cutoff value) = 0.0196526 Important attributes (based on mean cutoff value) = 3 *************************************************** *** MCFS-ID Cutoff Permutation Experiment #3/3 *** *************************************************** Loading data: 'input.adx'... 70 objects and 14 attributes to load... Done MEMORY Status - free: 0.00G used: 0.52G total: 0.01G max: 0.53G Pearson's correlation of shuffled decision: 0.1923 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 163, splits(t) = 5 Start time: Mon Aug 19 10:53:29 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 12% Time: 00:00 ETA: --:-- [================> ] 25% Time: 00:00 ETA: --:-- [========================> ] 37% Time: 00:00 ETA: --:-- [=================================> ] 49% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 74% Time: 00:00 ETA: --:-- [===========================================================> ] 86% Time: 00:00 ETA: --:-- [===================================================================> ] 98% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 815 trees built within 0.25 s. Prediction Summary on a Random Subsample (st): Accuracy = 50.23% WeightedAccuracy = 31.71% Cutoff RI (based on linear regression angle) = 0.0119106 Cutoff RI (based on k-means clustering) = 0.0101714 Cutoff RI (based on mean cutoff value) = 0.0101714 Important attributes (based on mean cutoff value) = 6 ************************** *** MCFS-ID Experiment *** ************************** Loading data: 'input.adx'... 70 objects and 14 attributes to load... Done MEMORY Status - free: 0.00G used: 0.52G total: 0.01G max: 0.53G Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: finalCV is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 163, splits(t) = 5 Start time: Mon Aug 19 10:53:29 CEST 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 12% Time: 00:00 ETA: --:-- [================> ] 25% Time: 00:00 ETA: --:-- [========================> ] 37% Time: 00:00 ETA: --:-- [=================================> ] 49% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:00 ETA: --:-- [==================================================> ] 74% Time: 00:00 ETA: --:-- [===========================================================> ] 86% Time: 00:00 ETA: --:-- [===================================================================> ] 98% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 815 trees built within 0.187 s. Prediction Summary on a Random Subsample (st): Accuracy = 82.42% WeightedAccuracy = 73.87% Cutoff RI (based on linear regression angle) = 0.2575351 Cutoff RI (based on k-means clustering) = 0.4248852 Cutoff RI (based on mean cutoff value) = 0.2575351 Important attributes (based on mean cutoff value) = 5 *** Calculation of cutoff RI (based on permutations) *** Max RI (raw data) = 0.7795318 Max RI (after permutations) = [0.027199801, 0.060029972, 0.01621368] Anderson-Darling normality test p-value = 0.3349681 Confidence Interval: -0.0221512 ; 0.0911135 Cutoff RI (based on permutations) = 0.0911135 Important attributes (based on permutations) = 6 *** Calculation of cutoff ID *** Anderson-Darling normality test p-value = 0.5761072 Confidence Interval: 4.5315525 ; 10.9624841 Cutoff ID (based on permutations) = 10.9624841 *** Final Important attributes (based on permutations) = 6 *** Running CV experiment on input data limited to the top [2, 3, 5, 6, 8, 9, 12] attributes *** Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper *** MCFS-ID Processing is done. Time: 5.9 s. *** Reading results... Done. [ FAIL 0 | WARN 2 | SKIP 3 | PASS 35 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • empty test (3): 'test-man.build.idgraph.R:4:1', 'test-man.mcfs.R:4:1', 'test-man.plot.idgraph.R:4:1' [ FAIL 0 | WARN 2 | SKIP 3 | PASS 35 ] > > proc.time() user system elapsed 72.96 8.75 27.70