 | Run 14068 |
| Creator | zenogantner |
| Program | MyMediaLite-biased-matrix-factorization-k-20 |
| Dataset | collaborativefiltering-sample |
| Task type | CollaborativeFiltering |
| Created | 28d17h ago |
| Download | Login required! |
Done! 
| 6s | |
| 418M | |
| CollaborativeFiltering | |
| 1s | |
| 0.066 | |
| 0.045 | |
| 0s | |
| 2.27 | |
| 1.78 | |
Log file
... (lines omitted) ... loss 0.0830007778937565 learn_rate 0.656014293415656 loss 0.0829621340895792 learn_rate 0.688815008086439 loss 0.0829443723288018 learn_rate 0.723255758490761 loss 0.0829335641047615 learn_rate 0.759418546415299 loss 0.0829255485096557 learn_rate 0.797389473736064 loss 0.0829192651717335 learn_rate 0.837258947422868 loss 0.0829146158486991 learn_rate 0.879121894794011 loss 0.0829117758836837 learn_rate 0.923077989533712 loss 0.082911000325724 learn_rate 0.969231889010397 loss 0.0829125806466106 learn_rate 0.484615944505199 loss 0.0827093186442371 learn_rate 0.508846741730459 loss 0.0826312314651233 learn_rate 0.534289078816982 loss 0.0825962427206141 learn_rate 0.561003532757831 loss 0.0825781782376093 learn_rate 0.589053709395722 loss 0.0825674905192483 learn_rate 0.618506394865509 loss 0.0825602945007355 learn_rate 0.649431714608784 loss 0.0825549829390974 learn_rate 0.681903300339223 loss 0.0825509626579094 learn_rate 0.715998465356184 loss 0.0825480973516961 learn_rate 0.751798388623994 loss 0.0825464499610233 learn_rate 0.789388308055193 loss 0.0825461695995218 learn_rate 0.828857723457953 loss 0.0825474473099058 learn_rate 0.414428861728977 loss 0.0824281374175248 learn_rate 0.435150304815425 loss 0.0823728259403192 learn_rate 0.456907820056197 loss 0.0823444442814325 learn_rate 0.479753211059007 loss 0.0823284155082638 learn_rate 0.503740871611957 loss 0.0823185657097087 learn_rate 0.528927915192555 loss 0.0823120385328412 learn_rate 0.555374310952183 loss 0.0823074381280229 learn_rate 0.583143026499792 loss 0.0823040938578704 learn_rate 0.612300177824781 loss 0.0823017231673374 learn_rate 0.642915186716021 loss 0.0823002548676968 learn_rate 0.675060946051822 loss 0.0822997316072845 learn_rate 0.708813993354413 loss 0.0823002575358433 learn_rate 0.354406996677206 loss 0.0822291850006204 learn_rate 0.372127346511067 loss 0.0821909409268285 learn_rate 0.39073371383662 loss 0.0821688819960734 learn_rate 0.410270399528451 loss 0.082155248268866 learn_rate 0.430783919504874 loss 0.0821462975585282 learn_rate 0.452323115480117 loss 0.0821401214368112 learn_rate 0.474939271254123 loss 0.082135690920211 learn_rate 0.49868623481683 loss 0.0821324379418198 learn_rate 0.523620546557671 loss 0.0821300604847113 learn_rate 0.549801573885555 loss 0.0821284206084238 learn_rate 0.577291652579832 loss 0.082127484229661 learn_rate 0.606156235208824 loss 0.0821272832359546 learn_rate 0.636464046969265 loss 0.0821278918048754 learn_rate 0.318232023484633 loss 0.082078811068215 learn_rate 0.334143624658864 loss 0.0820501589978281 learn_rate 0.350850805891807 loss 0.0820325418457332 learn_rate 0.368393346186398 loss 0.0820211220093695 learn_rate 0.386813013495718 loss 0.0820133705211814 learn_rate 0.406153664170504 loss 0.0820079180191061 learn_rate 0.426461347379029 loss 0.0820039889594063 learn_rate 0.44778441474798 loss 0.0820011296590165 learn_rate 0.470173635485379 loss 0.0819990764398216 learn_rate 0.493682317259648 loss 0.0819976875810589 learn_rate 0.518366433122631 loss 0.0819969040620367 learn_rate 0.544284754778762 loss 0.0819967242885439 learn_rate 0.571498992517701 loss 0.081997186901876 learn_rate 0.28574949625885 loss 0.0819630346784502 learn_rate 0.300036971071793 loss 0.0819416187171791 learn_rate 0.315038819625382 loss 0.0819276506591713 learn_rate 0.330790760606652 loss 0.0819181585726624 learn_rate 0.347330298636984 loss 0.0819114692175542 learn_rate 0.364696813568833 loss 0.0819066235275023 learn_rate 0.382931654247275 loss 0.0819030546042642 learn_rate 0.402078236959639 loss 0.0819004169186218 learn_rate 0.422182148807621 loss 0.0818984978187764 learn_rate 0.443291256248002 loss 0.0818971712671177 learn_rate 0.465455819060402 loss 0.081896372131398 learn_rate 0.488728610013422 loss 0.0818960802700446 learn_rate 0.513165040514093 loss 0.0818963095745081 learn_rate 0.256582520257047 loss 0.0818723882526507 learn_rate 0.269411646269899 loss 0.0818564096119868 learn_rate 0.282882228583394 loss 0.0818454087655277 learn_rate 0.297026340012564 loss 0.0818375862830773 learn_rate 0.311877657013192 loss 0.0818318579853031 learn_rate 0.327471539863851 loss 0.0818275677504117 learn_rate 0.343845116857044 loss 0.0818243119263732 learn_rate 0.361037372699896 loss 0.0818218365372275 learn_rate 0.379089241334891 loss 0.081819979457371 learn_rate 0.398043703401636 loss 0.081818638741858 learn_rate 0.417945888571717 loss 0.0818177552152729 learn_rate 0.438843183000303 loss 0.0818173023534838 learn_rate 0.460785342150318 loss 0.0818172797410963 learn_rate 0.483824609257834 loss 0.0818177083415638 learn_rate 0.241912304628917 loss 0.0817983806998388 learn_rate 0.254007919860363 loss 0.0817851098730121 learn_rate 0.266708315853381 loss 0.0817757696189403 learn_rate 0.28004373164605 loss 0.0817690193228951 learn_rate 0.294045918228353 loss 0.0817640230079598 learn_rate 0.30874821413977 loss 0.0817602602116626 learn_rate 0.324185624846759 loss 0.0817574039620055 learn_rate 0.340394906089097 loss 0.0817552454729233 learn_rate 0.357414651393552 loss 0.0817536496888521 learn_rate 0.375285383963229 loss 0.0817525300344951 learn_rate 0.394049653161391 loss 0.081751834365343 learn_rate 0.41375213581946 loss 0.081751537004416 learn_rate 0.434439742610433 loss 0.081751633865913 learn_rate 0.217219871305217 loss 0.0817379602141058 learn_rate 0.228080864870478 loss 0.0817280641997448 learn_rate 0.239484908114001 loss 0.0817207619928416 learn_rate 0.251459153519702 loss 0.0817152582813465 learn_rate 0.264032111195687 loss 0.0817110273246944 learn_rate 0.277233716755471 loss 0.0817077251200145 learn_rate 0.291095402593245 loss 0.0817051271803207 learn_rate 0.305650172722907 loss 0.0817030863823045 learn_rate 0.320932681359052 loss 0.0817015057416592 learn_rate 0.336979315427005 loss 0.0817003216833805 learn_rate 0.353828281198355 loss 0.0816994942457146 learn_rate 0.371519695258273 loss 0.0816990015537833 learn_rate 0.390095680021186 loss 0.0816988367179628 learn_rate 0.409600464022246 loss 0.0816990059883603 learn_rate 0.204800232011123 loss 0.0816878755677738 learn_rate 0.215040243611679 loss 0.0816796296617755 learn_rate 0.225792255792263 loss 0.0816734218521286 learn_rate 0.237081868581876 loss 0.081668665298727 learn_rate 0.24893596201097 loss 0.0816649605033018 learn_rate 0.261382760111519 loss 0.0816620392924233 learn_rate 0.274451898117094 loss 0.0816597231364342 learn_rate 0.288174493022949 loss 0.0816578934835717 learn_rate 0.302583217674097 loss 0.0816564716379639 learn_rate 0.317712378557802 loss 0.0816554058051717 learn_rate 0.333597997485692 loss 0.0816546632044679 learn_rate 0.350277897359976 loss 0.0816542255285453 learn_rate 0.367791792227975 loss 0.0816540864479526 learn_rate 0.386181381839374 loss 0.0816542502522891 learn_rate 0.193090690919687 loss 0.0816451538678175 learn_rate 0.202745225465671 loss 0.0816382648952768 learn_rate 0.212882486738955 loss 0.0816329777477581 learn_rate 0.223526611075903 loss 0.0816288600059819 learn_rate 0.234702941629698 loss 0.0816256090302056 learn_rate 0.246438088711183 loss 0.0816230166394199 learn_rate 0.258759993146742 loss 0.0816209415274668 learn_rate 0.271697992804079 loss 0.0816192886544872 learn_rate 0.285282892444283 loss 0.0816179945723711 learn_rate 0.299547037066497 loss 0.0816170175157294 learn_rate 0.314524388919822 loss 0.0816163310975053 learn_rate 0.330250608365813 loss 0.0816159205592879 learn_rate 0.346763138784104 loss 0.0816157807028041 learn_rate 0.364101295723309 loss 0.0816159148334781 learn_rate 0.182050647861654 loss 0.0816084601178067 learn_rate 0.191153180254737 loss 0.0816026956869087 learn_rate 0.200710839267474 loss 0.0815981887481914 learn_rate 0.210746381230848 loss 0.0815946216279206 learn_rate 0.22128370029239 loss 0.0815917661180231 learn_rate 0.23234788530701 loss 0.0815894615385591 learn_rate 0.24396527957236 loss 0.081587596757376 learn_rate 0.256163543550978 loss 0.0815860960945961 learn_rate 0.268971720728527 loss 0.0815849087897337 learn_rate 0.282420306764953 loss 0.0815840015382025 learn_rate 0.296541322103201 loss 0.0815833535150335 learn_rate 0.311368388208361 loss 0.0815829532895171 learn_rate 0.326936807618779 loss 0.0815827970800402 learn_rate 0.343283647999718 loss 0.0815828878840102 learn_rate 0.171641823999859 loss 0.0815767616986366 learn_rate 0.180223915199852 loss 0.0815719303950062 learn_rate 0.189235110959845 loss 0.0815680850930738 learn_rate 0.198696866507837 loss 0.0815649931430151 learn_rate 0.208631709833229 loss 0.0815624832625448 learn_rate 0.21906329532489 loss 0.0815604321561861 learn_rate 0.230016460091135 loss 0.081558752993993 learn_rate 0.241517283095691 loss 0.0815573859321803 learn_rate 0.253593147250476 loss 0.081556290680064 learn_rate 0.266272804613 loss 0.081555440971793 learn_rate 0.27958644484365 loss 0.0815548206972213 learn_rate 0.293565767085832 loss 0.0815544213872988 learn_rate 0.308244055440124 loss 0.0815542407328809 learn_rate 0.32365625821213 loss 0.0815542818345251 learn_rate 0.161828129106065 loss 0.0815492333265285 learn_rate 0.169919535561368 loss 0.0815451770976913 learn_rate 0.178415512339437 loss 0.0815418930535807 learn_rate 0.187336287956409 loss 0.0815392113590462 learn_rate 0.196703102354229 loss 0.0815370040589798 learn_rate 0.206538257471941 loss 0.0815351770994433 learn_rate 0.216865170345538 loss 0.0815336630920502 learn_rate 0.227708428862814 loss 0.0815324150580458 learn_rate 0.239093850305955 loss 0.0815314012781314 learn_rate 0.251048542821253 loss 0.0815306012708248 learn_rate 0.263600969962316 loss 0.0815300028352827 learn_rate 0.276781018460431 loss 0.0815296000303334 learn_rate 0.290620069383453 loss 0.0815293919227858 learn_rate 0.305151072852626 loss 0.0815293819241912 learn_rate 0.320408626495257 loss 0.0815295775425765 learn_rate 0.160204313247628 loss 0.0815248261286149 learn_rate 0.16821452891001 loss 0.0815210100629777 learn_rate 0.17662525535551 loss 0.0815179253731432 learn_rate 0.185456518123286 loss 0.0815154140746643 learn_rate 0.19472934402945 loss 0.0815133567313405 learn_rate 0.204465811230923 loss 0.0815116652965742 learn_rate 0.214689101792469 loss 0.0815102765607389 learn_rate 0.225423556882092 loss 0.0815091464362863 learn_rate 0.236694734726197 loss 0.0815082452134663 learn_rate 0.248529471462507 loss 0.0815075538250517 learn_rate 0.260955945035632 loss 0.0815070610770909 learn_rate 0.274003742287414 loss 0.0815067617407232 learn_rate 0.287703929401785 loss 0.0815066553606924 learn_rate 0.302089125871874 loss 0.081506745619398 learn_rate 0.151044562935937 loss 0.0815028013188983 learn_rate 0.158596791082734 loss 0.0814995784350143 learn_rate 0.16652663063687 loss 0.081496931015073 learn_rate 0.174852962168714 loss 0.0814947434448855 learn_rate 0.18359561027715 loss 0.0814929264233823 learn_rate 0.192775390791007 loss 0.0814914128189456 learn_rate 0.202414160330558 loss 0.0814901536488818 learn_rate 0.212534868347085 loss 0.0814891143812433 learn_rate 0.22316161176444 loss 0.0814882717051891 learn_rate 0.234319692352662 loss 0.0814876108582394 learn_rate 0.246035676970295 loss 0.0814871235421859 learn_rate 0.25833746081881 loss 0.081486806409514 learn_rate 0.27125433385975 loss 0.0814866600630817 learn_rate 0.284817050552738 loss 0.0814866884858544 learn_rate 0.142408525276369 loss 0.081483418412021 learn_rate 0.149528951540187 loss 0.0814807018180545 learn_rate 0.157005399117197 loss 0.0814784351192435 learn_rate 0.164855669073056 loss 0.0814765345180117 learn_rate 0.173098452526709 loss 0.0814749339769668 learn_rate 0.181753375153045 loss 0.081473582939283 learn_rate 0.190841043910697 loss 0.0814724439578843 learn_rate 0.200383096106232 loss 0.0814714903813769 learn_rate 0.210402250911543 loss 0.0814707042210333 learn_rate 0.220922363457121 loss 0.0814700742922459 learn_rate 0.231968481629977 loss 0.0814695946892108 learn_rate 0.243566905711475 loss 0.0814692636169381 learn_rate 0.255745250997049 loss 0.0814690825733847 learn_rate 0.268532513546902 loss 0.0814690558493656 learn_rate 0.281959139224247 loss 0.0814691902965629 learn_rate 0.140979569612123 loss 0.0814661092635874 learn_rate 0.14802854809273 loss 0.081463549479768 learn_rate 0.155429975497366 loss 0.0814614152341618 learn_rate 0.163201474272234 loss 0.0814596288618362 learn_rate 0.171361547985846 loss 0.0814581289920781 learn_rate 0.179929625385138 loss 0.0814568685406571 learn_rate 0.188926106654395 loss 0.0814558125930188 learn_rate 0.198372411987115 loss 0.0814549363152003 learn_rate 0.208291032586471 loss 0.0814542230095934 learn_rate 0.218705584215794 loss 0.0814536624058623 learn_rate 0.229640863426584 loss 0.0814532492463883 learn_rate 0.241122906597913 loss 0.0814529821939889 learn_rate 0.253179051927809 loss 0.0814528630604695 learn_rate 0.265838004524199 loss 0.08145289633051 learn_rate 0.1329190022621 loss 0.0814503162162055 learn_rate 0.139564952375205 loss 0.0814481398678168 learn_rate 0.146543199993965 loss 0.0814462988650052 learn_rate 0.153870359993663 loss 0.0814447365823444 learn_rate 0.161563877993346 loss 0.0814434074259089 learn_rate 0.169642071893014 loss 0.0814422758124757 learn_rate 0.178124175487664 loss 0.0814413149814205 learn_rate 0.187030384262048 loss 0.0814405057313956 learn_rate 0.19638190347515 loss 0.0814398351688452 learn_rate 0.206200998648907 loss 0.081439295544105 learn_rate 0.216511048581353 loss 0.0814388832346114 learn_rate 0.227336601010421 loss 0.0814385979155282 learn_rate 0.238703431060942 loss 0.0814384419379441 learn_rate 0.250638602613989 loss 0.0814384199158377 learn_rate 0.263170532744688 loss 0.0814385385069726 learn_rate 0.131585266372344 loss 0.0814361066536102 learn_rate 0.138164529690961 loss 0.0814340547131523 learn_rate 0.145072756175509 loss 0.0814323196094882 learn_rate 0.152326393984285 loss 0.0814308490003293 learn_rate 0.159942713683499 loss 0.0814296006446145 learn_rate 0.167939849367674 loss 0.0814285415228083 learn_rate 0.176336841836058 loss 0.0814276467906512 learn_rate 0.185153683927861 loss 0.0814268986500571 learn_rate 0.194411368124254 loss 0.0814262852175722 learn_rate 0.204131936530466 loss 0.0814257994615747 learn_rate 0.21433853335699 loss 0.0814254382654563 learn_rate 0.225055460024839 loss 0.0814252016569172 learn_rate 0.236308233026081 loss 0.0814250922251995 learn_rate 0.248123644677385 loss 0.0814251147304484 learn_rate 0.124061822338693 loss 0.0814230681015691 learn_rate 0.130264913455627 loss 0.0814213165888045 learn_rate 0.136778159128409 loss 0.08141981513957 learn_rate 0.143617067084829 loss 0.0814185256711964 learn_rate 0.15079792043907 loss 0.0814174169079869 learn_rate 0.158337816461024 loss 0.0814164640125885 learn_rate 0.166254707284075 loss 0.0814156480562856 learn_rate 0.174567442648279 loss 0.0814149553796885 learn_rate 0.183295814780693 loss 0.0814143768981064 learn_rate 0.192460605519728 loss 0.0814139074044759 learn_rate 0.202083635795714 loss 0.0814135449173492 learn_rate 0.2121878175855 loss 0.0814132901127679 learn_rate 0.222797208464775 loss 0.0814131458679 learn_rate 0.233937068888013 loss 0.0814131169322999 learn_rate 0.245633922332414 loss 0.0814132097308249 learn_rate 0.122816961166207 loss 0.0814112810102533 learn_rate 0.128957809224517 loss 0.0814096295498895 learn_rate 0.135405699685743 loss 0.0814082139493761 learn_rate 0.14217598467003 loss 0.0814069991341228 learn_rate 0.149284783903532 loss 0.0814059562480351 learn_rate 0.156749023098709 loss 0.0814050623533306 learn_rate 0.164586474253644 loss 0.0814042999764623 learn_rate 0.172815797966326 loss 0.0814036565461283 learn_rate 0.181456587864643 loss 0.0814031237726742 learn_rate 0.190529417257875 loss 0.0814026970176738 learn_rate 0.200055888120768 loss 0.0814023746982443 learn_rate 0.210058682526807 loss 0.0814021577632742 learn_rate 0.220561616653147 loss 0.081402049269058 learn_rate 0.231589697485805 loss 0.0814020540709469 learn_rate 0.115794848742902 loss 0.0814004223493973 learn_rate 0.121584591180047 loss 0.0813990066168276 learn_rate 0.12766382073905 loss 0.0813977773441154 learn_rate 0.134047011776002 loss 0.0813967090751226 learn_rate 0.140749362364802 loss 0.0813957805222832 learn_rate 0.147786830483043 loss 0.081394974516064 learn_rate 0.155176172007195 loss 0.081394277826699 learn_rate 0.162934980607554 loss 0.0813936808835775 learn_rate 0.171081729637932 loss 0.0813931774227093 learn_rate 0.179635816119829 loss 0.0813927640953564 learn_rate 0.18861760692582 loss 0.0813924400709997 learn_rate 0.198048487272111 loss 0.0813922066653422 learn_rate 0.207950911635717 loss 0.0813920670193685 learn_rate 0.218348457217503 loss 0.0813920258490885 learn_rate 0.229265880078378 loss 0.0813920892782491 learn_rate 0.114632940039189 loss 0.0813905522646921 learn_rate 0.120364587041148 loss 0.0813892177833702 learn_rate 0.126382816393206 loss 0.0813880588068253 learn_rate 0.132701957212866 loss 0.0813870519936354 learn_rate 0.139337055073509 loss 0.0813861778019536 learn_rate 0.146303907827185 loss 0.0813854204680201 learn_rate 0.153619103218544 loss 0.0813847678651117 learn_rate 0.161300058379471 loss 0.0813842112650745 learn_rate 0.169365061298445 loss 0.0813837450295789 learn_rate 0.177833314363367 loss 0.0813833662611305 learn_rate 0.186724980081535 loss 0.0813830744444142 learn_rate 0.196061229085612 loss 0.081382871106733 learn_rate 0.205864290539893 loss 0.0813827595223711 learn_rate 0.216157505066888 loss 0.0813827444801125 learn_rate 0.226965380320232 loss 0.0813828321265078 learn_rate 0.113482690160116 loss 0.0813813660612965 learn_rate 0.119156824668122 loss 0.0813800930188619 learn_rate 0.125114665901528 loss 0.0813789877944725 learn_rate 0.131370399196604 loss 0.0813780285683355 learn_rate 0.137938919156434 loss 0.0813771970357299 learn_rate 0.144835865114256 loss 0.0813764784105475 learn_rate 0.152077658369969 loss 0.0813758613152816 learn_rate 0.159681541288467 loss 0.0813753375769577 learn_rate 0.167665618352891 loss 0.0813749019534886 learn_rate 0.176048899270535 loss 0.0813745518180396 learn_rate 0.184851344234062 loss 0.0813742868299299 learn_rate 0.194093911445765 loss 0.0813741086193447 learn_rate 0.203798607018054 loss 0.0813740205098219 learn_rate 0.213988537368956 loss 0.081374027297541 learn_rate 0.106994268684478 loss 0.0813727789044027 learn_rate 0.112343982118702 loss 0.0813716815594405 learn_rate 0.117961181224637 loss 0.0813707173210673 learn_rate 0.123859240285869 loss 0.0813698704005888 learn_rate 0.130052202300162 loss 0.081369127351012 learn_rate 0.136554812415171 loss 0.0813684771665982 learn_rate 0.143382553035929 loss 0.0813679112965661 learn_rate 0.150551680687726 loss 0.0813674235815153 learn_rate 0.158079264722112 loss 0.0813670101257384 learn_rate 0.165983227958217 loss 0.0813666691222693 learn_rate 0.174282389356128 loss 0.0813664006498768 learn_rate 0.182996508823935 loss 0.0813662064621371 learn_rate 0.192146334265132 loss 0.0813660897880645 learn_rate 0.201753650978388 training_time 00:00:00.1035790 memory 0 Save model to model.txt === END program1: ./run learn ../dataset2/train --- OK [1s] ===== MAIN: predict/evaluate on train data ===== === START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in === END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [1s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out loading_time 0.1 ratings range: [0, 4] training data: 3 users, 3 items, 5 ratings, sparsity 44.44444 test data: 3 users, 3 items, 5 ratings, sparsity 44.44444 Load model from model.txt Set num_factors to 3 BiasedMatrixFactorization num_factors=20 bias_reg=0.0001 reg_u=0.015 reg_i=0.015 learn_rate=0.01 num_iter=30 bold_driver=False init_mean=0 init_stdev=0.1 optimize_mae=False RMSE 3.14512 MAE 2.96238 NMAE 0.7406 testing_time 00:00:00.0030250 predicting_time 00:00:00.0035250 memory 0 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [2s] ===== MAIN: predict/evaluate on test data ===== === START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in === END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [1s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out loading_time 0.03 ratings range: [0, 4] training data: 3 users, 3 items, 5 ratings, sparsity 44.44444 test data: 3 users, 3 items, 4 ratings, sparsity 55.55556 Load model from model.txt Set num_factors to 3 BiasedMatrixFactorization num_factors=20 bias_reg=0.0001 reg_u=0.015 reg_i=0.015 learn_rate=0.01 num_iter=30 bold_driver=False init_mean=0 init_stdev=0.1 optimize_mae=False RMSE 2.03317 MAE 1.21928 NMAE 0.30482 testing_time 00:00:00.0028570 predicting_time 00:00:00.0035820 memory 0 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 0m9.900s user 0m7.244s sys 0m2.308s
Run specification 
Results
Must be logged in to post comments.