From f6a86cfbe974cccb7f6aa3436138413c5e3dcd47 Mon Sep 17 00:00:00 2001 From: saeedkhosravi94 Date: Mon, 1 Dec 2025 00:10:33 +0100 Subject: [PATCH] editing readme.md --- README.md | 16 +- __pycache__/train.cpython-312.pyc | Bin 0 -> 5601 bytes catboost_info/catboost_training.json | 504 +++++++++++++++++++++++ catboost_info/learn/events.out.tfevents | Bin 0 -> 27370 bytes catboost_info/learn_error.tsv | 501 ++++++++++++++++++++++ catboost_info/time_left.tsv | 501 ++++++++++++++++++++++ lgbm_vs_cat_kmeans_smote_k10_results.csv | 5 + runner.py | 497 +++++++++++----------- train.py | 2 +- 9 files changed, 1789 insertions(+), 237 deletions(-) create mode 100644 __pycache__/train.cpython-312.pyc create mode 100644 catboost_info/catboost_training.json create mode 100644 catboost_info/learn/events.out.tfevents create mode 100644 catboost_info/learn_error.tsv create mode 100644 catboost_info/time_left.tsv create mode 100644 lgbm_vs_cat_kmeans_smote_k10_results.csv diff --git a/README.md b/README.md index 61be949..0390c8d 100644 --- a/README.md +++ b/README.md @@ -88,12 +88,24 @@ We are dealing with an exteremly imbalance dataset related to electrocardiogram #### Costly: Healthy → predicted sick : high precision score or low FP +## STEP 5: + + +Current results taken: + +| model | stage | accuracy | f1_macro | f2_macro | recall_macro | precision_macro | f1_class0 | f1_class1 | f2_class0 | f2_class1 | recall_class0 | recall_class1 | precision_class0 | precision_class1 | TP | TN | FP | FN | +|-----------------------|-------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|-----|-------|----|----| +| CatBoost_balanced | train | 0.9843784049402589 | 0.8696686267343388 | 0.8824472728294012 | 0.8916952848998795 | 0.8508242781484853 | 0.9919396338322237 | 0.7473976196364541 | 0.9908276010500254 | 0.7740669446087769 | 0.9900881006639566 | 0.7933024691358025 | 0.9938004847319636 | 0.7078480715650071 | 789 | 26898 | 140 | 19 | +| CatBoost_balanced | test | 0.9802604802604803 | 0.8348421298822796 | 0.8461546793313885 | 0.8541662696976049 | 0.8176680164072361 | 0.9898162729658793 | 0.6798679867986799 | 0.988757446094471 | 0.703551912568306 | 0.9880528191154894 | 0.7202797202797203 | 0.991586032814472 | 0.64375 | 103 | 4714 | 57 | 40 | +| LGBM_KMEANS_SMOTE | train | 0.9883286128479746 | 0.8784419356817057 | 0.8436008106620193 | 0.8240767336379762 | 0.9582821430574249 | 0.9940169232360254 | 0.7628669481273861 | 0.9966698960611392 | 0.6905317252628993 | 0.9984466771524954 | 0.6497067901234568 | 0.9896275269971563 | 0.9269367591176938 | 775 | 27036 | 2 | 33 | +| LGBM_KMEANS_SMOTE | test | 0.9865689865689866 | 0.8543196878009516 | 0.8121616449258658 | 0.7895809912158687 | 0.9600745182511498 | 0.9931221342225928 | 0.7155172413793104 | 0.9964866786565728 | 0.6278366111951589 | 0.9987424020121568 | 0.5804195804195804 | 0.9875647668393782 | 0.9325842696629213 | 83 | 4765 | 6 | 60 | + ## next steps: ``` ✅ 1. Stratified K-fold only apply on train. -🗹 2. train LGBM model using KMEANS_SMOTE with k_neighbors=10 -🗹 3. train Cat_boost using KMEANS_SMOTE with k_neighbors=10 +🗹 2. train LGBM model using KMEANS_SMOTE with k_neighbors=10 (fine-tune remained) +🗹 3. train Cat_boost using KMEANS_SMOTE with k_neighbors=10 (fine-tune remained) 🗹 4. implement proposed methods of this article : https://1drv.ms/b/c/ab2a38fe5c318317/IQBEDsSFcYj6R6AMtOnh0X6DAZUlFqAYq19WT8nTeXomFwg 🗹 5. compare proposed model with SMOTE vs oversampling balancing method ``` \ No newline at end of file diff --git a/__pycache__/train.cpython-312.pyc b/__pycache__/train.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4c3f2b51b02e5174facb92d8e0e778d26198c0d1 GIT binary patch literal 5601 zcma)AOKcm*8Q$e%xqL1ukrba6Wm!t;mn^^IJXJ@E?O1jKNw&(`CDz=PL}^LNvnwSE zmVi*?U>5}>0`uSl1q%ofpa6UG(M5ZZ%q>K02vkLN@lAzY1n8xmS?-z-(=i8} zf98MAoqHIAF*t|Csx2PFA+O_fDSb?z zGQX2F@A-| zoDA_57IQ7(QSuoY_Da+!6GI;jjs4<^Y#bZCIzB9uW7kHjJ?iHfz_5TzrrD~%`pGCu zr+I~ejt)AJSO9clDb6P{fR)LqLA7U{y2T2#%24xwO(gkbCanr=kf%AW%0+RR8WR95 zB&U)r^T|ktV-hBHM^Hn-s)zOYB@*q9fFmI)qMpG;!-nn=(!!E8O=DRSRx_*?Sgo+y zV70^QfR%#P39Ad%4p=*3bu;>`M^GbRm|(2N!mwF`)~mImX|{f5szSs?-6J)J&;rh~ zxiE$eqc%%Mzd>l$A7LxHo8M#D4d|WqDAb$n67gB(tTaXuy?!Wojjjvnvi_`3)cu5S zYhBehm<@XftVpx?J4#D*@ zh7CrrYkLAC+>twGlpjLj+2V;CXF?fj2~Osu8*xBDm^S;Sf+#l)`j~#2>M5 zb&u4u`)gbb%?34XtrdM@K#QQ91|Q4{HXE*4XuWz%9D_F9nx@u@er>#LSR`kAY8tI4 z^lCjZD2BC>H7^*~+|sMB-iztnaBJDG^R%?|e5=9J1AV=Z)aR_u*V@uI+Th!IR9j2W zLv484TY5IGv}?oHVx^8YJoPO-sg|D2d(^ezZ?Q+twtV$1eO)bm8~3;MT07eCY`xab zmY$87X+5U94bRqNdKmqe@NJg$tmuCGCj%xN(ayo_QLR^NMe>pQbiTpP+@_tSO*?Cw zcD6R{>}}dP+O(tEv~#v;=W5fgqfNWcHtpP+9TWJ*K88huSH`1ynS_{Ro=Xb6Y~p7Y zrlvSnwgEcLWKwZnpau3BIuu_z#sQs7C)3jr1W)*=PBsb|f#zfb!}1A;V2sch2r@C3 zLip$zy1%FYa37UFe*j0Z&c)Mga(d=g2Jo^!!7cCt3*xtEj!q}oxG)1)eg*~x=uU*O zQW-cfLLyNe@WG}kC;7#cqLM8zj*814*+eh0fSzWbA^5VclMM*9f&FMmMS{!b`3xWD=vyqet^??G2+;d! z-7b?F_ES)w5)k%|$E!UJ;Z?ah09X~6Ay7v!_jpaJb_Zp<3KmuS26j+( z*07~&&@35@`T)W-vYAaUq~LM|RyGK?nH1b;lzD)_jBH49nS^ZQGM}>mAdm;py#!Da z0D%aCh_dAx%`LFQ0Azq{n@S3Cm^eB$4-btVa-o7U*{?2}DjJate{tkiaa%qZ)?`kj)UbC)4pH z^M!0$q`7KmGOkEv<76DlWYdzyFID;Kh5?yailgn~_raKvpDh19G%&%lfFIy#mSqM$ zViyO7IW{4HOo9eXGBXY6)POSRQMjkx>ZFt%jenjLX5w>b!r(d_#{nz?Fi4eqi+cPQub zh24WEDx~de^Ih{w@4e`1bZuzu_U{Mso!^DlL!0kjmdMe~iEBUVb%%&O43aIf{|>p; z(ZBNc+B6(15Paah@6DOF{0H+x-;Jz~6wa()UGdy8mHmO&X#Qz+mZ{)2)w4zQl&Id^ z7f-2yN}#VC*0JE;lJdPhF=ffoOq#%t?WDeYjTV&{)U*(4jr%T~;68TQ~rEoIG=C2j6OW|RO9I1(anfDZk z;%84xQutGe{H#p+x5>yB8F?7WvxRGquS-3n5_zRehPKJxEwcCF)xvP`^pi8vfia03 z-yvPwq;HG#edEjZ<-Ns0$^V{2zF%%Fu2~0j6Zw&%>oG0)FG%FYnthX^wxk>RzJmAh zVDs!7#5<&Ko9x~qyB|^oV`1@$ONx9Vk(bM@#XF>PW$3Q`H{>3T!z1=$3dcNwdzV%( ztz}E@BNey5b>A0$V7YJE>^)iXovH*Q56;~`x7mNL6nv)wy<@AfoVnyV*jC@a??K>x zAU9g_AFtH+Lp={J-M_T?)|pc1Y^9$0chF&j&x6BAJcmyymU#J1u0FSjfBrqZj_;xG z9Tlq~ORb2wcEe}&wJ&;6C+f8?o2;SseUIb}Uia^TQ9!SKr6@Y2HE75?O9<#SUenfl zngsMtZ>=MnMl`~&e|2S9bCb?gr?b2=8{-D~ayHgiw5B?(t!b|HW7?Z^^}ZL?>fbu$JWth`{{;J&5!oasUy8F+e{e9f-jH z7BGN5L!t!u0QFyYeMVj2Jk-ksylYr^0m7>UwO|4{9Yh3uSb)QbAkYsG-f#DQ;}HmZ z2N?RNF&7W0dbT~gP1 zcym=6S$G9`!ew^|{d@(v0xJJLDT@ye;UtF0>*(CNjks~ zY`|GaWN$Kci(_e!KAM4`Gp@h@h^Q4{)eN@KlPFXCT-6@pR*+2a&Bp;fbPA-ByIxu% z7!~JPgbv`}jSB8lE`B9j>zFdnYAVThVHy`W_I+>*#zC(W9)X=bJ&xl)V74DH>p!r` mzhjd>>Ihu-9}E!(hM(Lcu?zU8_;dYr94DR&x?Y@6g8m-@fYS5; literal 0 HcmV?d00001 diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json new file mode 100644 index 0000000..541b534 --- /dev/null +++ b/catboost_info/catboost_training.json @@ -0,0 +1,504 @@ +{ +"meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":500,"learn_sets":["learn"],"name":"experiment"}, +"iterations":[ +{"learn":[0.6505594592],"iteration":0,"passed_time":0.03105403676,"remaining_time":15.49596434}, +{"learn":[0.6199570308],"iteration":1,"passed_time":0.05615028788,"remaining_time":13.98142168}, +{"learn":[0.5883839207],"iteration":2,"passed_time":0.07864994549,"remaining_time":13.0296743}, +{"learn":[0.5581651741],"iteration":3,"passed_time":0.1024556292,"remaining_time":12.70449802}, +{"learn":[0.5343091005],"iteration":4,"passed_time":0.1254755472,"remaining_time":12.42207917}, +{"learn":[0.5070162395],"iteration":5,"passed_time":0.1492401884,"remaining_time":12.28744218}, +{"learn":[0.4927200876],"iteration":6,"passed_time":0.1737518445,"remaining_time":12.23709419}, +{"learn":[0.4682830337],"iteration":7,"passed_time":0.1985714651,"remaining_time":12.2121451}, +{"learn":[0.4539977069],"iteration":8,"passed_time":0.22143513,"remaining_time":12.08051653}, +{"learn":[0.4430063307],"iteration":9,"passed_time":0.2448470975,"remaining_time":11.99750778}, +{"learn":[0.4260619942],"iteration":10,"passed_time":0.26909804,"remaining_time":11.96263105}, +{"learn":[0.4123586166],"iteration":11,"passed_time":0.2945448815,"remaining_time":11.97815851}, +{"learn":[0.3986592355],"iteration":12,"passed_time":0.3204721075,"remaining_time":12.00537818}, +{"learn":[0.3898627233],"iteration":13,"passed_time":0.3453221871,"remaining_time":11.98761307}, +{"learn":[0.3805898091],"iteration":14,"passed_time":0.3702246843,"remaining_time":11.97059813}, +{"learn":[0.3714802352],"iteration":15,"passed_time":0.3938229889,"remaining_time":11.91314541}, +{"learn":[0.3635447217],"iteration":16,"passed_time":0.4161642267,"remaining_time":11.82396009}, +{"learn":[0.3579714002],"iteration":17,"passed_time":0.4397888234,"remaining_time":11.77656738}, +{"learn":[0.3518505418],"iteration":18,"passed_time":0.4639988485,"remaining_time":11.74649716}, +{"learn":[0.3469646375],"iteration":19,"passed_time":0.4877384475,"remaining_time":11.70572274}, +{"learn":[0.342350397],"iteration":20,"passed_time":0.5123099798,"remaining_time":11.68554668}, +{"learn":[0.3380417078],"iteration":21,"passed_time":0.5373129791,"remaining_time":11.67434564}, +{"learn":[0.3336186075],"iteration":22,"passed_time":0.5607558639,"remaining_time":11.629589}, +{"learn":[0.3298240945],"iteration":23,"passed_time":0.5846985086,"remaining_time":11.59652042}, +{"learn":[0.3257224063],"iteration":24,"passed_time":0.6079887237,"remaining_time":11.55178575}, +{"learn":[0.320477237],"iteration":25,"passed_time":0.6313950661,"remaining_time":11.51081774}, +{"learn":[0.3169914291],"iteration":26,"passed_time":0.6559140557,"remaining_time":11.49064253}, +{"learn":[0.3120681587],"iteration":27,"passed_time":0.6803675439,"remaining_time":11.46905288}, +{"learn":[0.3094016032],"iteration":28,"passed_time":0.7053136254,"remaining_time":11.45526612}, +{"learn":[0.304723249],"iteration":29,"passed_time":0.7290930169,"remaining_time":11.42245726}, +{"learn":[0.3011723022],"iteration":30,"passed_time":0.7546858196,"remaining_time":11.41766611}, +{"learn":[0.2987501309],"iteration":31,"passed_time":0.7790562646,"remaining_time":11.39369787}, +{"learn":[0.2972378553],"iteration":32,"passed_time":0.8033529164,"remaining_time":11.36866097}, +{"learn":[0.2949280448],"iteration":33,"passed_time":0.8292874343,"remaining_time":11.36611601}, +{"learn":[0.2922181334],"iteration":34,"passed_time":0.8548019437,"remaining_time":11.3566544}, +{"learn":[0.2902886884],"iteration":35,"passed_time":0.8790401361,"remaining_time":11.32985064}, +{"learn":[0.2868615526],"iteration":36,"passed_time":0.9020597207,"remaining_time":11.28793651}, +{"learn":[0.2855207315],"iteration":37,"passed_time":0.9252963514,"remaining_time":11.24965564}, +{"learn":[0.2829764561],"iteration":38,"passed_time":0.9488582802,"remaining_time":11.21599147}, +{"learn":[0.2790864362],"iteration":39,"passed_time":0.9725538367,"remaining_time":11.18436912}, +{"learn":[0.2771876663],"iteration":40,"passed_time":0.9956992988,"remaining_time":11.14697508}, +{"learn":[0.274396297],"iteration":41,"passed_time":1.020353624,"remaining_time":11.12671333}, +{"learn":[0.269695974],"iteration":42,"passed_time":1.045984095,"remaining_time":11.11662166}, +{"learn":[0.2674762223],"iteration":43,"passed_time":1.068509211,"remaining_time":11.07364091}, +{"learn":[0.266351576],"iteration":44,"passed_time":1.091556296,"remaining_time":11.036847}, +{"learn":[0.2645715787],"iteration":45,"passed_time":1.113948243,"remaining_time":10.99418484}, +{"learn":[0.2629624082],"iteration":46,"passed_time":1.137601257,"remaining_time":10.96453978}, +{"learn":[0.2622682603],"iteration":47,"passed_time":1.160066498,"remaining_time":10.92395952}, +{"learn":[0.2610050288],"iteration":48,"passed_time":1.185722802,"remaining_time":10.91348946}, +{"learn":[0.2591143021],"iteration":49,"passed_time":1.209731531,"remaining_time":10.88758378}, +{"learn":[0.2572681325],"iteration":50,"passed_time":1.233362378,"remaining_time":10.85842564}, +{"learn":[0.2562124872],"iteration":51,"passed_time":1.258443962,"remaining_time":10.84197875}, +{"learn":[0.2551455095],"iteration":52,"passed_time":1.280135895,"remaining_time":10.79661783}, +{"learn":[0.254392464],"iteration":53,"passed_time":1.305388733,"remaining_time":10.78154398}, +{"learn":[0.2528077948],"iteration":54,"passed_time":1.329479755,"remaining_time":10.75669984}, +{"learn":[0.2513889396],"iteration":55,"passed_time":1.353478943,"remaining_time":10.73115448}, +{"learn":[0.2506161354],"iteration":56,"passed_time":1.377812096,"remaining_time":10.70825892}, +{"learn":[0.2494460772],"iteration":57,"passed_time":1.398895142,"remaining_time":10.66054573}, +{"learn":[0.2485459323],"iteration":58,"passed_time":1.422329859,"remaining_time":10.63131302}, +{"learn":[0.2469745034],"iteration":59,"passed_time":1.446372548,"remaining_time":10.60673202}, +{"learn":[0.2459537181],"iteration":60,"passed_time":1.468595408,"remaining_time":10.56907187}, +{"learn":[0.2453627957],"iteration":61,"passed_time":1.490412886,"remaining_time":10.52904587}, +{"learn":[0.2433912548],"iteration":62,"passed_time":1.513328343,"remaining_time":10.49721406}, +{"learn":[0.2422951861],"iteration":63,"passed_time":1.537322947,"remaining_time":10.47301258}, +{"learn":[0.2400155969],"iteration":64,"passed_time":1.561566473,"remaining_time":10.45048332}, +{"learn":[0.2390895846],"iteration":65,"passed_time":1.585986169,"remaining_time":10.42906057}, +{"learn":[0.2375225747],"iteration":66,"passed_time":1.610181194,"remaining_time":10.40609637}, +{"learn":[0.2362798202],"iteration":67,"passed_time":1.636099003,"remaining_time":10.39404073}, +{"learn":[0.2348801591],"iteration":68,"passed_time":1.659691516,"remaining_time":10.3670586}, +{"learn":[0.234274226],"iteration":69,"passed_time":1.683029898,"remaining_time":10.33861223}, +{"learn":[0.2331038168],"iteration":70,"passed_time":1.706093401,"remaining_time":10.30864886}, +{"learn":[0.2323492401],"iteration":71,"passed_time":1.730072838,"remaining_time":10.28432187}, +{"learn":[0.2316465084],"iteration":72,"passed_time":1.754562494,"remaining_time":10.26298883}, +{"learn":[0.2299897258],"iteration":73,"passed_time":1.778226924,"remaining_time":10.23681986}, +{"learn":[0.2292043653],"iteration":74,"passed_time":1.801190591,"remaining_time":10.20674668}, +{"learn":[0.2281960167],"iteration":75,"passed_time":1.825801874,"remaining_time":10.18605256}, +{"learn":[0.2271872251],"iteration":76,"passed_time":1.850385615,"remaining_time":10.16510539}, +{"learn":[0.2266875911],"iteration":77,"passed_time":1.875686287,"remaining_time":10.14794376}, +{"learn":[0.2256819597],"iteration":78,"passed_time":1.899642849,"remaining_time":10.12341316}, +{"learn":[0.2248734679],"iteration":79,"passed_time":1.924752267,"remaining_time":10.1049494}, +{"learn":[0.2235711296],"iteration":80,"passed_time":1.94802419,"remaining_time":10.07681649}, +{"learn":[0.2225638016],"iteration":81,"passed_time":1.971505867,"remaining_time":10.04987137}, +{"learn":[0.2216924707],"iteration":82,"passed_time":1.994628121,"remaining_time":10.02120393}, +{"learn":[0.2206671726],"iteration":83,"passed_time":2.016718978,"remaining_time":9.987560654}, +{"learn":[0.2198053434],"iteration":84,"passed_time":2.041628101,"remaining_time":9.967948962}, +{"learn":[0.2180945036],"iteration":85,"passed_time":2.065207905,"remaining_time":9.941814798}, +{"learn":[0.2174988086],"iteration":86,"passed_time":2.089190717,"remaining_time":9.917652484}, +{"learn":[0.2167547953],"iteration":87,"passed_time":2.113248156,"remaining_time":9.893843638}, +{"learn":[0.2146076599],"iteration":88,"passed_time":2.139377261,"remaining_time":9.879596115}, +{"learn":[0.21355014],"iteration":89,"passed_time":2.162607891,"remaining_time":9.851880394}, +{"learn":[0.2120397433],"iteration":90,"passed_time":2.186820333,"remaining_time":9.828676003}, +{"learn":[0.2113293421],"iteration":91,"passed_time":2.211540868,"remaining_time":9.807702982}, +{"learn":[0.2106641679],"iteration":92,"passed_time":2.2348375,"remaining_time":9.780417878}, +{"learn":[0.2103582924],"iteration":93,"passed_time":2.258997524,"remaining_time":9.756946755}, +{"learn":[0.2100276146],"iteration":94,"passed_time":2.283395637,"remaining_time":9.734476135}, +{"learn":[0.2094603169],"iteration":95,"passed_time":2.307091443,"remaining_time":9.709009823}, +{"learn":[0.208520409],"iteration":96,"passed_time":2.332533326,"remaining_time":9.690834334}, +{"learn":[0.2081060903],"iteration":97,"passed_time":2.35668535,"remaining_time":9.667219497}, +{"learn":[0.2075147098],"iteration":98,"passed_time":2.380415491,"remaining_time":9.641884967}, +{"learn":[0.2063558526],"iteration":99,"passed_time":2.404065671,"remaining_time":9.616262685}, +{"learn":[0.2055862444],"iteration":100,"passed_time":2.428021233,"remaining_time":9.59188586}, +{"learn":[0.204938244],"iteration":101,"passed_time":2.450523349,"remaining_time":9.561846008}, +{"learn":[0.2041199746],"iteration":102,"passed_time":2.474099945,"remaining_time":9.536093961}, +{"learn":[0.20357568],"iteration":103,"passed_time":2.497499162,"remaining_time":9.509708347}, +{"learn":[0.2029479812],"iteration":104,"passed_time":2.518867297,"remaining_time":9.475738879}, +{"learn":[0.2024233181],"iteration":105,"passed_time":2.542382266,"remaining_time":9.449986915}, +{"learn":[0.2018709313],"iteration":106,"passed_time":2.567375765,"remaining_time":9.429707251}, +{"learn":[0.2007005578],"iteration":107,"passed_time":2.590388183,"remaining_time":9.402149702}, +{"learn":[0.1995046398],"iteration":108,"passed_time":2.613561688,"remaining_time":9.375253394}, +{"learn":[0.1988329196],"iteration":109,"passed_time":2.638674064,"remaining_time":9.355298955}, +{"learn":[0.1980569761],"iteration":110,"passed_time":2.661896361,"remaining_time":9.328627788}, +{"learn":[0.197332958],"iteration":111,"passed_time":2.685906882,"remaining_time":9.304748842}, +{"learn":[0.1968751476],"iteration":112,"passed_time":2.711071926,"remaining_time":9.284821554}, +{"learn":[0.196281669],"iteration":113,"passed_time":2.73628168,"remaining_time":9.264953758}, +{"learn":[0.1954746084],"iteration":114,"passed_time":2.764253363,"remaining_time":9.254239521}, +{"learn":[0.195100344],"iteration":115,"passed_time":2.788790562,"remaining_time":9.231858411}, +{"learn":[0.1947434834],"iteration":116,"passed_time":2.81466212,"remaining_time":9.213808478}, +{"learn":[0.1940842572],"iteration":117,"passed_time":2.840468552,"remaining_time":9.195415143}, +{"learn":[0.193016598],"iteration":118,"passed_time":2.865915977,"remaining_time":9.175747791}, +{"learn":[0.192326827],"iteration":119,"passed_time":2.892278503,"remaining_time":9.158881927}, +{"learn":[0.1919624319],"iteration":120,"passed_time":2.916626073,"remaining_time":9.135547781}, +{"learn":[0.1913875196],"iteration":121,"passed_time":2.941569321,"remaining_time":9.11404265}, +{"learn":[0.190963803],"iteration":122,"passed_time":2.965608759,"remaining_time":9.0897114}, +{"learn":[0.1906330299],"iteration":123,"passed_time":2.990774553,"remaining_time":9.068800258}, +{"learn":[0.1902243409],"iteration":124,"passed_time":3.014306065,"remaining_time":9.042918194}, +{"learn":[0.1896069044],"iteration":125,"passed_time":3.038773387,"remaining_time":9.019851164}, +{"learn":[0.1890660639],"iteration":126,"passed_time":3.062043601,"remaining_time":8.993246168}, +{"learn":[0.1882835195],"iteration":127,"passed_time":3.087900243,"remaining_time":8.97421008}, +{"learn":[0.1875493514],"iteration":128,"passed_time":3.113845219,"remaining_time":8.955322297}, +{"learn":[0.1869075794],"iteration":129,"passed_time":3.137285562,"remaining_time":8.929197369}, +{"learn":[0.1864064876],"iteration":130,"passed_time":3.16263411,"remaining_time":8.908488447}, +{"learn":[0.1861340119],"iteration":131,"passed_time":3.185744155,"remaining_time":8.881468553}, +{"learn":[0.1856804614],"iteration":132,"passed_time":3.208130727,"remaining_time":8.852511103}, +{"learn":[0.1851377629],"iteration":133,"passed_time":3.232801094,"remaining_time":8.829889556}, +{"learn":[0.1843016898],"iteration":134,"passed_time":3.256082726,"remaining_time":8.803482926}, +{"learn":[0.1835527038],"iteration":135,"passed_time":3.278891223,"remaining_time":8.775855921}, +{"learn":[0.1830184334],"iteration":136,"passed_time":3.302763366,"remaining_time":8.751117533}, +{"learn":[0.1823413379],"iteration":137,"passed_time":3.327347232,"remaining_time":8.728258682}, +{"learn":[0.1818241981],"iteration":138,"passed_time":3.352467526,"remaining_time":8.706768178}, +{"learn":[0.1810634762],"iteration":139,"passed_time":3.378940304,"remaining_time":8.688703639}, +{"learn":[0.1804669956],"iteration":140,"passed_time":3.402716196,"remaining_time":8.663653292}, +{"learn":[0.1800037734],"iteration":141,"passed_time":3.426736425,"remaining_time":8.639236903}, +{"learn":[0.179229966],"iteration":142,"passed_time":3.452764778,"remaining_time":8.619839342}, +{"learn":[0.1784811687],"iteration":143,"passed_time":3.476910969,"remaining_time":8.595696562}, +{"learn":[0.1776685278],"iteration":144,"passed_time":3.501042451,"remaining_time":8.571517724}, +{"learn":[0.1770219582],"iteration":145,"passed_time":3.524742882,"remaining_time":8.546294386}, +{"learn":[0.1766783294],"iteration":146,"passed_time":3.547484211,"remaining_time":8.518788616}, +{"learn":[0.1762438721],"iteration":147,"passed_time":3.57135048,"remaining_time":8.494022762}, +{"learn":[0.1758425501],"iteration":148,"passed_time":3.59339267,"remaining_time":8.464971994}, +{"learn":[0.1749885611],"iteration":149,"passed_time":3.616867722,"remaining_time":8.439358018}, +{"learn":[0.1738557684],"iteration":150,"passed_time":3.64147013,"remaining_time":8.416377982}, +{"learn":[0.1732595564],"iteration":151,"passed_time":3.665010475,"remaining_time":8.390945034}, +{"learn":[0.1727113418],"iteration":152,"passed_time":3.688104561,"remaining_time":8.364524723}, +{"learn":[0.1719918768],"iteration":153,"passed_time":3.71185641,"remaining_time":8.339625441}, +{"learn":[0.1714453538],"iteration":154,"passed_time":3.737889555,"remaining_time":8.319818687}, +{"learn":[0.1709170131],"iteration":155,"passed_time":3.761972786,"remaining_time":8.295632297}, +{"learn":[0.1700539884],"iteration":156,"passed_time":3.78548638,"remaining_time":8.270202729}, +{"learn":[0.1696775312],"iteration":157,"passed_time":3.809217854,"remaining_time":8.245269026}, +{"learn":[0.169185393],"iteration":158,"passed_time":3.832797158,"remaining_time":8.220024095}, +{"learn":[0.1684625546],"iteration":159,"passed_time":3.856642093,"remaining_time":8.195364448}, +{"learn":[0.1678449693],"iteration":160,"passed_time":3.881884639,"remaining_time":8.173657717}, +{"learn":[0.1674127808],"iteration":161,"passed_time":3.907000432,"remaining_time":8.151642876}, +{"learn":[0.1669160497],"iteration":162,"passed_time":3.931548214,"remaining_time":8.128415632}, +{"learn":[0.1666650109],"iteration":163,"passed_time":3.954377045,"remaining_time":8.10165053}, +{"learn":[0.1661196582],"iteration":164,"passed_time":3.980562901,"remaining_time":8.08174892}, +{"learn":[0.1656309243],"iteration":165,"passed_time":4.004673966,"remaining_time":8.057597015}, +{"learn":[0.1650005575],"iteration":166,"passed_time":4.031946343,"remaining_time":8.039749296}, +{"learn":[0.1630925189],"iteration":167,"passed_time":4.056403165,"remaining_time":8.016225303}, +{"learn":[0.1627982599],"iteration":168,"passed_time":4.080694317,"remaining_time":7.992365792}, +{"learn":[0.1622984813],"iteration":169,"passed_time":4.103059305,"remaining_time":7.96476218}, +{"learn":[0.1617063039],"iteration":170,"passed_time":4.127242621,"remaining_time":7.94071826}, +{"learn":[0.161085537],"iteration":171,"passed_time":4.15240079,"remaining_time":7.91853174}, +{"learn":[0.1605654299],"iteration":172,"passed_time":4.177543792,"remaining_time":7.896282197}, +{"learn":[0.1600969242],"iteration":173,"passed_time":4.201120555,"remaining_time":7.871064948}, +{"learn":[0.1595581484],"iteration":174,"passed_time":4.224817861,"remaining_time":7.846090314}, +{"learn":[0.1591783098],"iteration":175,"passed_time":4.247270893,"remaining_time":7.818839599}, +{"learn":[0.1586366754],"iteration":176,"passed_time":4.274704149,"remaining_time":7.8007313}, +{"learn":[0.1581714089],"iteration":177,"passed_time":4.299976404,"remaining_time":7.778609}, +{"learn":[0.1575630275],"iteration":178,"passed_time":4.329490118,"remaining_time":7.764057698}, +{"learn":[0.1568621859],"iteration":179,"passed_time":4.35938259,"remaining_time":7.750013493}, +{"learn":[0.1563319754],"iteration":180,"passed_time":4.386468548,"remaining_time":7.730847882}, +{"learn":[0.1557732651],"iteration":181,"passed_time":4.424954691,"remaining_time":7.73151424}, +{"learn":[0.15493488],"iteration":182,"passed_time":4.465823049,"remaining_time":7.73587927}, +{"learn":[0.1542286414],"iteration":183,"passed_time":4.500220611,"remaining_time":7.728639744}, +{"learn":[0.1534099833],"iteration":184,"passed_time":4.527682992,"remaining_time":7.709298068}, +{"learn":[0.1522403016],"iteration":185,"passed_time":4.559108703,"remaining_time":7.696559854}, +{"learn":[0.1517637469],"iteration":186,"passed_time":4.586250537,"remaining_time":7.676451433}, +{"learn":[0.1514181606],"iteration":187,"passed_time":4.617491244,"remaining_time":7.663070575}, +{"learn":[0.1511232407],"iteration":188,"passed_time":4.642788374,"remaining_time":7.639720552}, +{"learn":[0.1505915384],"iteration":189,"passed_time":4.667452033,"remaining_time":7.615316475}, +{"learn":[0.1501060085],"iteration":190,"passed_time":4.690511869,"remaining_time":7.588315013}, +{"learn":[0.1493588672],"iteration":191,"passed_time":4.715714164,"remaining_time":7.564791471}, +{"learn":[0.1485757524],"iteration":192,"passed_time":4.73898017,"remaining_time":7.538170529}, +{"learn":[0.1479396924],"iteration":193,"passed_time":4.762746353,"remaining_time":7.512373113}, +{"learn":[0.1476450754],"iteration":194,"passed_time":4.786778958,"remaining_time":7.487013241}, +{"learn":[0.1472334844],"iteration":195,"passed_time":4.809902503,"remaining_time":7.460256943}, +{"learn":[0.1466508463],"iteration":196,"passed_time":4.834416951,"remaining_time":7.435676833}, +{"learn":[0.1459767298],"iteration":197,"passed_time":4.861952084,"remaining_time":7.415704693}, +{"learn":[0.1449640915],"iteration":198,"passed_time":4.888074439,"remaining_time":7.393519628}, +{"learn":[0.1442547887],"iteration":199,"passed_time":4.913456779,"remaining_time":7.370185168}, +{"learn":[0.1435485115],"iteration":200,"passed_time":4.937169961,"remaining_time":7.344347354}, +{"learn":[0.142877376],"iteration":201,"passed_time":4.959637826,"remaining_time":7.316693426}, +{"learn":[0.1424560809],"iteration":202,"passed_time":4.981872478,"remaining_time":7.288749389}, +{"learn":[0.1418883293],"iteration":203,"passed_time":5.007844497,"remaining_time":7.266284172}, +{"learn":[0.1412903231],"iteration":204,"passed_time":5.030112108,"remaining_time":7.238454009}, +{"learn":[0.1403055475],"iteration":205,"passed_time":5.052978648,"remaining_time":7.211532634}, +{"learn":[0.1394721343],"iteration":206,"passed_time":5.075945815,"remaining_time":7.184792869}, +{"learn":[0.1390547172],"iteration":207,"passed_time":5.100007962,"remaining_time":7.159626562}, +{"learn":[0.1386442713],"iteration":208,"passed_time":5.12446045,"remaining_time":7.135014311}, +{"learn":[0.1376316573],"iteration":209,"passed_time":5.148769603,"remaining_time":7.110205642}, +{"learn":[0.136783024],"iteration":210,"passed_time":5.17443199,"remaining_time":7.08725519}, +{"learn":[0.1353549217],"iteration":211,"passed_time":5.19723982,"remaining_time":7.060401265}, +{"learn":[0.1349160868],"iteration":212,"passed_time":5.219840605,"remaining_time":7.033306355}, +{"learn":[0.1338548394],"iteration":213,"passed_time":5.245297572,"remaining_time":7.010070586}, +{"learn":[0.1331950161],"iteration":214,"passed_time":5.268794208,"remaining_time":6.984215577}, +{"learn":[0.1325567202],"iteration":215,"passed_time":5.293958168,"remaining_time":6.960574629}, +{"learn":[0.1315897372],"iteration":216,"passed_time":5.319155755,"remaining_time":6.936963496}, +{"learn":[0.130948128],"iteration":217,"passed_time":5.342938188,"remaining_time":6.911507197}, +{"learn":[0.1305183499],"iteration":218,"passed_time":5.368048564,"remaining_time":6.887770076}, +{"learn":[0.1296405854],"iteration":219,"passed_time":5.392814892,"remaining_time":6.86358259}, +{"learn":[0.1288357946],"iteration":220,"passed_time":5.418255067,"remaining_time":6.84024056}, +{"learn":[0.1279353696],"iteration":221,"passed_time":5.443410653,"remaining_time":6.81652325}, +{"learn":[0.1265519823],"iteration":222,"passed_time":5.46906979,"remaining_time":6.793418528}, +{"learn":[0.1260898397],"iteration":223,"passed_time":5.493449527,"remaining_time":6.768714596}, +{"learn":[0.1252943222],"iteration":224,"passed_time":5.518333274,"remaining_time":6.744629557}, +{"learn":[0.1248322461],"iteration":225,"passed_time":5.542836305,"remaining_time":6.720075874}, +{"learn":[0.1241281529],"iteration":226,"passed_time":5.568137435,"remaining_time":6.696482466}, +{"learn":[0.1232154663],"iteration":227,"passed_time":5.594682673,"remaining_time":6.674358277}, +{"learn":[0.1225610856],"iteration":228,"passed_time":5.618332187,"remaining_time":6.648768658}, +{"learn":[0.1217825847],"iteration":229,"passed_time":5.642942679,"remaining_time":6.624324014}, +{"learn":[0.12119544],"iteration":230,"passed_time":5.665603423,"remaining_time":6.597607449}, +{"learn":[0.1201025465],"iteration":231,"passed_time":5.692312873,"remaining_time":6.575602801}, +{"learn":[0.1193795277],"iteration":232,"passed_time":5.716253059,"remaining_time":6.550384407}, +{"learn":[0.1188654429],"iteration":233,"passed_time":5.738083495,"remaining_time":6.522778674}, +{"learn":[0.1178438479],"iteration":234,"passed_time":5.762168059,"remaining_time":6.497763982}, +{"learn":[0.1173572605],"iteration":235,"passed_time":5.784092539,"remaining_time":6.470340806}, +{"learn":[0.1164264053],"iteration":236,"passed_time":5.808362273,"remaining_time":6.445566573}, +{"learn":[0.1159977674],"iteration":237,"passed_time":5.830803263,"remaining_time":6.418783424}, +{"learn":[0.1154646969],"iteration":238,"passed_time":5.854558279,"remaining_time":6.393471594}, +{"learn":[0.1148100226],"iteration":239,"passed_time":5.880790595,"remaining_time":6.370856477}, +{"learn":[0.1138558555],"iteration":240,"passed_time":5.905832803,"remaining_time":6.346932348}, +{"learn":[0.1130261467],"iteration":241,"passed_time":5.932641838,"remaining_time":6.324882621}, +{"learn":[0.1116842639],"iteration":242,"passed_time":5.958386227,"remaining_time":6.301667738}, +{"learn":[0.1105644217],"iteration":243,"passed_time":5.983179681,"remaining_time":6.277434419}, +{"learn":[0.1097393073],"iteration":244,"passed_time":6.006970781,"remaining_time":6.252153262}, +{"learn":[0.1088130653],"iteration":245,"passed_time":6.031075929,"remaining_time":6.227208479}, +{"learn":[0.1082146647],"iteration":246,"passed_time":6.05573538,"remaining_time":6.202838263}, +{"learn":[0.1076318341],"iteration":247,"passed_time":6.079839694,"remaining_time":6.177901625}, +{"learn":[0.1069338009],"iteration":248,"passed_time":6.105178242,"remaining_time":6.154215818}, +{"learn":[0.1061728523],"iteration":249,"passed_time":6.130897005,"remaining_time":6.130897005}, +{"learn":[0.1057125099],"iteration":250,"passed_time":6.156366347,"remaining_time":6.107311635}, +{"learn":[0.105263911],"iteration":251,"passed_time":6.180644665,"remaining_time":6.082539195}, +{"learn":[0.104622054],"iteration":252,"passed_time":6.206252677,"remaining_time":6.059068819}, +{"learn":[0.1040334688],"iteration":253,"passed_time":6.231044588,"remaining_time":6.034791216}, +{"learn":[0.1034978632],"iteration":254,"passed_time":6.255672205,"remaining_time":6.010351727}, +{"learn":[0.1022838523],"iteration":255,"passed_time":6.280818957,"remaining_time":5.986405569}, +{"learn":[0.1017358698],"iteration":256,"passed_time":6.305317822,"remaining_time":5.961837473}, +{"learn":[0.1009628412],"iteration":257,"passed_time":6.330371114,"remaining_time":5.937789959}, +{"learn":[0.1003992689],"iteration":258,"passed_time":6.354081004,"remaining_time":5.91248464}, +{"learn":[0.0997164366],"iteration":259,"passed_time":6.378524908,"remaining_time":5.887869146}, +{"learn":[0.09903368875],"iteration":260,"passed_time":6.404176671,"remaining_time":5.864361013}, +{"learn":[0.09854662155],"iteration":261,"passed_time":6.429238338,"remaining_time":5.840300475}, +{"learn":[0.09805747523],"iteration":262,"passed_time":6.451193318,"remaining_time":5.813432762}, +{"learn":[0.09741884443],"iteration":263,"passed_time":6.476889623,"remaining_time":5.789946784}, +{"learn":[0.09610540779],"iteration":264,"passed_time":6.503024811,"remaining_time":5.766833323}, +{"learn":[0.09565119136],"iteration":265,"passed_time":6.529094415,"remaining_time":5.743639448}, +{"learn":[0.09510746696],"iteration":266,"passed_time":6.552466423,"remaining_time":5.71806995}, +{"learn":[0.09465424408],"iteration":267,"passed_time":6.576626739,"remaining_time":5.693199267}, +{"learn":[0.09394914967],"iteration":268,"passed_time":6.601929619,"remaining_time":5.669315026}, +{"learn":[0.09341616081],"iteration":269,"passed_time":6.626500568,"remaining_time":5.64479678}, +{"learn":[0.09285369188],"iteration":270,"passed_time":6.650069914,"remaining_time":5.619431772}, +{"learn":[0.09232671453],"iteration":271,"passed_time":6.673907015,"remaining_time":5.59430441}, +{"learn":[0.09171540397],"iteration":272,"passed_time":6.697626072,"remaining_time":5.569088345}, +{"learn":[0.09092693429],"iteration":273,"passed_time":6.723696551,"remaining_time":5.545822702}, +{"learn":[0.09032530539],"iteration":274,"passed_time":6.748984181,"remaining_time":5.521896148}, +{"learn":[0.08961859073],"iteration":275,"passed_time":6.773474045,"remaining_time":5.497312268}, +{"learn":[0.0889928576],"iteration":276,"passed_time":6.797473357,"remaining_time":5.472334147}, +{"learn":[0.08843924734],"iteration":277,"passed_time":6.821897053,"remaining_time":5.447701963}, +{"learn":[0.08792722887],"iteration":278,"passed_time":6.84618433,"remaining_time":5.422963215}, +{"learn":[0.08737190418],"iteration":279,"passed_time":6.870468773,"remaining_time":5.398225465}, +{"learn":[0.08682846822],"iteration":280,"passed_time":6.895059473,"remaining_time":5.373729625}, +{"learn":[0.08609147931],"iteration":281,"passed_time":6.918379022,"remaining_time":5.348250449}, +{"learn":[0.0855848848],"iteration":282,"passed_time":6.943729361,"remaining_time":5.324343715}, +{"learn":[0.0849478236],"iteration":283,"passed_time":6.968742736,"remaining_time":5.300170531}, +{"learn":[0.08443710276],"iteration":284,"passed_time":6.994168285,"remaining_time":5.27630239}, +{"learn":[0.08352754909],"iteration":285,"passed_time":7.01814843,"remaining_time":5.251341833}, +{"learn":[0.08305956533],"iteration":286,"passed_time":7.040939427,"remaining_time":5.225505568}, +{"learn":[0.0823083313],"iteration":287,"passed_time":7.066037303,"remaining_time":5.201388571}, +{"learn":[0.08184267631],"iteration":288,"passed_time":7.089837904,"remaining_time":5.176317639}, +{"learn":[0.08124107118],"iteration":289,"passed_time":7.114132764,"remaining_time":5.151613381}, +{"learn":[0.08074769811],"iteration":290,"passed_time":7.139030136,"remaining_time":5.127344668}, +{"learn":[0.08021947918],"iteration":291,"passed_time":7.162731984,"remaining_time":5.102220043}, +{"learn":[0.0795478537],"iteration":292,"passed_time":7.186983177,"remaining_time":5.077493234}, +{"learn":[0.07916246376],"iteration":293,"passed_time":7.209541169,"remaining_time":5.051583268}, +{"learn":[0.07867604825],"iteration":294,"passed_time":7.232862134,"remaining_time":5.026226229}, +{"learn":[0.07823144468],"iteration":295,"passed_time":7.255230248,"remaining_time":5.000226252}, +{"learn":[0.0778000497],"iteration":296,"passed_time":7.279124058,"remaining_time":4.975293548}, +{"learn":[0.07743135104],"iteration":297,"passed_time":7.302617527,"remaining_time":4.950096445}, +{"learn":[0.07687188002],"iteration":298,"passed_time":7.325879534,"remaining_time":4.924755138}, +{"learn":[0.07654323388],"iteration":299,"passed_time":7.34999689,"remaining_time":4.899997927}, +{"learn":[0.07612356047],"iteration":300,"passed_time":7.372068914,"remaining_time":4.873892737}, +{"learn":[0.07578569588],"iteration":301,"passed_time":7.397275168,"remaining_time":4.84986915}, +{"learn":[0.07528439413],"iteration":302,"passed_time":7.422076413,"remaining_time":4.825574433}, +{"learn":[0.07491501857],"iteration":303,"passed_time":7.445517631,"remaining_time":4.800399525}, +{"learn":[0.07459938027],"iteration":304,"passed_time":7.468580633,"remaining_time":4.774994175}, +{"learn":[0.07434000126],"iteration":305,"passed_time":7.490690199,"remaining_time":4.748999669}, +{"learn":[0.07405502385],"iteration":306,"passed_time":7.512408466,"remaining_time":4.722784476}, +{"learn":[0.07359936025],"iteration":307,"passed_time":7.534048065,"remaining_time":4.696549443}, +{"learn":[0.07321257176],"iteration":308,"passed_time":7.557631661,"remaining_time":4.671545784}, +{"learn":[0.07281411801],"iteration":309,"passed_time":7.580658371,"remaining_time":4.646209969}, +{"learn":[0.0723927812],"iteration":310,"passed_time":7.604700851,"remaining_time":4.621506305}, +{"learn":[0.07195828953],"iteration":311,"passed_time":7.628316531,"remaining_time":4.596549705}, +{"learn":[0.07162083385],"iteration":312,"passed_time":7.650410514,"remaining_time":4.570692543}, +{"learn":[0.07091211511],"iteration":313,"passed_time":7.674291574,"remaining_time":4.545917939}, +{"learn":[0.07035546389],"iteration":314,"passed_time":7.700929689,"remaining_time":4.52276823}, +{"learn":[0.06998280407],"iteration":315,"passed_time":7.72398715,"remaining_time":4.497511505}, +{"learn":[0.06967070096],"iteration":316,"passed_time":7.747017651,"remaining_time":4.472253092}, +{"learn":[0.06930882148],"iteration":317,"passed_time":7.7707525,"remaining_time":4.447411808}, +{"learn":[0.06897063076],"iteration":318,"passed_time":7.795024443,"remaining_time":4.422882208}, +{"learn":[0.06859761419],"iteration":319,"passed_time":7.820585662,"remaining_time":4.399079435}, +{"learn":[0.06823762036],"iteration":320,"passed_time":7.844623642,"remaining_time":4.374416299}, +{"learn":[0.06793421641],"iteration":321,"passed_time":7.867563183,"remaining_time":4.349149834}, +{"learn":[0.06750300536],"iteration":322,"passed_time":7.892254926,"remaining_time":4.324857963}, +{"learn":[0.06715951494],"iteration":323,"passed_time":7.914888878,"remaining_time":4.29944581}, +{"learn":[0.06685166117],"iteration":324,"passed_time":7.937601623,"remaining_time":4.274093182}, +{"learn":[0.06656581837],"iteration":325,"passed_time":7.961838316,"remaining_time":4.249570144}, +{"learn":[0.06596331136],"iteration":326,"passed_time":7.984908443,"remaining_time":4.224431684}, +{"learn":[0.06557079132],"iteration":327,"passed_time":8.008651584,"remaining_time":4.199658757}, +{"learn":[0.06514273361],"iteration":328,"passed_time":8.033376369,"remaining_time":4.175402307}, +{"learn":[0.0648161061],"iteration":329,"passed_time":8.056064947,"remaining_time":4.150094064}, +{"learn":[0.06429278866],"iteration":330,"passed_time":8.080953069,"remaining_time":4.125924679}, +{"learn":[0.06388688686],"iteration":331,"passed_time":8.105029258,"remaining_time":4.101340107}, +{"learn":[0.06356263701],"iteration":332,"passed_time":8.126972821,"remaining_time":4.075689073}, +{"learn":[0.06327858563],"iteration":333,"passed_time":8.149405353,"remaining_time":4.050303259}, +{"learn":[0.06299176056],"iteration":334,"passed_time":8.172558982,"remaining_time":4.025290245}, +{"learn":[0.06268236565],"iteration":335,"passed_time":8.196346832,"remaining_time":4.000597858}, +{"learn":[0.0624306551],"iteration":336,"passed_time":8.218442148,"remaining_time":3.975092196}, +{"learn":[0.06208009921],"iteration":337,"passed_time":8.242736758,"remaining_time":3.950660813}, +{"learn":[0.06180855918],"iteration":338,"passed_time":8.267183163,"remaining_time":3.926302328}, +{"learn":[0.06162921724],"iteration":339,"passed_time":8.288522756,"remaining_time":3.900481297}, +{"learn":[0.06115204937],"iteration":340,"passed_time":8.311136957,"remaining_time":3.875280869}, +{"learn":[0.06057626473],"iteration":341,"passed_time":8.335185312,"remaining_time":3.850758127}, +{"learn":[0.06030054307],"iteration":342,"passed_time":8.356700617,"remaining_time":3.825078708}, +{"learn":[0.05995592477],"iteration":343,"passed_time":8.380787681,"remaining_time":3.800589762}, +{"learn":[0.05966810366],"iteration":344,"passed_time":8.403365632,"remaining_time":3.775425139}, +{"learn":[0.05913088628],"iteration":345,"passed_time":8.427123857,"remaining_time":3.750800792}, +{"learn":[0.05885383971],"iteration":346,"passed_time":8.450990666,"remaining_time":3.726229314}, +{"learn":[0.0586178066],"iteration":347,"passed_time":8.474472052,"remaining_time":3.70149354}, +{"learn":[0.0581001034],"iteration":348,"passed_time":8.498085649,"remaining_time":3.676822157}, +{"learn":[0.05783949145],"iteration":349,"passed_time":8.519921751,"remaining_time":3.651395036}, +{"learn":[0.05741847465],"iteration":350,"passed_time":8.542184946,"remaining_time":3.626169678}, +{"learn":[0.05711831445],"iteration":351,"passed_time":8.565647248,"remaining_time":3.60146532}, +{"learn":[0.05679720458],"iteration":352,"passed_time":8.588951671,"remaining_time":3.576702254}, +{"learn":[0.05657943319],"iteration":353,"passed_time":8.613224281,"remaining_time":3.552346737}, +{"learn":[0.05636778384],"iteration":354,"passed_time":8.636055404,"remaining_time":3.527402911}, +{"learn":[0.05607497286],"iteration":355,"passed_time":8.65967575,"remaining_time":3.502790191}, +{"learn":[0.05587282454],"iteration":356,"passed_time":8.682130782,"remaining_time":3.477716252}, +{"learn":[0.05559949705],"iteration":357,"passed_time":8.705118783,"remaining_time":3.452868344}, +{"learn":[0.05524366884],"iteration":358,"passed_time":8.728642336,"remaining_time":3.42824114}, +{"learn":[0.05488789301],"iteration":359,"passed_time":8.750906405,"remaining_time":3.403130269}, +{"learn":[0.05445960317],"iteration":360,"passed_time":8.776331496,"remaining_time":3.379252294}, +{"learn":[0.05417052086],"iteration":361,"passed_time":8.799956843,"remaining_time":3.35467968}, +{"learn":[0.05393397186],"iteration":362,"passed_time":8.822983761,"remaining_time":3.329886433}, +{"learn":[0.05357891611],"iteration":363,"passed_time":8.846812654,"remaining_time":3.30540253}, +{"learn":[0.05335249446],"iteration":364,"passed_time":8.86857613,"remaining_time":3.280158295}, +{"learn":[0.05317320492],"iteration":365,"passed_time":8.890601153,"remaining_time":3.255028837}, +{"learn":[0.05301985043],"iteration":366,"passed_time":8.913022226,"remaining_time":3.230059826}, +{"learn":[0.05270775281],"iteration":367,"passed_time":8.935795139,"remaining_time":3.205230865}, +{"learn":[0.05243844604],"iteration":368,"passed_time":8.958194669,"remaining_time":3.180280492}, +{"learn":[0.05218227737],"iteration":369,"passed_time":8.982394903,"remaining_time":3.155976587}, +{"learn":[0.05202597967],"iteration":370,"passed_time":9.003933791,"remaining_time":3.130747868}, +{"learn":[0.05168457597],"iteration":371,"passed_time":9.028771995,"remaining_time":3.106674235}, +{"learn":[0.0513787649],"iteration":372,"passed_time":9.0515192,"remaining_time":3.081884553}, +{"learn":[0.05116703418],"iteration":373,"passed_time":9.073754977,"remaining_time":3.056933495}, +{"learn":[0.05093101511],"iteration":374,"passed_time":9.095141029,"remaining_time":3.031713676}, +{"learn":[0.0507589405],"iteration":375,"passed_time":9.117408265,"remaining_time":3.006804853}, +{"learn":[0.05053112856],"iteration":376,"passed_time":9.14141262,"remaining_time":2.982476796}, +{"learn":[0.05033706654],"iteration":377,"passed_time":9.164087822,"remaining_time":2.957721466}, +{"learn":[0.05020300153],"iteration":378,"passed_time":9.186253515,"remaining_time":2.932814447}, +{"learn":[0.05001435435],"iteration":379,"passed_time":9.208657962,"remaining_time":2.907997251}, +{"learn":[0.04975525929],"iteration":380,"passed_time":9.230626984,"remaining_time":2.883056722}, +{"learn":[0.04948290657],"iteration":381,"passed_time":9.25602245,"remaining_time":2.859190181}, +{"learn":[0.04929070362],"iteration":382,"passed_time":9.278065015,"remaining_time":2.834291401}, +{"learn":[0.04910496235],"iteration":383,"passed_time":9.300303501,"remaining_time":2.809466682}, +{"learn":[0.04881316411],"iteration":384,"passed_time":9.324029016,"remaining_time":2.785099576}, +{"learn":[0.04858449122],"iteration":385,"passed_time":9.34674072,"remaining_time":2.760436378}, +{"learn":[0.04830036156],"iteration":386,"passed_time":9.372360523,"remaining_time":2.736632401}, +{"learn":[0.04799888759],"iteration":387,"passed_time":9.394939182,"remaining_time":2.711941207}, +{"learn":[0.04764501811],"iteration":388,"passed_time":9.420174019,"remaining_time":2.688018808}, +{"learn":[0.04735869133],"iteration":389,"passed_time":9.445368564,"remaining_time":2.664078313}, +{"learn":[0.04717676982],"iteration":390,"passed_time":9.467458672,"remaining_time":2.639265972}, +{"learn":[0.04702198439],"iteration":391,"passed_time":9.491415983,"remaining_time":2.614981955}, +{"learn":[0.04680681448],"iteration":392,"passed_time":9.515370837,"remaining_time":2.59069893}, +{"learn":[0.04651695718],"iteration":393,"passed_time":9.539620988,"remaining_time":2.566497017}, +{"learn":[0.04627615035],"iteration":394,"passed_time":9.564574986,"remaining_time":2.542481958}, +{"learn":[0.04606321813],"iteration":395,"passed_time":9.590745592,"remaining_time":2.518781671}, +{"learn":[0.04589309758],"iteration":396,"passed_time":9.614750404,"remaining_time":2.494507032}, +{"learn":[0.04556006451],"iteration":397,"passed_time":9.640125578,"remaining_time":2.470584947}, +{"learn":[0.0453589455],"iteration":398,"passed_time":9.665230704,"remaining_time":2.446587221}, +{"learn":[0.04514104221],"iteration":399,"passed_time":9.689814403,"remaining_time":2.422453601}, +{"learn":[0.04478819136],"iteration":400,"passed_time":9.716095345,"remaining_time":2.398736756}, +{"learn":[0.04443821936],"iteration":401,"passed_time":9.73906397,"remaining_time":2.374199674}, +{"learn":[0.04430866417],"iteration":402,"passed_time":9.761612004,"remaining_time":2.349569142}, +{"learn":[0.0442286461],"iteration":403,"passed_time":9.784576629,"remaining_time":2.325047912}, +{"learn":[0.0441045591],"iteration":404,"passed_time":9.807797468,"remaining_time":2.300594468}, +{"learn":[0.04396148169],"iteration":405,"passed_time":9.830501963,"remaining_time":2.276027548}, +{"learn":[0.04383991606],"iteration":406,"passed_time":9.853293043,"remaining_time":2.251489565}, +{"learn":[0.04369433117],"iteration":407,"passed_time":9.876191375,"remaining_time":2.22698433}, +{"learn":[0.04359398001],"iteration":408,"passed_time":9.900105352,"remaining_time":2.202712927}, +{"learn":[0.04335115657],"iteration":409,"passed_time":9.921932205,"remaining_time":2.177985118}, +{"learn":[0.0432504709],"iteration":410,"passed_time":9.943676473,"remaining_time":2.153253543}, +{"learn":[0.04312499215],"iteration":411,"passed_time":9.965261154,"remaining_time":2.128502382}, +{"learn":[0.0430111312],"iteration":412,"passed_time":9.987106548,"remaining_time":2.103821476}, +{"learn":[0.04290906293],"iteration":413,"passed_time":10.00847627,"remaining_time":2.079055456}, +{"learn":[0.04276421934],"iteration":414,"passed_time":10.03028929,"remaining_time":2.054396601}, +{"learn":[0.04243290185],"iteration":415,"passed_time":10.05547683,"remaining_time":2.030432821}, +{"learn":[0.04207124094],"iteration":416,"passed_time":10.08181365,"remaining_time":2.006691925}, +{"learn":[0.04190756042],"iteration":417,"passed_time":10.10569317,"remaining_time":1.982456554}, +{"learn":[0.04169658039],"iteration":418,"passed_time":10.12932701,"remaining_time":1.958175389}, +{"learn":[0.04139810429],"iteration":419,"passed_time":10.15671773,"remaining_time":1.9346129}, +{"learn":[0.04116312391],"iteration":420,"passed_time":10.18063383,"remaining_time":1.91038022}, +{"learn":[0.0409318057],"iteration":421,"passed_time":10.206051,"remaining_time":1.886426489}, +{"learn":[0.04070910759],"iteration":422,"passed_time":10.22978806,"remaining_time":1.862160002}, +{"learn":[0.04058466131],"iteration":423,"passed_time":10.25200434,"remaining_time":1.837623419}, +{"learn":[0.04045016676],"iteration":424,"passed_time":10.27407965,"remaining_time":1.81307288}, +{"learn":[0.04033295089],"iteration":425,"passed_time":10.29598217,"remaining_time":1.788503946}, +{"learn":[0.04012984965],"iteration":426,"passed_time":10.31875555,"remaining_time":1.764096381}, +{"learn":[0.03995289728],"iteration":427,"passed_time":10.34082299,"remaining_time":1.739577699}, +{"learn":[0.03970746465],"iteration":428,"passed_time":10.36400745,"remaining_time":1.715255312}, +{"learn":[0.03949775163],"iteration":429,"passed_time":10.38808739,"remaining_time":1.691083993}, +{"learn":[0.03932209138],"iteration":430,"passed_time":10.41095259,"remaining_time":1.666718629}, +{"learn":[0.03910362134],"iteration":431,"passed_time":10.43412156,"remaining_time":1.642408023}, +{"learn":[0.03886318619],"iteration":432,"passed_time":10.45846792,"remaining_time":1.618284874}, +{"learn":[0.03869829193],"iteration":433,"passed_time":10.48182288,"remaining_time":1.594009932}, +{"learn":[0.03846231914],"iteration":434,"passed_time":10.50794099,"remaining_time":1.570152102}, +{"learn":[0.03826711438],"iteration":435,"passed_time":10.53190868,"remaining_time":1.545968246}, +{"learn":[0.03807202579],"iteration":436,"passed_time":10.55517422,"remaining_time":1.521684156}, +{"learn":[0.03786720622],"iteration":437,"passed_time":10.579381,"remaining_time":1.49753795}, +{"learn":[0.0377457012],"iteration":438,"passed_time":10.60138736,"remaining_time":1.473085715}, +{"learn":[0.03764523055],"iteration":439,"passed_time":10.62328529,"remaining_time":1.448629813}, +{"learn":[0.0375267263],"iteration":440,"passed_time":10.64579999,"remaining_time":1.424268026}, +{"learn":[0.03739077589],"iteration":441,"passed_time":10.66908854,"remaining_time":1.400016143}, +{"learn":[0.03720490686],"iteration":442,"passed_time":10.69258734,"remaining_time":1.375795663}, +{"learn":[0.03707441914],"iteration":443,"passed_time":10.71474358,"remaining_time":1.3514091}, +{"learn":[0.0369518746],"iteration":444,"passed_time":10.73673589,"remaining_time":1.327012301}, +{"learn":[0.03679965145],"iteration":445,"passed_time":10.75968647,"remaining_time":1.302742309}, +{"learn":[0.03665723969],"iteration":446,"passed_time":10.78333295,"remaining_time":1.27856073}, +{"learn":[0.03647330679],"iteration":447,"passed_time":10.80867049,"remaining_time":1.254577825}, +{"learn":[0.03637255373],"iteration":448,"passed_time":10.82990171,"remaining_time":1.230122466}, +{"learn":[0.03622583483],"iteration":449,"passed_time":10.85228003,"remaining_time":1.205808892}, +{"learn":[0.03607239533],"iteration":450,"passed_time":10.87496749,"remaining_time":1.181537487}, +{"learn":[0.03601574616],"iteration":451,"passed_time":10.89771261,"remaining_time":1.157279215}, +{"learn":[0.03584618827],"iteration":452,"passed_time":10.92194863,"remaining_time":1.133182308}, +{"learn":[0.03574008044],"iteration":453,"passed_time":10.94376603,"remaining_time":1.108839729}, +{"learn":[0.03569749187],"iteration":454,"passed_time":10.96549146,"remaining_time":1.084499155}, +{"learn":[0.03557336502],"iteration":455,"passed_time":10.98880576,"remaining_time":1.060323363}, +{"learn":[0.03539082977],"iteration":456,"passed_time":11.01338067,"remaining_time":1.036269953}, +{"learn":[0.0352789402],"iteration":457,"passed_time":11.03566028,"remaining_time":1.012003781}, +{"learn":[0.03505536388],"iteration":458,"passed_time":11.05856328,"remaining_time":0.9878019484}, +{"learn":[0.03495206755],"iteration":459,"passed_time":11.08111735,"remaining_time":0.9635754219}, +{"learn":[0.03478190743],"iteration":460,"passed_time":11.10508112,"remaining_time":0.9394754094}, +{"learn":[0.03465723213],"iteration":461,"passed_time":11.12815812,"remaining_time":0.9153030492}, +{"learn":[0.03453161153],"iteration":462,"passed_time":11.15050611,"remaining_time":0.8910771623}, +{"learn":[0.034334941],"iteration":463,"passed_time":11.17327344,"remaining_time":0.866891905}, +{"learn":[0.03416935733],"iteration":464,"passed_time":11.19764405,"remaining_time":0.8428334234}, +{"learn":[0.03405767511],"iteration":465,"passed_time":11.22081306,"remaining_time":0.8186859313}, +{"learn":[0.03394943303],"iteration":466,"passed_time":11.24345459,"remaining_time":0.7945053567}, +{"learn":[0.03374958171],"iteration":467,"passed_time":11.26703627,"remaining_time":0.7703956426}, +{"learn":[0.03363658898],"iteration":468,"passed_time":11.28955126,"remaining_time":0.7462176741}, +{"learn":[0.03341608014],"iteration":469,"passed_time":11.31481723,"remaining_time":0.7222223762}, +{"learn":[0.03332221514],"iteration":470,"passed_time":11.33944193,"remaining_time":0.6981821993}, +{"learn":[0.03326545376],"iteration":471,"passed_time":11.36179187,"remaining_time":0.6740046026}, +{"learn":[0.0331200389],"iteration":472,"passed_time":11.38850836,"remaining_time":0.650083987}, +{"learn":[0.03302212556],"iteration":473,"passed_time":11.41176383,"remaining_time":0.625961729}, +{"learn":[0.03287398575],"iteration":474,"passed_time":11.43655516,"remaining_time":0.6019239557}, +{"learn":[0.03278840054],"iteration":475,"passed_time":11.46011271,"remaining_time":0.577820809}, +{"learn":[0.03265757698],"iteration":476,"passed_time":11.48284467,"remaining_time":0.5536801411}, +{"learn":[0.03252871663],"iteration":477,"passed_time":11.50658214,"remaining_time":0.5295916466}, +{"learn":[0.03236889905],"iteration":478,"passed_time":11.5305887,"remaining_time":0.5055164149}, +{"learn":[0.03224167231],"iteration":479,"passed_time":11.55399534,"remaining_time":0.4814164723}, +{"learn":[0.03216011999],"iteration":480,"passed_time":11.57584315,"remaining_time":0.4572578374}, +{"learn":[0.03192498158],"iteration":481,"passed_time":11.59976646,"remaining_time":0.4331862993}, +{"learn":[0.03183675149],"iteration":482,"passed_time":11.62260133,"remaining_time":0.4090770655}, +{"learn":[0.03171906574],"iteration":483,"passed_time":11.64573888,"remaining_time":0.3849831033}, +{"learn":[0.03156880496],"iteration":484,"passed_time":11.67016736,"remaining_time":0.3609330113}, +{"learn":[0.03145563437],"iteration":485,"passed_time":11.69324895,"remaining_time":0.3368425623}, +{"learn":[0.03136115044],"iteration":486,"passed_time":11.71487592,"remaining_time":0.3127174271}, +{"learn":[0.03122164061],"iteration":487,"passed_time":11.73913228,"remaining_time":0.2886671873}, +{"learn":[0.03109933328],"iteration":488,"passed_time":11.76249975,"remaining_time":0.2645961089}, +{"learn":[0.03102429087],"iteration":489,"passed_time":11.78385305,"remaining_time":0.240486797}, +{"learn":[0.03095523906],"iteration":490,"passed_time":11.80655921,"remaining_time":0.216413509}, +{"learn":[0.03085009531],"iteration":491,"passed_time":11.82931033,"remaining_time":0.1923465095}, +{"learn":[0.03071320709],"iteration":492,"passed_time":11.85312218,"remaining_time":0.1682999093}, +{"learn":[0.03055708091],"iteration":493,"passed_time":11.87753088,"remaining_time":0.1442615087}, +{"learn":[0.03041588169],"iteration":494,"passed_time":11.90089939,"remaining_time":0.1202111049}, +{"learn":[0.03027938644],"iteration":495,"passed_time":11.92492595,"remaining_time":0.09616875767}, +{"learn":[0.0301679957],"iteration":496,"passed_time":11.94755094,"remaining_time":0.07211801375}, +{"learn":[0.03003869429],"iteration":497,"passed_time":11.97044565,"remaining_time":0.04807407892}, +{"learn":[0.02994175927],"iteration":498,"passed_time":11.99279918,"remaining_time":0.02403366569}, +{"learn":[0.02983188626],"iteration":499,"passed_time":12.01560647,"remaining_time":0} +]} \ No newline at end of file diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents new file mode 100644 index 0000000000000000000000000000000000000000..50bba2ebdca7c775e43f257a84873f0ee970f871 GIT binary patch literal 27370 zcmZ|XXhBnc71-ln8MgHll;Whf;Tp^zd;QZht_ z2BJb4ODbgOd7j&P?qi+*eRRLOUwl8mW36?yu5+F1vb!k!`JbVl{p3ap3icEG8Rw4H zZLO@K+`ze6-Bk{f+i`&O_BR~RYz1a zkExTYC@Xa4Rb5fND{4aO`|;`1d8H((ZD|cjJ^!S4l2`Ra^&mr$RMCuAF1)HQs{2Mi zNBs93HeY*bDz6%dDkHg+lwsG|5xiFPi7NgJ(lz6IFu=mq=M=InCu&b5UK_ zjwQ8tO3g%GwGh?3&~v0ZeJ(5DRZCHMPKYK|6ZXi4S8AfV+u}4S{er|>yiylc)657` zn)x;Nc%>n#*!*x($r^6iylN#X^_yX&T68Tngw&C$p}%<5UQ}n50!bY&3CZP^rl<`21(1p}iXOwO z4x+kMd5~1$rnZ&5>L@BNeVIC2Tph=&PNKT6aDZBB%Z8rgm6oW2g8WEnm;`;|RcBGf z9rGm>(5pI(S6xKq^kN^Wwc}I6d8I9?RDGE;yj;?QS307aROn4DSG|P?dDT@^t6%LU z)vJ3Bt^uUGXg5*qdGAT8$bRg7zSUh+Q9C?H4cB&i#4BA<9Z+*8WnVWUl2>}73N3df zH7%hsh*v#CHOx$=bbIYg;#E&keca?it?%Z2`tz!nsOp65Cgm5Hx0P4=qG~Wfrkd>f z8pW&LqOuKkrq+z%(ZRgxBdX&OPNdGo1kdDEUr~j{Ig--<^1B(Y`iW{nFPZ8R{b3QW z3`AuU>_Dx!ayw&Q^%qsbNSTU>aQETW08!Onw~Jcinlv`x)j(0HT(l=ux9Bad926$~ z-)dfUkf@TU$&~YkQeS9EYOts>*6yU1ZH!lUUJVgdmV+IsrW@ay^J=K53g2ue<$itV z4qgotm2uuSQi(e0fxH?nD)Uh?b-CIb*YncZ7$GXJpRkn2McV3MbRn>#p8=xJU}5|wA{DpFIj%By*0EUJM~D@mD^Ud87W z>3W%n>Sl-)saD#RMSN?rs4_arRN9yNxO*VAriiLX>lM^GH7olJ-rCaTA!7NkntZW!^c>7q(0UPfxK*?D{#m0G5vnqOC@tQYHD;af9AwZCd9wPs#? zs>Q3BqB_}9rmU@OGI%vhRJt?Gs5N28FnnH?j%&84>clN3wYxO0ma{QORNpEVkuq6* z8dv60Yp$r;B`+Z5RT^8xAJ;rl6-CV_RkPLjDzD~?s=VVoQq=>kYq`%a5LKMzTvCZv zRvq}(LQ&0&n?vft;K=p7S|lo6E1Bw+=v>QvezB;wHIu1+n`&yA&`eY*+h@~+`X1MB z@DnZ()so$_NPVA|{+w4!MRlXKOc^$9YX@r7pS6v-sIJ(}q?Sum6+UH4YMH3MOqoH- zqMPeizGWe*e$J+(lmyU$H>=h1ncpHRjUQvE^};3^-&ROhd8??DJY;IZsG5iTgxf?lrN2yl zU9u^bSKCFUciWIAyfFG-D+g)99ip1nO{UgORN2nA>_p`jFq&G?`?sy*)lN|@zdMSQ z{^5(qx=L@d5(>_Z`` zJYVP$JK}D;hiAuu8LpKvkF-)Po&^I_{-8@W9qYz_@hk+`um~CJ=@UJcXD5J-Yb#@K z2emBaStzishr82|X5F)D88Qr5No6-;I=8=N@U@e`;uX6RYxpqThG*fx{9|>9#YKcq z;n^u*`}@e)s!K0JcoqR{w!b#ja-G$e@+=bA;V)f?oxXlZmuFGH#;%sJmn9=E@a!}& z+p^A7>z`-)mS<;x4Ok{)%{A-Ra_5Q$cA`d$YLD6lujXrKft_40V;;X8PVwv*D756ob$jD@y~JHfLUVB3y$q+0t_r@_FI{w!x=f$dO}u`}WNn|XEtSOdon zRBNM_`i*B7fsHz;Nlg1`K`ldG0@nUhdtyyAyWxjRI<`1q4jM9cYfb(Se#pzf3SYIQ z+UAy-syvGaR=GmPOv?7(=2-%;MJh7(#5?p3&#nMdNoqqwcJS0)%d@M%A`N8B`N(;1 zo+SdaxX_wvgQ_NfY~uAf^GE7IsXlV?f5Ms$&};JP3KEFwy zl4M{;y=5%nM8t2tb_3Xw6m_aiRk*o{XDPr==*iduja+k{-30b=s~Xi#O_+!aL+RL3 zf&H_-B{82Sb#O?X~4{XHz(G5b&DjPr32eAMaJ9?x;gOdHn2J` zn^CRN7(ZN@OQ$3QSWjaaGrQLJ8eh8uY~>kMswJqOw&vMgV1`jmiFI((?#Z)zz|tNx zA!eem!;WY7fi1LJ!UUn}nM>(qBLhu(294X8ynnzuy#$|MM%9TwnoV^{6(| z-Do%*o5b>f*(WIxdt-GSmopN32F&$iU1BS|o@Mg2=fHL-%GlE8b8yWi)$)O5`zul{ zxxuPWeC-9W^X+A9N|jAA&t3xSHd%pc-g-GlN^#0$7 znO$7pNK`z&0NI~`4QM~?~lYjRNjBUvroXT|MP*E zjoVZUo|OV~>hzvikK3!4@vID(Vb8b3c4(fd!?Vx8MsF=9_My@jcW0zCSq>~N{uQxH zhF8@1+81DlYzm1P1lX4ItOA(gwU@-gJNJFWvr1s*Ir+rWvmJ0>Svs~VU|~m}6VqL< zIE}A;1=e!yGh#~<67jK5s(l02P9cw2l$z!YzV;nh*Y!EXT1AelqII&znor)dH% zu}A$`&Nw0)bcfiOjoG`QCNU>qJs;jC_PW)BraW^7miaK9*k6`veR;MU*usZt#6oT> zzPRSu)3DeFJvkV)MZD>HiY>;$l%Z-a?-pQ(p0<)tA*fqm*AW2x^eNAa~VU?m-oQ>~kEGVV`GwUfZ^ z4?IRJ)}aZm6eJc7EO^#YV$XJG)$(V03fN5FBgAq)WZvSBEdrRLO%O5L@7A^4UL%3U zBnJ}vq*+x4HTyrynJ8e9Zw?ddqMARSXQzR=_Ls3+er3yfb_Q5%uK=pqoz!j5vuI$A z*Bl}?XW7s|o}C5eb?YE83;%`dcy$Fobo@)z$X zHu%Uqd{~i&j00AFdLOa9b4pYB+GSv7Lwtyt`X9!xe5n=>%thUs*yyPm_|igR3Ba^x zcoBPHVq?n>c?Fnb^j=~iS~d8PDb=n5OaJUitUR$IiLWIB+cHYVRxg@*k7w6_m2CH* z+JdHEaAhtHnFLHXX%Df+W>M|<+I3*9I>}i5>TPRzmJCd3q&wAm6)(lFd}+uVz&^RV z5!3LwgRe*=mI7?|TUTN}6;b%rA+ejlPR)?914%PC@yC`5ELFpWYTFbKWby14upbk5 z6FYAogsWF+$TVPkS2`1$sDfi{ccX?*<)Z2H2)#id+Sd8;c?QRRnQY)vA;GGoAo#ke?2C#r@-d=ZX%Xn z+35`oDY0x|dn`8+D?T2E-=-4F0XC%H24Xso%MAHiF0jk0>xntN8XUm0JYYjwuOrqr zNgWqY(vZ)9Eva5ZEN5wjWw2;^#0I#uy2%gu8kotIWyIXlE{5~$4KV%j=EOF}q~ji*G-NTbP7jw5 z^R)CI%hyVPHEd=^Y}4`;+B|y;tfXKOF|#LqYWZ-z1D5}GA+f>(6Lk67dtg4B77z>E z-K>~rAAo7Q&L_5fW@#-$egvj-XCATSMy@4%?Gv!MdNLMk`l>6>N`a}rnnSe`r4{%S z0O@j<0Xv^Qo7mk+>nr%$XJBeqXAwJpcH6&xzW3)lpd47=)-u-BKNY`(r6Ion>mM_N zY8#L3#g&4@DuB6fFeNri#j6c}Y?Z)v`%Wh|SpVopo>c);4w^>nfSXDWo_z&&xMV7^ z{nPK)a_9O6EaCeUVxDL2#PhZ9z=rpdvAGRTY~Wcnu!!T6spgb>w=d6r0L#rYA!fdO zEWV?XuG>#wJ?>eu--pL6SKP58sCUXY&WpA!(^=g&Hi{Vk(djx zjsc^n7FU!tl|Ln}z;?xrB(}tCuNlwWfcdzNAeMjsd?wG_fvI{7C$`1P8-Hah9orsY z=~2UoC2uRh2WW|T09#Q#lvtpmM;Sk)C$K(2Lx^qsb|{}`dx1^9J($?kss;Ud<^?SC z^&n!V{eFJnnK!U3+kwQ&W7Iu(<^wEOX#laD)Q!0JDxJxFz!Y}$C#JG93vaIy+YhXD zcNrTrYw;#n}TMz?i|-EdIDx3toA3jx;Dr#rDl zWp;^t?F6uhfNsPT5@+b~EEHI~@UFy;O>o2KZfVFcU0K>O!vos@wEtGea*U1?TE`m+(MFSk-)-2I}_`D zde&8_jry|+iUMYQLW`KT{)YWLI}L1kS|?)XeZJl1*%@F4sxs#6P@%=MXkeOp9jTVL za71UGodu?--+`ER+B)2dkWR@tU~L>UiMfZz8uGRCz#`n+6I+rJd!1)7z^bpdBlf{E zx`1b~z*c>3ORUA19iw@60a$1o8SB;At}V|l0vmp~4b{d!-QR>~mw*-Zk+EO8-Hdq_ z2kh8|)>Qj@;Ept&T?W>?tBe_T&zQuscwjv&T2XD!*Sok3l|Cg2z~<#?5NkVcfhJ$O z0xV~~jAeCF!S4X6b`{vr1a+!i9axP|`4US6R;(>!b<`%e}Bj*^vN8a-3B($zbVxQ+_S(1y>x6Dz}|G1F{k6XzI^Qt zuq}T#p_*%WV|$+61(sN$LhRnJ_qb&%4S5e(z4bEo?$WH6eCDRcjFz$Mk;=Jn zY!b@`RuJBRhV*D!_g}wL_*2UP*56RZ^46;0U0JH-0(%iwpK2}7K0eD2nFp-XWEnHN z?7xL)&wy3js7JNpW}5inl7@T^?C~%ed+87`jj!bc8|9}&wM|L8r||3ru7#*IolV_EdptQEpG%dG-dF=SCUpt+f6O&x(P$q|^-i@4qD6 zRuNUgvl3tpQhpO_7?q7rRMIJV3+$whjD5~>f5_L~0ekBAi)uqsX)Yf&})v6TYb z+V~qW55@7ggCSjjGGH&Xz7n(03c$?)iG2okp+yz3b-%6fcjgi+2WCB?lGr!HR{s34 QeF3ImUO{Z&;gPBT1KDe@5C8xG literal 0 HcmV?d00001 diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv new file mode 100644 index 0000000..c5e7d15 --- /dev/null +++ b/catboost_info/learn_error.tsv @@ -0,0 +1,501 @@ +iter Logloss +0 0.6505594592 +1 0.6199570308 +2 0.5883839207 +3 0.5581651741 +4 0.5343091005 +5 0.5070162395 +6 0.4927200876 +7 0.4682830337 +8 0.4539977069 +9 0.4430063307 +10 0.4260619942 +11 0.4123586166 +12 0.3986592355 +13 0.3898627233 +14 0.3805898091 +15 0.3714802352 +16 0.3635447217 +17 0.3579714002 +18 0.3518505418 +19 0.3469646375 +20 0.342350397 +21 0.3380417078 +22 0.3336186075 +23 0.3298240945 +24 0.3257224063 +25 0.320477237 +26 0.3169914291 +27 0.3120681587 +28 0.3094016032 +29 0.304723249 +30 0.3011723022 +31 0.2987501309 +32 0.2972378553 +33 0.2949280448 +34 0.2922181334 +35 0.2902886884 +36 0.2868615526 +37 0.2855207315 +38 0.2829764561 +39 0.2790864362 +40 0.2771876663 +41 0.274396297 +42 0.269695974 +43 0.2674762223 +44 0.266351576 +45 0.2645715787 +46 0.2629624082 +47 0.2622682603 +48 0.2610050288 +49 0.2591143021 +50 0.2572681325 +51 0.2562124872 +52 0.2551455095 +53 0.254392464 +54 0.2528077948 +55 0.2513889396 +56 0.2506161354 +57 0.2494460772 +58 0.2485459323 +59 0.2469745034 +60 0.2459537181 +61 0.2453627957 +62 0.2433912548 +63 0.2422951861 +64 0.2400155969 +65 0.2390895846 +66 0.2375225747 +67 0.2362798202 +68 0.2348801591 +69 0.234274226 +70 0.2331038168 +71 0.2323492401 +72 0.2316465084 +73 0.2299897258 +74 0.2292043653 +75 0.2281960167 +76 0.2271872251 +77 0.2266875911 +78 0.2256819597 +79 0.2248734679 +80 0.2235711296 +81 0.2225638016 +82 0.2216924707 +83 0.2206671726 +84 0.2198053434 +85 0.2180945036 +86 0.2174988086 +87 0.2167547953 +88 0.2146076599 +89 0.21355014 +90 0.2120397433 +91 0.2113293421 +92 0.2106641679 +93 0.2103582924 +94 0.2100276146 +95 0.2094603169 +96 0.208520409 +97 0.2081060903 +98 0.2075147098 +99 0.2063558526 +100 0.2055862444 +101 0.204938244 +102 0.2041199746 +103 0.20357568 +104 0.2029479812 +105 0.2024233181 +106 0.2018709313 +107 0.2007005578 +108 0.1995046398 +109 0.1988329196 +110 0.1980569761 +111 0.197332958 +112 0.1968751476 +113 0.196281669 +114 0.1954746084 +115 0.195100344 +116 0.1947434834 +117 0.1940842572 +118 0.193016598 +119 0.192326827 +120 0.1919624319 +121 0.1913875196 +122 0.190963803 +123 0.1906330299 +124 0.1902243409 +125 0.1896069044 +126 0.1890660639 +127 0.1882835195 +128 0.1875493514 +129 0.1869075794 +130 0.1864064876 +131 0.1861340119 +132 0.1856804614 +133 0.1851377629 +134 0.1843016898 +135 0.1835527038 +136 0.1830184334 +137 0.1823413379 +138 0.1818241981 +139 0.1810634762 +140 0.1804669956 +141 0.1800037734 +142 0.179229966 +143 0.1784811687 +144 0.1776685278 +145 0.1770219582 +146 0.1766783294 +147 0.1762438721 +148 0.1758425501 +149 0.1749885611 +150 0.1738557684 +151 0.1732595564 +152 0.1727113418 +153 0.1719918768 +154 0.1714453538 +155 0.1709170131 +156 0.1700539884 +157 0.1696775312 +158 0.169185393 +159 0.1684625546 +160 0.1678449693 +161 0.1674127808 +162 0.1669160497 +163 0.1666650109 +164 0.1661196582 +165 0.1656309243 +166 0.1650005575 +167 0.1630925189 +168 0.1627982599 +169 0.1622984813 +170 0.1617063039 +171 0.161085537 +172 0.1605654299 +173 0.1600969242 +174 0.1595581484 +175 0.1591783098 +176 0.1586366754 +177 0.1581714089 +178 0.1575630275 +179 0.1568621859 +180 0.1563319754 +181 0.1557732651 +182 0.15493488 +183 0.1542286414 +184 0.1534099833 +185 0.1522403016 +186 0.1517637469 +187 0.1514181606 +188 0.1511232407 +189 0.1505915384 +190 0.1501060085 +191 0.1493588672 +192 0.1485757524 +193 0.1479396924 +194 0.1476450754 +195 0.1472334844 +196 0.1466508463 +197 0.1459767298 +198 0.1449640915 +199 0.1442547887 +200 0.1435485115 +201 0.142877376 +202 0.1424560809 +203 0.1418883293 +204 0.1412903231 +205 0.1403055475 +206 0.1394721343 +207 0.1390547172 +208 0.1386442713 +209 0.1376316573 +210 0.136783024 +211 0.1353549217 +212 0.1349160868 +213 0.1338548394 +214 0.1331950161 +215 0.1325567202 +216 0.1315897372 +217 0.130948128 +218 0.1305183499 +219 0.1296405854 +220 0.1288357946 +221 0.1279353696 +222 0.1265519823 +223 0.1260898397 +224 0.1252943222 +225 0.1248322461 +226 0.1241281529 +227 0.1232154663 +228 0.1225610856 +229 0.1217825847 +230 0.12119544 +231 0.1201025465 +232 0.1193795277 +233 0.1188654429 +234 0.1178438479 +235 0.1173572605 +236 0.1164264053 +237 0.1159977674 +238 0.1154646969 +239 0.1148100226 +240 0.1138558555 +241 0.1130261467 +242 0.1116842639 +243 0.1105644217 +244 0.1097393073 +245 0.1088130653 +246 0.1082146647 +247 0.1076318341 +248 0.1069338009 +249 0.1061728523 +250 0.1057125099 +251 0.105263911 +252 0.104622054 +253 0.1040334688 +254 0.1034978632 +255 0.1022838523 +256 0.1017358698 +257 0.1009628412 +258 0.1003992689 +259 0.0997164366 +260 0.09903368875 +261 0.09854662155 +262 0.09805747523 +263 0.09741884443 +264 0.09610540779 +265 0.09565119136 +266 0.09510746696 +267 0.09465424408 +268 0.09394914967 +269 0.09341616081 +270 0.09285369188 +271 0.09232671453 +272 0.09171540397 +273 0.09092693429 +274 0.09032530539 +275 0.08961859073 +276 0.0889928576 +277 0.08843924734 +278 0.08792722887 +279 0.08737190418 +280 0.08682846822 +281 0.08609147931 +282 0.0855848848 +283 0.0849478236 +284 0.08443710276 +285 0.08352754909 +286 0.08305956533 +287 0.0823083313 +288 0.08184267631 +289 0.08124107118 +290 0.08074769811 +291 0.08021947918 +292 0.0795478537 +293 0.07916246376 +294 0.07867604825 +295 0.07823144468 +296 0.0778000497 +297 0.07743135104 +298 0.07687188002 +299 0.07654323388 +300 0.07612356047 +301 0.07578569588 +302 0.07528439413 +303 0.07491501857 +304 0.07459938027 +305 0.07434000126 +306 0.07405502385 +307 0.07359936025 +308 0.07321257176 +309 0.07281411801 +310 0.0723927812 +311 0.07195828953 +312 0.07162083385 +313 0.07091211511 +314 0.07035546389 +315 0.06998280407 +316 0.06967070096 +317 0.06930882148 +318 0.06897063076 +319 0.06859761419 +320 0.06823762036 +321 0.06793421641 +322 0.06750300536 +323 0.06715951494 +324 0.06685166117 +325 0.06656581837 +326 0.06596331136 +327 0.06557079132 +328 0.06514273361 +329 0.0648161061 +330 0.06429278866 +331 0.06388688686 +332 0.06356263701 +333 0.06327858563 +334 0.06299176056 +335 0.06268236565 +336 0.0624306551 +337 0.06208009921 +338 0.06180855918 +339 0.06162921724 +340 0.06115204937 +341 0.06057626473 +342 0.06030054307 +343 0.05995592477 +344 0.05966810366 +345 0.05913088628 +346 0.05885383971 +347 0.0586178066 +348 0.0581001034 +349 0.05783949145 +350 0.05741847465 +351 0.05711831445 +352 0.05679720458 +353 0.05657943319 +354 0.05636778384 +355 0.05607497286 +356 0.05587282454 +357 0.05559949705 +358 0.05524366884 +359 0.05488789301 +360 0.05445960317 +361 0.05417052086 +362 0.05393397186 +363 0.05357891611 +364 0.05335249446 +365 0.05317320492 +366 0.05301985043 +367 0.05270775281 +368 0.05243844604 +369 0.05218227737 +370 0.05202597967 +371 0.05168457597 +372 0.0513787649 +373 0.05116703418 +374 0.05093101511 +375 0.0507589405 +376 0.05053112856 +377 0.05033706654 +378 0.05020300153 +379 0.05001435435 +380 0.04975525929 +381 0.04948290657 +382 0.04929070362 +383 0.04910496235 +384 0.04881316411 +385 0.04858449122 +386 0.04830036156 +387 0.04799888759 +388 0.04764501811 +389 0.04735869133 +390 0.04717676982 +391 0.04702198439 +392 0.04680681448 +393 0.04651695718 +394 0.04627615035 +395 0.04606321813 +396 0.04589309758 +397 0.04556006451 +398 0.0453589455 +399 0.04514104221 +400 0.04478819136 +401 0.04443821936 +402 0.04430866417 +403 0.0442286461 +404 0.0441045591 +405 0.04396148169 +406 0.04383991606 +407 0.04369433117 +408 0.04359398001 +409 0.04335115657 +410 0.0432504709 +411 0.04312499215 +412 0.0430111312 +413 0.04290906293 +414 0.04276421934 +415 0.04243290185 +416 0.04207124094 +417 0.04190756042 +418 0.04169658039 +419 0.04139810429 +420 0.04116312391 +421 0.0409318057 +422 0.04070910759 +423 0.04058466131 +424 0.04045016676 +425 0.04033295089 +426 0.04012984965 +427 0.03995289728 +428 0.03970746465 +429 0.03949775163 +430 0.03932209138 +431 0.03910362134 +432 0.03886318619 +433 0.03869829193 +434 0.03846231914 +435 0.03826711438 +436 0.03807202579 +437 0.03786720622 +438 0.0377457012 +439 0.03764523055 +440 0.0375267263 +441 0.03739077589 +442 0.03720490686 +443 0.03707441914 +444 0.0369518746 +445 0.03679965145 +446 0.03665723969 +447 0.03647330679 +448 0.03637255373 +449 0.03622583483 +450 0.03607239533 +451 0.03601574616 +452 0.03584618827 +453 0.03574008044 +454 0.03569749187 +455 0.03557336502 +456 0.03539082977 +457 0.0352789402 +458 0.03505536388 +459 0.03495206755 +460 0.03478190743 +461 0.03465723213 +462 0.03453161153 +463 0.034334941 +464 0.03416935733 +465 0.03405767511 +466 0.03394943303 +467 0.03374958171 +468 0.03363658898 +469 0.03341608014 +470 0.03332221514 +471 0.03326545376 +472 0.0331200389 +473 0.03302212556 +474 0.03287398575 +475 0.03278840054 +476 0.03265757698 +477 0.03252871663 +478 0.03236889905 +479 0.03224167231 +480 0.03216011999 +481 0.03192498158 +482 0.03183675149 +483 0.03171906574 +484 0.03156880496 +485 0.03145563437 +486 0.03136115044 +487 0.03122164061 +488 0.03109933328 +489 0.03102429087 +490 0.03095523906 +491 0.03085009531 +492 0.03071320709 +493 0.03055708091 +494 0.03041588169 +495 0.03027938644 +496 0.0301679957 +497 0.03003869429 +498 0.02994175927 +499 0.02983188626 diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv new file mode 100644 index 0000000..c618ac5 --- /dev/null +++ b/catboost_info/time_left.tsv @@ -0,0 +1,501 @@ +iter Passed Remaining +0 31 15495 +1 56 13981 +2 78 13029 +3 102 12704 +4 125 12422 +5 149 12287 +6 173 12237 +7 198 12212 +8 221 12080 +9 244 11997 +10 269 11962 +11 294 11978 +12 320 12005 +13 345 11987 +14 370 11970 +15 393 11913 +16 416 11823 +17 439 11776 +18 463 11746 +19 487 11705 +20 512 11685 +21 537 11674 +22 560 11629 +23 584 11596 +24 607 11551 +25 631 11510 +26 655 11490 +27 680 11469 +28 705 11455 +29 729 11422 +30 754 11417 +31 779 11393 +32 803 11368 +33 829 11366 +34 854 11356 +35 879 11329 +36 902 11287 +37 925 11249 +38 948 11215 +39 972 11184 +40 995 11146 +41 1020 11126 +42 1045 11116 +43 1068 11073 +44 1091 11036 +45 1113 10994 +46 1137 10964 +47 1160 10923 +48 1185 10913 +49 1209 10887 +50 1233 10858 +51 1258 10841 +52 1280 10796 +53 1305 10781 +54 1329 10756 +55 1353 10731 +56 1377 10708 +57 1398 10660 +58 1422 10631 +59 1446 10606 +60 1468 10569 +61 1490 10529 +62 1513 10497 +63 1537 10473 +64 1561 10450 +65 1585 10429 +66 1610 10406 +67 1636 10394 +68 1659 10367 +69 1683 10338 +70 1706 10308 +71 1730 10284 +72 1754 10262 +73 1778 10236 +74 1801 10206 +75 1825 10186 +76 1850 10165 +77 1875 10147 +78 1899 10123 +79 1924 10104 +80 1948 10076 +81 1971 10049 +82 1994 10021 +83 2016 9987 +84 2041 9967 +85 2065 9941 +86 2089 9917 +87 2113 9893 +88 2139 9879 +89 2162 9851 +90 2186 9828 +91 2211 9807 +92 2234 9780 +93 2258 9756 +94 2283 9734 +95 2307 9709 +96 2332 9690 +97 2356 9667 +98 2380 9641 +99 2404 9616 +100 2428 9591 +101 2450 9561 +102 2474 9536 +103 2497 9509 +104 2518 9475 +105 2542 9449 +106 2567 9429 +107 2590 9402 +108 2613 9375 +109 2638 9355 +110 2661 9328 +111 2685 9304 +112 2711 9284 +113 2736 9264 +114 2764 9254 +115 2788 9231 +116 2814 9213 +117 2840 9195 +118 2865 9175 +119 2892 9158 +120 2916 9135 +121 2941 9114 +122 2965 9089 +123 2990 9068 +124 3014 9042 +125 3038 9019 +126 3062 8993 +127 3087 8974 +128 3113 8955 +129 3137 8929 +130 3162 8908 +131 3185 8881 +132 3208 8852 +133 3232 8829 +134 3256 8803 +135 3278 8775 +136 3302 8751 +137 3327 8728 +138 3352 8706 +139 3378 8688 +140 3402 8663 +141 3426 8639 +142 3452 8619 +143 3476 8595 +144 3501 8571 +145 3524 8546 +146 3547 8518 +147 3571 8494 +148 3593 8464 +149 3616 8439 +150 3641 8416 +151 3665 8390 +152 3688 8364 +153 3711 8339 +154 3737 8319 +155 3761 8295 +156 3785 8270 +157 3809 8245 +158 3832 8220 +159 3856 8195 +160 3881 8173 +161 3907 8151 +162 3931 8128 +163 3954 8101 +164 3980 8081 +165 4004 8057 +166 4031 8039 +167 4056 8016 +168 4080 7992 +169 4103 7964 +170 4127 7940 +171 4152 7918 +172 4177 7896 +173 4201 7871 +174 4224 7846 +175 4247 7818 +176 4274 7800 +177 4299 7778 +178 4329 7764 +179 4359 7750 +180 4386 7730 +181 4424 7731 +182 4465 7735 +183 4500 7728 +184 4527 7709 +185 4559 7696 +186 4586 7676 +187 4617 7663 +188 4642 7639 +189 4667 7615 +190 4690 7588 +191 4715 7564 +192 4738 7538 +193 4762 7512 +194 4786 7487 +195 4809 7460 +196 4834 7435 +197 4861 7415 +198 4888 7393 +199 4913 7370 +200 4937 7344 +201 4959 7316 +202 4981 7288 +203 5007 7266 +204 5030 7238 +205 5052 7211 +206 5075 7184 +207 5100 7159 +208 5124 7135 +209 5148 7110 +210 5174 7087 +211 5197 7060 +212 5219 7033 +213 5245 7010 +214 5268 6984 +215 5293 6960 +216 5319 6936 +217 5342 6911 +218 5368 6887 +219 5392 6863 +220 5418 6840 +221 5443 6816 +222 5469 6793 +223 5493 6768 +224 5518 6744 +225 5542 6720 +226 5568 6696 +227 5594 6674 +228 5618 6648 +229 5642 6624 +230 5665 6597 +231 5692 6575 +232 5716 6550 +233 5738 6522 +234 5762 6497 +235 5784 6470 +236 5808 6445 +237 5830 6418 +238 5854 6393 +239 5880 6370 +240 5905 6346 +241 5932 6324 +242 5958 6301 +243 5983 6277 +244 6006 6252 +245 6031 6227 +246 6055 6202 +247 6079 6177 +248 6105 6154 +249 6130 6130 +250 6156 6107 +251 6180 6082 +252 6206 6059 +253 6231 6034 +254 6255 6010 +255 6280 5986 +256 6305 5961 +257 6330 5937 +258 6354 5912 +259 6378 5887 +260 6404 5864 +261 6429 5840 +262 6451 5813 +263 6476 5789 +264 6503 5766 +265 6529 5743 +266 6552 5718 +267 6576 5693 +268 6601 5669 +269 6626 5644 +270 6650 5619 +271 6673 5594 +272 6697 5569 +273 6723 5545 +274 6748 5521 +275 6773 5497 +276 6797 5472 +277 6821 5447 +278 6846 5422 +279 6870 5398 +280 6895 5373 +281 6918 5348 +282 6943 5324 +283 6968 5300 +284 6994 5276 +285 7018 5251 +286 7040 5225 +287 7066 5201 +288 7089 5176 +289 7114 5151 +290 7139 5127 +291 7162 5102 +292 7186 5077 +293 7209 5051 +294 7232 5026 +295 7255 5000 +296 7279 4975 +297 7302 4950 +298 7325 4924 +299 7349 4899 +300 7372 4873 +301 7397 4849 +302 7422 4825 +303 7445 4800 +304 7468 4774 +305 7490 4748 +306 7512 4722 +307 7534 4696 +308 7557 4671 +309 7580 4646 +310 7604 4621 +311 7628 4596 +312 7650 4570 +313 7674 4545 +314 7700 4522 +315 7723 4497 +316 7747 4472 +317 7770 4447 +318 7795 4422 +319 7820 4399 +320 7844 4374 +321 7867 4349 +322 7892 4324 +323 7914 4299 +324 7937 4274 +325 7961 4249 +326 7984 4224 +327 8008 4199 +328 8033 4175 +329 8056 4150 +330 8080 4125 +331 8105 4101 +332 8126 4075 +333 8149 4050 +334 8172 4025 +335 8196 4000 +336 8218 3975 +337 8242 3950 +338 8267 3926 +339 8288 3900 +340 8311 3875 +341 8335 3850 +342 8356 3825 +343 8380 3800 +344 8403 3775 +345 8427 3750 +346 8450 3726 +347 8474 3701 +348 8498 3676 +349 8519 3651 +350 8542 3626 +351 8565 3601 +352 8588 3576 +353 8613 3552 +354 8636 3527 +355 8659 3502 +356 8682 3477 +357 8705 3452 +358 8728 3428 +359 8750 3403 +360 8776 3379 +361 8799 3354 +362 8822 3329 +363 8846 3305 +364 8868 3280 +365 8890 3255 +366 8913 3230 +367 8935 3205 +368 8958 3180 +369 8982 3155 +370 9003 3130 +371 9028 3106 +372 9051 3081 +373 9073 3056 +374 9095 3031 +375 9117 3006 +376 9141 2982 +377 9164 2957 +378 9186 2932 +379 9208 2907 +380 9230 2883 +381 9256 2859 +382 9278 2834 +383 9300 2809 +384 9324 2785 +385 9346 2760 +386 9372 2736 +387 9394 2711 +388 9420 2688 +389 9445 2664 +390 9467 2639 +391 9491 2614 +392 9515 2590 +393 9539 2566 +394 9564 2542 +395 9590 2518 +396 9614 2494 +397 9640 2470 +398 9665 2446 +399 9689 2422 +400 9716 2398 +401 9739 2374 +402 9761 2349 +403 9784 2325 +404 9807 2300 +405 9830 2276 +406 9853 2251 +407 9876 2226 +408 9900 2202 +409 9921 2177 +410 9943 2153 +411 9965 2128 +412 9987 2103 +413 10008 2079 +414 10030 2054 +415 10055 2030 +416 10081 2006 +417 10105 1982 +418 10129 1958 +419 10156 1934 +420 10180 1910 +421 10206 1886 +422 10229 1862 +423 10252 1837 +424 10274 1813 +425 10295 1788 +426 10318 1764 +427 10340 1739 +428 10364 1715 +429 10388 1691 +430 10410 1666 +431 10434 1642 +432 10458 1618 +433 10481 1594 +434 10507 1570 +435 10531 1545 +436 10555 1521 +437 10579 1497 +438 10601 1473 +439 10623 1448 +440 10645 1424 +441 10669 1400 +442 10692 1375 +443 10714 1351 +444 10736 1327 +445 10759 1302 +446 10783 1278 +447 10808 1254 +448 10829 1230 +449 10852 1205 +450 10874 1181 +451 10897 1157 +452 10921 1133 +453 10943 1108 +454 10965 1084 +455 10988 1060 +456 11013 1036 +457 11035 1012 +458 11058 987 +459 11081 963 +460 11105 939 +461 11128 915 +462 11150 891 +463 11173 866 +464 11197 842 +465 11220 818 +466 11243 794 +467 11267 770 +468 11289 746 +469 11314 722 +470 11339 698 +471 11361 674 +472 11388 650 +473 11411 625 +474 11436 601 +475 11460 577 +476 11482 553 +477 11506 529 +478 11530 505 +479 11553 481 +480 11575 457 +481 11599 433 +482 11622 409 +483 11645 384 +484 11670 360 +485 11693 336 +486 11714 312 +487 11739 288 +488 11762 264 +489 11783 240 +490 11806 216 +491 11829 192 +492 11853 168 +493 11877 144 +494 11900 120 +495 11924 96 +496 11947 72 +497 11970 48 +498 11992 24 +499 12015 0 diff --git a/lgbm_vs_cat_kmeans_smote_k10_results.csv b/lgbm_vs_cat_kmeans_smote_k10_results.csv new file mode 100644 index 0000000..3fca988 --- /dev/null +++ b/lgbm_vs_cat_kmeans_smote_k10_results.csv @@ -0,0 +1,5 @@ +model,stage,accuracy,f1_macro,f2_macro,recall_macro,precision_macro,f1_class0,f1_class1,f2_class0,f2_class1,recall_class0,recall_class1,precision_class0,precision_class1,TP,TN,FP,FN +CatBoost_balanced,train,0.9843784049402589,0.8696686267343388,0.8824472728294012,0.8916952848998795,0.8508242781484853,0.9919396338322237,0.7473976196364541,0.9908276010500254,0.7740669446087769,0.9900881006639566,0.7933024691358025,0.9938004847319636,0.7078480715650071,789,26898,140,19 +CatBoost_balanced,test,0.9802604802604803,0.8348421298822796,0.8461546793313885,0.8541662696976049,0.8176680164072361,0.9898162729658793,0.6798679867986799,0.988757446094471,0.703551912568306,0.9880528191154894,0.7202797202797203,0.991586032814472,0.64375,103,4714,57,40 +LGBM_KMEANS_SMOTE,train,0.9883286128479746,0.8784419356817057,0.8436008106620193,0.8240767336379762,0.9582821430574249,0.9940169232360254,0.7628669481273861,0.9966698960611392,0.6905317252628993,0.9984466771524954,0.6497067901234568,0.9896275269971563,0.9269367591176938,775,27036,2,33 +LGBM_KMEANS_SMOTE,test,0.9865689865689866,0.8543196878009516,0.8121616449258658,0.7895809912158687,0.9600745182511498,0.9931221342225928,0.7155172413793104,0.9964866786565728,0.6278366111951589,0.9987424020121568,0.5804195804195804,0.9875647668393782,0.9325842696629213,83,4765,6,60 diff --git a/runner.py b/runner.py index f07c7ba..dff26e3 100644 --- a/runner.py +++ b/runner.py @@ -1,14 +1,14 @@ import pandas from catboost import CatBoostClassifier -from imblearn.ensemble import BalancedRandomForestClassifier +# from imblearn.ensemble import BalancedRandomForestClassifier from lightgbm import LGBMClassifier -from sklearn.ensemble import RandomForestClassifier -from sklearn.linear_model import LogisticRegression +# from sklearn.ensemble import RandomForestClassifier +# from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split -from xgboost import XGBClassifier +# from xgboost import XGBClassifier -from custom_models.LGBMFocalWrapper import LGBMFocalWrapper +# from custom_models.LGBMFocalWrapper import LGBMFocalWrapper from train import test_model, train_model_with_kfold data_frame = pandas.read_csv("./data/Ketamin_icp_cleaned.csv") @@ -28,33 +28,266 @@ pos = sum(y_train == 1) scale_pos = neg / pos if pos > 0 else 1.0 models = [ + # { + # "name": "LGBM_FOCAL_LOSS", + # "model": LGBMFocalWrapper( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # random_state=42, + # ), + # "smote": True, + # "smote_method": "kmeans", + # }, + # { + # "name": "LGBM_SMOTE", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "smote", + # }, + # { + # "name": "LGBM_KMEANS_SMOTE", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "kmeans", + # }, + # { + # "name": "LGBM_SVM_SMOTE", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "svm", + # }, + # { + # "name": "LGBM_BORDERLINE_SMOTE", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "borderline", + # }, + # { + # "name": "LGBM_ADASYN_SMOTE", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "adasyn", + # }, + # { + # "name": "LGBM_Balanced", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # class_weight="balanced", + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": False, + # }, + # { + # "name": "LGBM_DART", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # boosting_type="dart", + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "kmeans", + # }, + # { + # "name": "LGBM_GOSS", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # boosting_type="goss", + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "kmeans", + # }, + # { + # "name": "LGBM_RF", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # boosting_type="rf", + # subsample=0.8, + # colsample_bytree=0.8, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "kmeans", + # }, + # { + # "name": "LGBM_scale_pos_weight", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # scale_pos_weight=scale_pos, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": False, + # }, + # { + # "name": "LGBM_is_unbalance", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # is_unbalance=True, + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": False, + # }, + # { + # "name": "LGBM_DART", + # "model": LGBMClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=-1, + # subsample=0.8, + # colsample_bytree=0.8, + # boosting_type="dart", + # random_state=42, + # verbose=-1, + # n_jobs=-1, + # ), + # "smote": True, + # "smote_method": "kmeans", + # }, + # { + # "name": "XGB_scale_pos_weight", + # "model": XGBClassifier( + # n_estimators=500, + # learning_rate=0.05, + # max_depth=6, + # scale_pos_weight=scale_pos, + # random_state=42, + # n_jobs=-1, + # use_label_encoder=False, + # eval_metric="logloss", + # ), + # "smote": False, + # }, + # { + # "name": "CatBoost_balanced", + # "model": CatBoostClassifier( + # iterations=500, + # learning_rate=0.05, + # depth=6, + # class_weights=[1, scale_pos], + # random_state=42, + # verbose=0, + # ), + # "smote": False, + # }, + # { + # "name": "RandomForest_balanced", + # "model": RandomForestClassifier( + # n_estimators=500, + # max_depth=None, + # class_weight="balanced", + # random_state=42, + # n_jobs=-1, + # ), + # "smote": False, + # }, + # { + # "name": "BalancedRandomForest", + # "model": BalancedRandomForestClassifier( + # n_estimators=500, + # max_depth=None, + # random_state=42, + # n_jobs=-1, + # ), + # "smote": False, + # }, + # { + # "name": "LogisticRegression_balanced", + # "model": LogisticRegression( + # max_iter=1000, + # class_weight="balanced", + # solver="liblinear", + # random_state=42, + # ), + # "smote": False, + # }, { - "name": "LGBM_FOCAL_LOSS", - "model": LGBMFocalWrapper( - n_estimators=500, + "name": "CatBoost_balanced", + "model": CatBoostClassifier( + iterations=500, learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, + depth=6, + class_weights=[1, scale_pos], random_state=42, - ), - "smote": True, - "smote_method": "kmeans", - }, - { - "name": "LGBM_SMOTE", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, - random_state=42, - verbose=-1, + verbose=0, n_jobs=-1, ), - "smote": True, - "smote_method": "smote", + "smote": False, }, { "name": "LGBM_KMEANS_SMOTE", @@ -70,212 +303,8 @@ models = [ ), "smote": True, "smote_method": "kmeans", - }, - { - "name": "LGBM_SVM_SMOTE", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": True, - "smote_method": "svm", - }, - { - "name": "LGBM_BORDERLINE_SMOTE", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": True, - "smote_method": "borderline", - }, - { - "name": "LGBM_ADASYN_SMOTE", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": True, - "smote_method": "adasyn", - }, - { - "name": "LGBM_Balanced", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, - class_weight="balanced", - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": False, - }, - { - "name": "LGBM_DART", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, - boosting_type="dart", - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": True, - "smote_method": "kmeans", - }, - { - "name": "LGBM_GOSS", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - boosting_type="goss", - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": True, - "smote_method": "kmeans", - }, - { - "name": "LGBM_RF", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - boosting_type="rf", - subsample=0.8, - colsample_bytree=0.8, - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": True, - "smote_method": "kmeans", - }, - { - "name": "LGBM_scale_pos_weight", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - scale_pos_weight=scale_pos, - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": False, - }, - { - "name": "LGBM_is_unbalance", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - is_unbalance=True, - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": False, - }, - { - "name": "LGBM_DART", - "model": LGBMClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=-1, - subsample=0.8, - colsample_bytree=0.8, - boosting_type="dart", - random_state=42, - verbose=-1, - n_jobs=-1, - ), - "smote": True, - "smote_method": "kmeans", - }, - { - "name": "XGB_scale_pos_weight", - "model": XGBClassifier( - n_estimators=500, - learning_rate=0.05, - max_depth=6, - scale_pos_weight=scale_pos, - random_state=42, - n_jobs=-1, - use_label_encoder=False, - eval_metric="logloss", - ), - "smote": False, - }, - { - "name": "CatBoost_balanced", - "model": CatBoostClassifier( - iterations=500, - learning_rate=0.05, - depth=6, - class_weights=[1, scale_pos], - random_state=42, - verbose=0, - ), - "smote": False, - }, - { - "name": "RandomForest_balanced", - "model": RandomForestClassifier( - n_estimators=500, - max_depth=None, - class_weight="balanced", - random_state=42, - n_jobs=-1, - ), - "smote": False, - }, - { - "name": "BalancedRandomForest", - "model": BalancedRandomForestClassifier( - n_estimators=500, - max_depth=None, - random_state=42, - n_jobs=-1, - ), - "smote": False, - }, - { - "name": "LogisticRegression_balanced", - "model": LogisticRegression( - max_iter=1000, - class_weight="balanced", - solver="liblinear", - random_state=42, - ), - "smote": False, - }, + } + ] @@ -308,7 +337,7 @@ for m in models: ) results_df = pandas.DataFrame(results_to_save) -csv_file = "lightgbm_results.csv" +csv_file = "lgbm_vs_cat_kmeans_smote_k10_results.csv" try: results_df.to_csv(csv_file, mode="a", index=False, header=False) diff --git a/train.py b/train.py index f2df775..93f8f14 100644 --- a/train.py +++ b/train.py @@ -43,7 +43,7 @@ def train_model_with_kfold( if smote: if smote_method.lower() == "kmeans": sampler = KMeansSMOTE( - k_neighbors=5, + k_neighbors=10, cluster_balance_threshold=0.1, random_state=random_state, )