├── README.md ├── data ├── assistments │ ├── builder_test.csv │ └── builder_train.csv └── synthetic │ ├── info │ ├── naive_c2_q50_s4000_v0_info.txt │ ├── naive_c2_q50_s4000_v10_info.txt │ ├── naive_c2_q50_s4000_v11_info.txt │ ├── naive_c2_q50_s4000_v12_info.txt │ ├── naive_c2_q50_s4000_v13_info.txt │ ├── naive_c2_q50_s4000_v14_info.txt │ ├── naive_c2_q50_s4000_v15_info.txt │ ├── naive_c2_q50_s4000_v16_info.txt │ ├── naive_c2_q50_s4000_v17_info.txt │ ├── naive_c2_q50_s4000_v18_info.txt │ ├── naive_c2_q50_s4000_v19_info.txt │ ├── naive_c2_q50_s4000_v1_info.txt │ ├── naive_c2_q50_s4000_v2_info.txt │ ├── naive_c2_q50_s4000_v3_info.txt │ ├── naive_c2_q50_s4000_v4_info.txt │ ├── naive_c2_q50_s4000_v5_info.txt │ ├── naive_c2_q50_s4000_v6_info.txt │ ├── naive_c2_q50_s4000_v7_info.txt │ ├── naive_c2_q50_s4000_v8_info.txt │ ├── naive_c2_q50_s4000_v9_info.txt │ ├── naive_c5_q50_s4000_v0_info.txt │ ├── naive_c5_q50_s4000_v10_info.txt │ ├── naive_c5_q50_s4000_v11_info.txt │ ├── naive_c5_q50_s4000_v12_info.txt │ ├── naive_c5_q50_s4000_v13_info.txt │ ├── naive_c5_q50_s4000_v14_info.txt │ ├── naive_c5_q50_s4000_v15_info.txt │ ├── naive_c5_q50_s4000_v16_info.txt │ ├── naive_c5_q50_s4000_v17_info.txt │ ├── naive_c5_q50_s4000_v18_info.txt │ ├── naive_c5_q50_s4000_v19_info.txt │ ├── naive_c5_q50_s4000_v1_info.txt │ ├── naive_c5_q50_s4000_v2_info.txt │ ├── naive_c5_q50_s4000_v3_info.txt │ ├── naive_c5_q50_s4000_v4_info.txt │ ├── naive_c5_q50_s4000_v5_info.txt │ ├── naive_c5_q50_s4000_v6_info.txt │ ├── naive_c5_q50_s4000_v7_info.txt │ ├── naive_c5_q50_s4000_v8_info.txt │ └── naive_c5_q50_s4000_v9_info.txt │ ├── naive_c2_q50_s4000_v0.csv │ ├── naive_c2_q50_s4000_v1.csv │ ├── naive_c2_q50_s4000_v10.csv │ ├── naive_c2_q50_s4000_v11.csv │ ├── naive_c2_q50_s4000_v12.csv │ ├── naive_c2_q50_s4000_v13.csv │ ├── naive_c2_q50_s4000_v14.csv │ ├── naive_c2_q50_s4000_v15.csv │ ├── naive_c2_q50_s4000_v16.csv │ ├── naive_c2_q50_s4000_v17.csv │ ├── naive_c2_q50_s4000_v18.csv │ ├── naive_c2_q50_s4000_v19.csv │ ├── naive_c2_q50_s4000_v2.csv │ ├── naive_c2_q50_s4000_v3.csv │ ├── naive_c2_q50_s4000_v4.csv │ ├── naive_c2_q50_s4000_v5.csv │ ├── naive_c2_q50_s4000_v6.csv │ ├── naive_c2_q50_s4000_v7.csv │ ├── naive_c2_q50_s4000_v8.csv │ ├── naive_c2_q50_s4000_v9.csv │ ├── naive_c5_q50_s4000_v0.csv │ ├── naive_c5_q50_s4000_v1.csv │ ├── naive_c5_q50_s4000_v10.csv │ ├── naive_c5_q50_s4000_v11.csv │ ├── naive_c5_q50_s4000_v12.csv │ ├── naive_c5_q50_s4000_v13.csv │ ├── naive_c5_q50_s4000_v14.csv │ ├── naive_c5_q50_s4000_v15.csv │ ├── naive_c5_q50_s4000_v16.csv │ ├── naive_c5_q50_s4000_v17.csv │ ├── naive_c5_q50_s4000_v18.csv │ ├── naive_c5_q50_s4000_v19.csv │ ├── naive_c5_q50_s4000_v2.csv │ ├── naive_c5_q50_s4000_v3.csv │ ├── naive_c5_q50_s4000_v4.csv │ ├── naive_c5_q50_s4000_v5.csv │ ├── naive_c5_q50_s4000_v6.csv │ ├── naive_c5_q50_s4000_v7.csv │ ├── naive_c5_q50_s4000_v8.csv │ └── naive_c5_q50_s4000_v9.csv └── scripts ├── dataAssist.lua ├── dataSynthetic.lua ├── rnn.lua ├── trainAssist.lua ├── trainSynthetic.lua ├── util.lua └── utilExp.lua /README.md: -------------------------------------------------------------------------------- 1 | # DeepKnowledgeTracing 2 | source code for the paper Deep Knowledge Tracing. http://stanford.edu/~cpiech/bio/papers/deepKnowledgeTracing.pdf 3 | 4 | At the moment the code doesn't include LSTM model, only RNN (I will upload LSTM when I get a chance). 5 | 6 | Questions people have asked me: 7 | ---------- 8 | ``` 9 | Q. How do you handle multiple students during training? It looks like sequences (ie. data of different 10 | students) of different length are padded to the same length. 11 | A. Correct. This is very important for training speed. 12 | ``` 13 | 14 | ``` 15 | Q. Does the training code have a termination condition? 16 | A. No. I save a copy of the model each epoch and let training run until I feel like terminating it. You can 17 | start training 18 | from any saved model (where you left off). 19 | ``` 20 | -------------------------------------------------------------------------------- /data/synthetic/info/naive_c2_q50_s4000_v0_info.txt: -------------------------------------------------------------------------------- 1 | 0.642821410705353 0.47928797249920774 0 -0.8653712117353465 2 | 0.4062031015507755 0.4912462352364784 1 1.3260072066765187 3 | 0.5657828914457228 0.4957777430037612 0 0.06677241338992082 4 | 0.5767883941970986 0.4941919834050663 0 -0.05400141636641748 5 | 0.7093546773386693 0.4541737636350896 0 -1.2165347994628688 6 | 0.661330665332666 0.47337565920718633 1 -1.1691152062733108 7 | 0.712856428214107 0.4525423620910444 0 -1.1552097558514987 8 | 0.7368684342171088 0.4404433999819835 0 -1.4312146514290613 9 | 0.744872436218109 0.4360419748596002 0 -1.4191915824310621 10 | 0.643821910955478 0.4789885492686513 0 -0.033218008299181714 11 | 0.6018009004502249 0.4896493795503955 0 0.43691464023819143 12 | 0.7353676838419206 0.4412476082972228 0 -0.894360004151828 13 | 0.4392196098049025 0.4964161765252693 1 1.240174304655607 14 | 0.6653326663331667 0.47199211163099136 0 -0.3177417937303213 15 | 0.5397698849424714 0.4985405597976034 0 1.2057662064268246 16 | 0.7198599299649823 0.44917985588528964 0 -0.5266059796696169 17 | 0.5942971485742872 0.4911504071761964 1 -0.13695778891773963 18 | 0.6113056528264134 0.4875756106484019 1 -0.21945272244192665 19 | 0.753376688344172 0.43115339140932 0 -0.6839133386749444 20 | 0.64032016008004 0.4800265857969209 1 -0.4079449562558323 21 | 0.8334167083541771 0.3726966390050952 0 -1.672810275071511 22 | 0.7478739369684841 0.4343418987399919 0 -0.5370473017240149 23 | 0.5777888944472235 0.49403540808445573 0 1.4666155866460928 24 | 0.5747873936968485 0.4944989094959148 1 0.3276939168435907 25 | 0.5917958979489745 0.49162426789568986 1 0.22648282503800324 26 | 0.8424212106053026 0.36443676382215334 0 -1.7218791385242522 27 | 0.4832416208104052 0.4998441169903538 1 1.5831913512132465 28 | 0.8804402201100552 0.3245272318733532 0 -2.185870593753447 29 | 0.7473736868434221 0.4346271467414963 0 -0.19392008579898953 30 | 0.655327663831916 0.4753802336621199 1 -0.13554595063368285 31 | 0.6778389194597297 0.4674212390132925 1 -0.48627871051034877 32 | 0.7393696848424214 0.43908837550423474 0 0.10417556975013098 33 | 0.5892946473236618 0.4920849523473396 1 0.71087977493687 34 | 0.7678839419709853 0.42228829244866084 0 -0.021057270932174563 35 | 0.6023011505752875 0.4895450558583864 1 0.5525908356363549 36 | 0.7193596798399198 0.4494244903959617 0 0.56300394608483 37 | 0.6248124062031014 0.48429246485741856 1 0.6224373690205246 38 | 0.7833916958479239 0.412036498659165 0 -0.160148604864588 39 | 0.7018509254627313 0.45755976300875245 0 0.9325388667227809 40 | 0.8029014507253623 0.39790691839751774 0 -0.06483485142482599 41 | 0.6843421710855426 0.46489362337910695 1 7.834395129571537E-4 42 | 0.8099049524762383 0.39247417360564985 1 -1.3479940531221064 43 | 0.6853426713356676 0.4644954533267629 1 0.14543920494923257 44 | 0.8029014507253626 0.39790691839751763 0 -0.13376039779426024 45 | 0.826913456728364 0.37841673752788996 0 -0.42453551304755066 46 | 0.6478239119559779 0.47776801827642895 0 1.9662822426611546 47 | 0.7373686843421712 0.44017386641246287 0 1.0774695253710045 48 | 0.7008504252126062 0.45800004495606317 1 0.26932814110085684 49 | 0.8589294647323662 0.34818139641870305 0 -0.461444043918184 50 | 0.7198599299649823 0.449179855885289 1 -0.010420482389746896 51 | -------------------------------------------------------------------------------- /data/synthetic/info/naive_c2_q50_s4000_v10_info.txt: -------------------------------------------------------------------------------- 1 | 0.29764882441220614,0.457338641895403,1,2.456568813759531 2 | 0.4612306153076537,0.4986194015988851,0,0.7633451693019683 3 | 0.5532766383191594,0.49727789445522375,0,0.05406524513200592 4 | 0.6273136568284141,0.4836406159680989,0,-0.7165350622288362 5 | 0.707353676838419,0.45509126396116567,1,-1.5356618185650395 6 | 0.5987993996998501,0.4902641310536902,0,-0.0974711847945554 7 | 0.37568784392196103,0.4844211788163068,0,2.273892063964383 8 | 0.5957978989494749,0.4908597503055818,0,0.1427268433609692 9 | 0.6283141570785392,0.4833760044896621,0,-0.2416004986080984 10 | 0.446223111555778,0.4972240178934482,1,1.2116250986660153 11 | 0.6878439219609804,0.46348907838006653,0,-0.5732325210128255 12 | 0.56928464232116,0.49530027345050187,0,0.5900586116699692 13 | 0.5732866433216608,0.4947236651723471,1,-0.020844457410159216 14 | 0.45172586293146555,0.49778867650128106,1,1.4530547315536977 15 | 0.5752876438219111,0.49442295543518555,1,0.3214421535831839 16 | 0.6768384192096047,0.4678008626026151,1,-0.7608321755840909 17 | 0.6758379189594796,0.4681780392321895,1,-0.5947348159754183 18 | 0.5652826413206603,0.4958438956190299,1,0.6569806258803448 19 | 0.5682841420710355,0.49543926708632013,0,0.6297068959254786 20 | 0.6423211605802902,0.47943683064824666,1,-0.036517685414084795 21 | 0.6548274137068535,0.47554347785265216,0,-0.12159749389191593 22 | 0.6963481740870434,0.45994915377623574,1,-0.560003376392585 23 | 0.530265132566283,0.499208061261332,0,1.3445057545770376 24 | 0.4572286143071532,0.4982919004757248,1,1.9372551998803575 25 | 0.5637818909454726,0.49603927204571485,1,0.8848650232894181 26 | 0.8939469734867433,0.30798251966956597,0,-2.8324118165966583 27 | 0.6013006503251626,0.48975316979260186,1,0.767155343801457 28 | 0.7658829414707352,0.4235516548290894,1,-0.9791870964834919 29 | 0.5897948974487242,0.4919938677665584,0,0.8933110457819663 30 | 0.7763881940970485,0.416769070155795,0,-0.9389571007091599 31 | 0.6873436718359177,0.46369160802852055,0,0.12168727215762117 32 | 0.565782891445723,0.4957777430037613,1,1.2398285297955918 33 | 0.7808904452226114,0.41374653315459425,0,-1.0122576471174316 34 | 0.6813406703351677,0.46607319996914864,0,0.3861268785308983 35 | 0.778889444722361,0.41509863184284945,1,-0.6147191590027592 36 | 0.8249124562281139,0.3801370595245161,1,-1.4069634487675975 37 | 0.7663831915957977,0.4232370551386869,1,-0.3743820213115447 38 | 0.5882941470735368,0.49226554509497555,1,1.575309319718254 39 | 0.7798899449724861,0.4144243422259108,1,-0.45659451860431105 40 | 0.7718859429714855,0.41972152810960295,1,-0.05870703456162573 41 | 0.7998999499749875,0.40017512432073904,1,-0.44403565086868124 42 | 0.8254127063531764,0.3797086988134279,0,-1.193801480404036 43 | 0.7733866933466736,0.41874506979365433,0,-0.507150942346616 44 | 0.7033516758379192,0.45689443464585144,1,0.8059520522030299 45 | 0.6673336668334169,0.4712860646962635,0,0.8482057137963543 46 | 0.8159079539769886,0.387656214799442,0,-0.8039841972936509 47 | 0.7838919459729864,0.41169181493035784,0,-0.3233700237091476 48 | 0.8694347173586795,0.3370086132737924,1,-1.3789343142019985 49 | 0.8904452226113058,0.312412152819267,1,-1.7117945386405262 50 | 0.6148074037018506,0.48676255835204774,0,1.6038134786388216 51 | -------------------------------------------------------------------------------- /data/synthetic/info/naive_c2_q50_s4000_v11_info.txt: -------------------------------------------------------------------------------- 1 | 0.5417708854427215,0.49837681023272434,1,0.009952771183529325 2 | 0.5137568784392195,0.4999357745752573,1,0.3814113744798141 3 | 0.4967483741870934,0.5001145336449145,0,0.7019445771163696 4 | 0.5157578789394698,0.4998766748877194,0,0.6242042742304645 5 | 0.5987993996998501,0.4902641310536905,0,-0.1714084254332477 6 | 0.8309154577288647,0.37492036172207965,0,-2.4629501311641646 7 | 0.46873436718359196,0.499146367381907,1,0.963578013849462 8 | 0.511255627813907,0.49999837255699586,0,0.8592287809139653 9 | 0.4882441220610307,0.4999868551167244,0,1.240357613090589 10 | 0.6483241620810404,0.477612873600059,0,-0.22547681271759512 11 | 0.5717858929464732,0.4949437660104406,1,0.07359991196588167 12 | 0.8449224612306153,0.3620691585406188,0,-2.4560478052019414 13 | 0.6588294147073537,0.47422116794944136,0,-0.11282450432847284 14 | 0.6113056528264134,0.4875756106484018,0,0.49455619955549457 15 | 0.7318659329664832,0.443098641886279,1,-1.3930184033330735 16 | 0.660330165082541,0.4737156294055221,1,-0.7015629729604218 17 | 0.7558779389694845,0.4296729406705765,1,-1.4638998278845545 18 | 0.6648324162081044,0.47216713273049915,0,0.03619652110776052 19 | 0.5917958979489745,0.49162426789568997,0,0.8838618666703342 20 | 0.6183091545772887,0.4859228987553138,1,-0.04079555134038739 21 | 0.39869934967483744,0.4897531697926019,1,2.030645994142564 22 | 0.7923961980990494,0.4056929847846196,1,-1.6585039326638467 23 | 0.7143571785892949,0.45183307494566194,0,-0.2730311696913266 24 | 0.7403701850925459,0.4385411969392673,0,-0.4368980865671783 25 | 0.7968984492246126,0.4024081479171423,0,-1.15445077416648 26 | 0.7928964482241119,0.4053317883426326,0,-0.8559993423685323 27 | 0.7718859429714858,0.4197215281096029,1,-1.3623122650865254 28 | 0.4942471235617809,0.5000920045086306,1,1.5650580424585767 29 | 0.7893946973486742,0.4078405542728808,0,-0.9070713284838421 30 | 0.773886943471736,0.4184178805301668,0,-0.4803731499419711 31 | 0.6068034017008501,0.488582080430447,0,1.4954628554290104 32 | 0.7753876938469235,0.4174311752309468,0,-0.4262857286673544 33 | 0.5197598799399704,0.4997344063080246,0,2.416459275254067 34 | 0.7823911955977984,0.4127231826151588,0,-0.10098865505511308 35 | 0.6078039019509754,0.48836218853630226,0,1.8846224248147794 36 | 0.5502751375687845,0.4975904647476495,1,1.0245585222873392 37 | 0.7838919459729865,0.41169181493035834,0,0.0782025191181913 38 | 0.6943471735867934,0.4607986511782446,1,-0.2984837350211064 39 | 0.6178089044522262,0.4860444843236998,1,0.7352809104243878 40 | 0.7888944472236117,0.40819523725282864,1,-0.8643996239802133 41 | 0.5587793896948472,0.4966572049559477,1,1.3777200131304803 42 | 0.6848424212106051,0.46469485039682423,1,0.2910422468186578 43 | 0.6918459229614808,0.4618461281836706,1,0.3963002162459853 44 | 0.769384692346173,0.42133204515923894,0,0.24264506377693135 45 | 0.7878939469734867,0.4089018433265611,0,0.0366186943674219 46 | 0.7773886943471735,0.416103504694287,1,-0.4532034253745995 47 | 0.8009004502251125,0.399422994234331,0,-0.02343956017462154 48 | 0.8694347173586795,0.33700861327379317,0,-0.9134273592689981 49 | 0.6773386693346675,0.4676113570494866,1,0.799926731080481 50 | 0.8694347173586794,0.33700861327379356,0,-0.81784539348907 51 | -------------------------------------------------------------------------------- /data/synthetic/info/naive_c2_q50_s4000_v12_info.txt: -------------------------------------------------------------------------------- 1 | 0.5707853926963484,0.4950879169523821,1,-0.18943958790334237 2 | 0.5262631315657832,0.49943470826948105,0,0.355286137772209 3 | 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0.6043021510755378,0.48912241973955467,3,0.09319852372266531 33 | 0.6023011505752877,0.48954505585838665,0,0.24040023024604604 34 | 0.6768384192096049,0.46780086260261516,4,-0.6805030635436827 35 | 0.5577788894447223,0.4967746513146154,1,0.3810887989068995 36 | 0.6288144072036016,0.4832428672430505,0,0.14908585255339044 37 | 0.7113556778389191,0.45324556765745927,3,-0.8718138734063074 38 | 0.6733366683341668,0.4691103139963966,0,-0.1495059352036749 39 | 0.37368684342171093,0.4839030131215301,1,2.4988380291851584 40 | 0.48074037018509275,0.49975394558942127,4,1.4406262829307623 41 | 0.7223611805902951,0.4479462953271296,4,-0.9965210403001242 42 | 0.6323161580790397,0.4822953419751578,2,-0.32500922871809773 43 | 0.7568784392196097,0.42907524549508,3,-1.2701204555427046 44 | 0.8199099549774888,0.3843585870243594,4,-1.9419470272784243 45 | 0.6073036518259131,0.48847240314095414,3,0.26703719191128017 46 | 0.7383691845922963,0.43963259500346163,1,-1.243104176591578 47 | 0.7038519259629816,0.45667134659823627,2,-0.8722006069396002 48 | 0.7248624312156078,0.44669531594766126,1,-0.9946471642919605 49 | 0.6843421710855426,0.464893623379107,3,-0.276781739521663 50 | 0.44572286143071554,0.49716963189186486,1,1.8911400262700124 51 | -------------------------------------------------------------------------------- /data/synthetic/info/naive_c5_q50_s4000_v8_info.txt: -------------------------------------------------------------------------------- 1 | 0.5827913956978492,0.49322132890346254,0,-0.16841368134704393 2 | 0.7593796898449224,0.42756710471777953,3,-2.1684210951467446 3 | 0.6173086543271634,0.4861655244845436,3,-0.4924976161924919 4 | 0.4787393696848425,0.4996727774156707,2,0.7161078125858718 5 | 0.5312656328164082,0.49914636738190693,4,0.2997025878706721 6 | 0.5772886443221615,0.4941139553047922,4,-0.10640656766258128 7 | 0.6863431715857926,0.4640947837030714,2,-1.1419564829182047 8 | 0.5352676338169087,0.4988794409519798,2,0.2902030568578953 9 | 0.5192596298149073,0.4997539455894206,3,0.4804325090564881 10 | 0.5147573786893449,0.49990722729346443,1,0.2686579919449209 11 | 0.6118059029514762,0.48746108424553247,1,-0.39251265508669503 12 | 0.4692346173086545,0.499177466062781,0,0.969893966096941 13 | 0.4392196098049023,0.4964161765252689,2,1.709400753891192 14 | 0.5612806403201601,0.496354640060008,4,0.2419252697472461 15 | 0.41370685342671315,0.4926204329897938,3,1.7409906070081957 16 | 0.5357678839419712,0.4988438065089807,4,0.39755030975640804 17 | 0.7053526763381691,0.45599813319691784,2,-1.046807706015648 18 | 0.6408204102051026,0.47987999757825534,2,-0.3252689955891307 19 | 0.5832916458229116,0.4931370546206009,0,0.02346073589983389 20 | 0.39469734867433715,0.48890789201530477,1,1.8474173812650414 21 | 0.6113056528264132,0.4875756106484018,3,-0.2210910953427572 22 | 0.5257628814407206,0.4994607761073696,0,0.5924942614599275 23 | 0.6273136568284141,0.4836406159680989,3,-0.2679465869266452 24 | 0.5242621310655328,0.49953596417191726,3,1.0179788489840311 25 | 0.49524762381190585,0.5001025182318518,4,0.8453484304859973 26 | 0.6443221610805404,0.4788379831197643,1,-0.5612289678718331 27 | 0.645822911455728,0.4783828600777195,0,-0.1572330479226111 28 | 0.6803401700850428,0.46646143528996903,3,-0.4564956164762619 29 | 0.6963481740870434,0.45994915377623574,3,-0.5618142989386165 30 | 0.6483241620810404,0.4776128736000589,3,-0.11858092770858354 31 | 0.7098549274637318,0.4539427199079623,1,-1.061252984614709 32 | 0.6263131565782889,0.48390301312152967,3,0.2933774687897459 33 | 0.6613306653326663,0.4733756592071849,2,-0.5391612792605451 34 | 0.870935467733867,0.3353552422076333,2,-2.8247375674411113 35 | 0.7228614307153576,0.44769749760022626,1,-1.2263090072138845 36 | 0.7153576788394198,0.45135682404548255,3,-0.6850375006896247 37 | 0.5822911455727869,0.49330508124265177,2,0.6035678905961259 38 | 0.6973486743371684,0.4595205470053696,2,-0.4406641351340278 39 | 0.48574287143571776,0.4999217515528178,1,1.381998659845287 40 | 0.47773886943471733,0.49962918165784376,1,1.5513506740974465 41 | 0.6823411705852926,0.46568249037586346,3,-0.05046967625678967 42 | 0.6703351675837919,0.4702090325090657,0,-0.49953905806780147 43 | 0.6733366683341669,0.4691103139963963,2,-0.14861798030836723 44 | 0.6333166583291647,0.4820196035181618,0,-0.2535991773319848 45 | 0.620310155077539,0.48543109418240193,2,0.5739575143618177 46 | 0.5567783891945972,0.49689005437757827,4,0.5643930312701683 47 | 0.6258129064532264,0.484033382452931,0,-0.010065931913272973 48 | 0.7478739369684844,0.43434189873999196,4,-1.3858411678251201 49 | 0.5212606303151575,0.4996727774156709,0,1.2507384869150786 50 | 0.5542771385692845,0.49716963189186475,2,1.0955673241323678 51 | -------------------------------------------------------------------------------- /data/synthetic/info/naive_c5_q50_s4000_v9_info.txt: -------------------------------------------------------------------------------- 1 | 0.5767883941970986,0.49419198340506615,3,-0.284652135974575 2 | 0.5512756378189096,0.49748830959124296,3,0.04868292016224166 3 | 0.6553276638319163,0.47538023366211996,3,-0.7098728314627069 4 | 0.754377188594297,0.43056356648546823,1,-1.9947137188747261 5 | 0.5047523761880942,0.5001025182318518,2,0.3716107906892738 6 | 0.7073536768384191,0.4550912639611658,0,-1.701588309434496 7 | 0.5887943971985997,0.4921755113601384,4,-0.30697795903023195 8 | 0.4817408704352177,0.49979151898356167,4,0.6137904806112446 9 | 0.5042521260630308,0.5001070240455919,3,0.657533362108285 10 | 0.6923461730865434,0.46163790765262896,3,-0.8083869644976258 11 | 0.5422711355677838,0.4983346083748995,3,0.686093168720599 12 | 0.5697848924462231,0.49523000364314196,2,-0.17759574381947832 13 | 0.5332666333166586,0.4990169359296088,2,0.31398946817495177 14 | 0.5042521260630312,0.5001070240455916,1,0.45755608452073343 15 | 0.6483241620810404,0.47761287360005905,4,-0.7450868485862965 16 | 0.5962981490745374,0.49076180607488096,0,-0.31687197330298267 17 | 0.7478739369684841,0.43434189873999157,4,-1.756125371838194 18 | 0.534267133566783,0.49894919678892913,3,0.801606452081699 19 | 0.5912956478239118,0.49171745768709235,1,-0.28279840470980094 20 | 0.4177088544272135,0.4933050812426516,4,1.6599695614605534 21 | 0.6763381690845428,0.467989756416691,3,-0.6140585252295025 22 | 0.41770885442721345,0.49330508124265166,1,1.5098831659554655 23 | 0.5802901450725363,0.49363487645139015,1,0.07913120538390132 24 | 0.4057028514257134,0.49115040717619624,4,1.8836732236252283 25 | 0.5642821410705354,0.49597465993689643,3,0.775361197443957 26 | 0.6938469234617308,0.4610094231873781,3,-0.38599810987625516 27 | 0.6818409204602301,0.46587815484471146,3,-0.23538417458543395 28 | 0.5002501250625311,0.5001250468945414,4,1.068432878854952 29 | 0.6353176588294147,0.48146141250889396,2,-0.541846867730555 30 | 0.5302651325662834,0.4992080612613314,4,0.9206607932878463 31 | 0.5517758879439721,0.49743646914931433,3,1.2271734650260164 32 | 0.4772386193096549,0.49960663063520533,1,1.2658511101451093 33 | 0.7033516758379191,0.45689443464585144,4,-0.8534538166743397 34 | 0.5212606303151573,0.4996727774156708,3,1.656871834999357 35 | 0.6143071535767882,0.48688033454230145,1,-0.015264619089591213 36 | 0.7553776888444221,0.42997060323015907,4,-1.099323781045576 37 | 0.7418709354677337,0.4377148565519858,3,-0.5350123854537302 38 | 0.5462731365682842,0.49797876489264076,0,0.2512192832337263 39 | 0.551775887943972,0.4974364691493147,3,1.4531199123513225 40 | 0.6568284142071035,0.4748870010189456,3,0.4745449076374619 41 | 0.5727863931965986,0.4947975490131941,1,0.36447193470092076 42 | 0.3791895947973986,0.48530677538853284,0,2.0017806367268687 43 | 0.5972986493246626,0.4905643278030859,1,0.36917790674574946 44 | 0.5797898949474737,0.4937160230226799,2,0.20128859151743772 45 | 0.5292646323161587,0.4992677415801489,2,0.6507925481600297 46 | 0.7318659329664833,0.4430986418862791,4,-0.9493855631200334 47 | 0.46023011505752864,0.4985405597976033,0,1.2651870581101412 48 | 0.5927963981990996,0.4914363068707381,1,0.4242093292564893 49 | 0.7708854427213606,0.42036826219286516,0,-1.8955725045363665 50 | 0.6018009004502249,0.4896493795503952,2,0.009893913735280086 51 | -------------------------------------------------------------------------------- /scripts/dataAssist.lua: -------------------------------------------------------------------------------- 1 | require 'csvigo' 2 | require 'util' 3 | require 'string' 4 | local class = require 'class' 5 | 6 | DataAssistMatrix = class.new('DataAssistMatrix') 7 | 8 | local max_train = nil 9 | local max_steps = nil 10 | 11 | -- Function: Get Data 12 | -- Reads a CSV in standard feature format. Each row is a student 13 | -- and each col is the score for the student on the 14 | -- corresponding item. 15 | function DataAssistMatrix:__init(params) 16 | print('Loading khan...') 17 | --train 18 | local root = '../data/assistments/' 19 | local trainPath = root .. 'builder_train.csv' 20 | local file = io.open(trainPath, "rb") 21 | io.input(file) 22 | local count = 0 23 | local trainData = {} 24 | local longest = 0 25 | 26 | self.questions = {} 27 | self.n_questions = 0 28 | local totalAnswers = 0 29 | while(true) do 30 | local student = self:loadStudent() 31 | if(student == nil) then 32 | break 33 | end 34 | if(student['n_answers'] >= 2) then 35 | table.insert(trainData, student) 36 | end 37 | if(#trainData % 100 == 0) then 38 | print(#trainData) 39 | end 40 | if(student['n_answers'] > longest) then 41 | longest = student['n_answers'] 42 | end 43 | totalAnswers = totalAnswers+ student['n_answers'] 44 | end 45 | 46 | self.trainData = trainData 47 | io.close() 48 | 49 | --test 50 | local testPath = root .. 'builder_test.csv' 51 | local testFile = io.open(testPath, "rb") 52 | io.input(testFile) 53 | local count = 0 54 | local testData = {} 55 | 56 | self.questions = {} 57 | while(true) do 58 | local student = self:loadStudent() 59 | if(student == nil) then 60 | break 61 | end 62 | if(student['n_answers'] >= 2) then 63 | table.insert(testData, student) 64 | end 65 | if(#testData % 100 == 0) then 66 | print(#testData) 67 | end 68 | if(student['n_answers'] > longest) then 69 | --longest = student['n_answers'] 70 | end 71 | totalAnswers = totalAnswers+ student['n_answers'] 72 | end 73 | 74 | self.testData = testData 75 | io.close() 76 | print('total answers', totalAnswers) 77 | print('longest', longest) 78 | end 79 | 80 | function DataAssistMatrix:loadStudent() 81 | local nStepsStr = io.read() 82 | local questionIdStr = io.read() 83 | local correctStr = io.read() 84 | if(nStepsStr == nil or questionIdStr == nil or correctStr == nil) then 85 | return nil 86 | end 87 | 88 | local n = tonumber(nStepsStr) 89 | if(max_steps ~= nil) then 90 | n = max_steps 91 | end 92 | 93 | local student = {} 94 | student['questionId'] = torch.zeros(n):byte() 95 | for i,id in ipairs(split(questionIdStr, ",")) do 96 | if(i > n) then 97 | break 98 | end 99 | student['questionId'][i] = tonumber(id) + 1 100 | if self.questions[id] == nil then 101 | self.questions[id] = true 102 | self.n_questions = self.n_questions + 1 103 | end 104 | 105 | end 106 | 107 | student['correct'] = torch.zeros(n):byte() 108 | for i, val in ipairs(split(correctStr, ",")) do 109 | if(i > n) then 110 | break 111 | end 112 | student['correct'][i] = val 113 | end 114 | 115 | student['n_answers'] = n 116 | return student 117 | end 118 | 119 | 120 | 121 | function DataAssistMatrix:getTestData() 122 | return self.testData 123 | end 124 | 125 | function DataAssistMatrix:getTrainData() 126 | return self.trainData 127 | end 128 | 129 | function DataAssistMatrix:getTestBatch() 130 | local batch = {} 131 | for id,answers in pairs(self.testData) do 132 | table.insert(batch, answers) 133 | end 134 | return batch; 135 | end 136 | 137 | -------------------------------------------------------------------------------- /scripts/dataSynthetic.lua: -------------------------------------------------------------------------------- 1 | require 'csvigo' 2 | local class = require 'class' 3 | 4 | Data = class.new('Data') 5 | 6 | -- Function: Get Data 7 | -- Reads a CSV in standard feature format. Each row is a student 8 | -- and each col is the score for the student on the 9 | -- corresponding item. 10 | function Data:__init(params) 11 | local name = self:getName(params) 12 | print('Loading ' .. name) 13 | local path = '../data/synthetic/' .. name 14 | local rawData = csvigo.load({path=path, mode='raw', header=false}) 15 | local totalStudents = table.getn(rawData) 16 | local n_questions = table.getn(rawData[1]) 17 | local n_steps = n_questions - 1 18 | local n_students = totalStudents / 2 19 | local data = torch.Tensor(totalStudents, n_questions) 20 | for r,row in ipairs(rawData) do 21 | for c,v in ipairs(row) do 22 | data[r][c] = v; 23 | end 24 | end 25 | 26 | self.n_questions = n_questions 27 | self.n_students = n_students 28 | self.n_steps = n_steps 29 | 30 | self.nTest = self.n_students 31 | self.nTrain = self.n_students 32 | 33 | local trainData = torch.Tensor(data:narrow(1, 1, n_students)) 34 | local testData = torch.Tensor(data:narrow(1, n_students, n_students)) 35 | trainData = self:compressData(trainData) 36 | testData = self:compressData(testData) 37 | 38 | self.trainData = trainData 39 | self.testData = testData 40 | end 41 | 42 | function Data:getName(params) 43 | local name = 'naive'; 44 | name = name .. '_c' .. params['c']; 45 | name = name .. '_q' .. params['q']; 46 | name = name .. '_s' .. params['n']; 47 | name = name .. '_v' .. params['v'] 48 | name = name ..'.csv' 49 | return name 50 | end 51 | 52 | function Data:compressData(dataset) 53 | local newDataset = {} 54 | for i = 1,self.n_students do 55 | local answers = self:compressAnswers(dataset[i]) 56 | table.insert(newDataset, answers) 57 | end 58 | return newDataset 59 | end 60 | 61 | function Data:compressAnswers(answers) 62 | local newAnswers = {} 63 | newAnswers['questionId'] = torch.zeros(self.n_questions) 64 | newAnswers['time'] = torch.zeros(self.n_questions) 65 | newAnswers['correct'] = torch.zeros(self.n_questions) 66 | newAnswers['n_answers'] = self.n_questions 67 | for i = 1, self.n_questions do 68 | newAnswers['questionId'][i] = i 69 | newAnswers['correct'][i] = answers[i] 70 | end 71 | return newAnswers 72 | end 73 | 74 | function Data:getTestData() 75 | return self.testData 76 | end 77 | 78 | function Data:getTrainData() 79 | return self.trainData 80 | end -------------------------------------------------------------------------------- /scripts/rnn.lua: -------------------------------------------------------------------------------- 1 | require 'nn' 2 | require 'nngraph' 3 | require 'util' 4 | require 'utilExp' 5 | require 'lfs' 6 | local class = require 'class' 7 | 8 | RNN = class.new('RNN') 9 | 10 | 11 | function RNN:__init(params) 12 | self.n_questions = params['n_questions'] 13 | self.n_hidden = params['n_hidden'] 14 | self.use_dropout = params['dropout'] 15 | self.max_grad = params['maxGrad'] 16 | self.dropoutPred = params['dropoutPred'] 17 | self.max_steps = params['maxSteps'] 18 | 19 | self.n_input = self.n_questions * 2 20 | self.compressedSensing = params['compressedSensing'] 21 | if(self.compressedSensing) then 22 | self.n_input = params['compressedDim'] 23 | torch.manualSeed(12345) 24 | self.basis = torch.randn(self.n_questions * 2, self.n_input) 25 | end 26 | if(params['modelDir'] ~= nil) then 27 | self:load(params['modelDir']) 28 | else 29 | self:build(params) 30 | end 31 | print('rnn made') 32 | end 33 | 34 | function RNN:build(params) 35 | 36 | -- The transfer parameters 37 | local transfer = nn.Linear(self.n_hidden, self.n_hidden) 38 | 39 | -- The first layer 40 | local start = nn.Linear(1, self.n_hidden) 41 | 42 | -- Prototypical layer 43 | local inputM = nn.Identity()(); -- the memory input 44 | local inputX = nn.Identity()(); -- the last student activity 45 | local inputY = nn.Identity()(); -- the next question answered 46 | local truth = nn.Identity()(); -- whether the next question is correct 47 | 48 | local linM = transfer:clone('weight', 'bias')(inputM); 49 | local linX = nn.Linear(self.n_input, self.n_hidden)(inputX); 50 | local madd = nn.CAddTable()({linM, linX}); 51 | local hidden = nn.Tanh()(madd); 52 | 53 | local predInput = nil 54 | if(self.dropoutPred) then 55 | predInput = nn.Dropout()(hidden) 56 | else 57 | predInput = hidden 58 | end 59 | 60 | local linY = nn.Linear(self.n_hidden, self.n_questions)(predInput); 61 | local pred_output = nn.Sigmoid()(linY); 62 | local pred = nn.Sum(2)(nn.CMulTable()({pred_output, inputY})); 63 | local err = nn.BCECriterion()({pred, truth}) 64 | 65 | linX:annotate{name='linX'} 66 | linY:annotate{name='linY'} 67 | linM:annotate{name='linM'} 68 | 69 | local layer = nn.gModule({inputM, inputX, inputY, truth}, {pred, err, hidden}); 70 | 71 | self.start = start; 72 | self.layer = layer; 73 | 74 | self:rollOutNetwork() 75 | end 76 | 77 | function RNN:rollOutNetwork() 78 | self.layers = self:cloneManyTimes(self.max_steps) 79 | for i = 1,self.max_steps do 80 | self.layers[i]:training() 81 | end 82 | end 83 | 84 | function RNN:zeroGrad(n_steps) 85 | self.start:zeroGradParameters() 86 | self.layer:zeroGradParameters() 87 | end 88 | 89 | function RNN:update(n_steps, rate) 90 | self.start:updateParameters(rate) 91 | self.layer:updateParameters(rate) 92 | end 93 | 94 | function RNN:fprop(batch) 95 | local n_steps = getNSteps(batch) 96 | local n_students = #batch 97 | assert(n_steps >= 1) 98 | assert(n_steps < self.max_steps) 99 | local inputs = {}; 100 | local sumErr = 0; 101 | local numTests = 0 102 | local state = self.start:forward(torch.zeros(n_students, 1)) 103 | for k = 1,n_steps do 104 | local inputX, inputY, truth = self:getInputs(batch, k) 105 | local mask = self:getMask(batch, k) 106 | inputs[k] = {state, inputX, inputY, truth}; 107 | local output = self.layers[k]:forward(inputs[k]); 108 | state = output[3] 109 | local stepErr = output[2][1] * n_students -- scalar 110 | numTests = mask:sum() + numTests 111 | sumErr = sumErr + stepErr; 112 | end 113 | return sumErr, numTests, inputs 114 | end 115 | 116 | function RNN:save(dir) 117 | lfs.mkdir(dir) 118 | torch.save(dir .. '/start.dat', self.start) 119 | torch.save(dir .. '/layer.dat', self.layer) 120 | end 121 | 122 | function RNN:load(dir) 123 | self.start = torch.load(dir .. '/start.dat') 124 | self.layer = torch.load(dir .. '/layer.dat') 125 | self:rollOutNetwork() 126 | end 127 | 128 | function RNN:calcGrad(batch, rate, alpha) 129 | local n_steps = getNSteps(batch) 130 | local n_students = #batch 131 | if(n_steps > self.max_steps) then 132 | print(n_steps, self.max_steps) 133 | end 134 | assert(n_steps <= self.max_steps) 135 | 136 | local maxNorm = 0 137 | local sumErr, numTests, inputs = self:fprop(batch) 138 | 139 | local parentGrad = torch.zeros(n_students, self.n_hidden) 140 | for k = n_steps,1,-1 do 141 | local layerGrad = self.layers[k]:backward(inputs[k], { 142 | torch.zeros(n_students), 143 | torch.ones(n_students):mul(alpha), 144 | parentGrad 145 | }) 146 | parentGrad = layerGrad[1] -- because state is the first input 147 | 148 | end 149 | self.start:backward(torch.zeros(n_students, 1), parentGrad) 150 | return sumErr, numTests, maxNorm; 151 | end 152 | 153 | function RNN:getMask(batch, k) 154 | local mask = torch.zeros(#batch) 155 | for i,ans in ipairs(batch) do 156 | if(k + 1 <= ans['n_answers']) then 157 | mask[i] = 1 158 | end 159 | end 160 | return mask 161 | end 162 | 163 | function RNN:getInputs(batch, k) 164 | local n_students = #batch 165 | local mask = self:getMask(batch, k) 166 | local inputX = torch.zeros(n_students, 2 * self.n_questions) 167 | local inputY = torch.zeros(n_students, self.n_questions) 168 | local truth = torch.zeros(n_students) 169 | 170 | for i,answers in ipairs(batch) do 171 | if(k + 1 <= answers['n_answers']) then 172 | local currentId = answers['questionId'][k] 173 | local nextId = answers['questionId'][k + 1] 174 | local currentCorrect = answers['correct'][k] 175 | local nextCorrect = answers['correct'][k + 1] 176 | 177 | local xIndex = self:getXIndex(currentCorrect, currentId) 178 | inputX[i][xIndex] = 1 179 | 180 | truth[i] = nextCorrect 181 | inputY[i][nextId] = 1 182 | end 183 | end 184 | --compressed sensing 185 | if(self.compressedSensing) then 186 | inputX = inputX * self.basis 187 | end 188 | return inputX, inputY, truth 189 | end 190 | 191 | function RNN:getXIndex(correct, id) 192 | assert(correct == 0 or correct == 1) 193 | assert(id ~= 0) 194 | local xIndex = correct * self.n_questions + id 195 | assert(xIndex >= 1) 196 | assert(xIndex ~= nil) 197 | assert(xIndex <= 2 * self.n_questions) 198 | return xIndex 199 | end 200 | 201 | function RNN:cloneManyTimes(n) 202 | return cloneManyTimes(self.layer, n) 203 | end 204 | 205 | function RNN:preventExplosion(grad) 206 | local norm = grad:norm() 207 | if norm > self.max_grad then 208 | print('explosion') 209 | local alpha = self.max_grad / norm 210 | grad:mul(alpha) 211 | end 212 | return norm 213 | end 214 | 215 | function RNN:err(batch) 216 | local n_steps = getNSteps(batch) 217 | for i = 1,n_steps do 218 | self.layers[i]:evaluate() 219 | end 220 | local sumErr, numTests, inputs = self:fprop(batch) 221 | for i = 1,n_steps do 222 | self.layers[i]:training() 223 | end 224 | return sumErr / numTests 225 | end 226 | 227 | function RNN:accuracy(batch) 228 | local n_steps = getNSteps(batch) 229 | local n_students = #batch 230 | 231 | self.layer:evaluate() 232 | 233 | local sumCorrect = 0 234 | local numTested = 0 235 | 236 | local state = self.start:forward(torch.zeros(n_students, 1)) 237 | for k = 1,n_steps do 238 | local inputX, inputY, truth = self:getInputs(batch, k) 239 | inputX = inputX 240 | inputY = inputY 241 | local inputs = {state, inputX, inputY, truth}; 242 | local output = self.layer:forward(inputs); 243 | state = output[3]:clone() 244 | 245 | local mask = self:getMask(batch, k) 246 | local p = output[1]:double() 247 | local pred = torch.gt(p, 0.5):double() 248 | local correct = torch.eq(pred, truth):double() 249 | local numCorrect = correct:cmul(mask):sum() 250 | sumCorrect = sumCorrect + numCorrect 251 | numTested = numTested + mask:sum() 252 | end 253 | 254 | -- for dropout 255 | for i = 1,n_steps do 256 | self.layer:training() 257 | end 258 | return sumCorrect, numTested 259 | end 260 | 261 | function RNN:getPredictionTruth(batch) 262 | local n_steps = getNSteps(batch) 263 | local n_students = #batch 264 | 265 | -- for dropout 266 | self.layer:evaluate() 267 | 268 | local predictionTruths = {} 269 | 270 | local state = self.start:forward(torch.zeros(n_students, 1)) 271 | for k = 1,n_steps do 272 | local inputX, inputY, truth = self:getInputs(batch, k) 273 | inputX = inputX 274 | inputY = inputY 275 | local inputs = {state, inputX, inputY, truth}; 276 | local output = self.layer:forward(inputs); 277 | state = output[3]:clone() 278 | 279 | local mask = self:getMask(batch, k) 280 | local pred = output[1]:double() 281 | 282 | for i = 1,n_students do 283 | if(mask[i] == 1) then 284 | predictionTruth = { 285 | pred = pred[i], 286 | truth = truth[i] 287 | } 288 | table.insert(predictionTruths, predictionTruth) 289 | end 290 | end 291 | end 292 | 293 | -- for dropout 294 | self.layer:training() 295 | return predictionTruths 296 | end -------------------------------------------------------------------------------- /scripts/trainAssist.lua: -------------------------------------------------------------------------------- 1 | require 'nn' 2 | require 'optim' 3 | require 'dataAssist' 4 | require 'nngraph' 5 | require 'rnn' 6 | require 'util' 7 | require 'utilExp' 8 | require 'lfs' 9 | 10 | START_EPOCH = 1 11 | OUTPUT_DIR = '../output/trainRNNAssist/' 12 | LEARNING_RATES = {30, 30, 30, 10, 10, 10, 5, 5, 5} 13 | LEARNING_RATE_REPEATS = 4 14 | MIN_LEARNING_RATE = 1 15 | 16 | function run() 17 | local fileId = arg[1] 18 | assert(fileId ~= nil) 19 | 20 | math.randomseed(os.time()) 21 | 22 | data = DataAssistMatrix() 23 | collectgarbage() 24 | 25 | local n_hidden = 200 26 | local decay_rate = 1 27 | local init_rate = 30 28 | local mini_batch_size = 100 29 | local dropoutPred = true 30 | local max_grad = 5e-5 31 | 32 | print('n_hidden' , n_hidden) 33 | print('init_rate', init_rate) 34 | print('decay_rate', decay_rate) 35 | print('mini_batch_size', mini_batch_size) 36 | print('dropoutPred', dropoutPred) 37 | print('maxGrad', max_grad) 38 | 39 | 40 | print('making rnn...') 41 | local rnn = RNN{ 42 | dropoutPred = dropoutPred, 43 | n_hidden = n_hidden, 44 | n_questions = data.n_questions, 45 | maxGrad = max_grad, 46 | maxSteps = 4290, 47 | compressedSensing = true, 48 | compressedDim = 100 49 | } 50 | print('rnn made!') 51 | 52 | lfs.mkdir('../output') 53 | lfs.mkdir(OUTPUT_DIR) 54 | lfs.mkdir(OUTPUT_DIR .. 'models/') 55 | local filePath = OUTPUT_DIR .. fileId .. '.txt' 56 | file = io.open(filePath, "w"); 57 | file:write('n_hidden,' .. n_hidden .. '\n') 58 | file:write('init_rate,' .. init_rate .. '\n') 59 | file:write('decay_rate,' .. decay_rate .. '\n') 60 | file:write('mini_batch_size,' .. mini_batch_size .. '\n') 61 | file:write('dropoutPred,' .. tostring(dropoutPred) .. '\n') 62 | file:write('maxGrad,' .. tostring(max_grad).. '\n') 63 | file:write('-----\n') 64 | file:write('i\taverageErr\tauc\ttestPred\trate\tclock\n') 65 | file:flush() 66 | trainMiniBatch(rnn, data, mini_batch_size, file, fileId) 67 | file:close() 68 | end 69 | 70 | function trainMiniBatch(rnn, data, mini_batch_size, file, modelId) 71 | print('train') 72 | local epochIndex = START_EPOCH 73 | local blob_size = 50 74 | --getAccuracy(rnn, data, mini_batch_size) 75 | while(true) do 76 | local rate = getLearningRate(epochIndex) 77 | local startTime = os.time() 78 | local miniBatches = semiSortedMiniBatches(data:getTrainData(), blob_size, true) 79 | local totalTests = getTotalTests(miniBatches) 80 | collectgarbage() 81 | local sumErr = 0 82 | local numTests = 0 83 | local done = 0 84 | rnn:zeroGrad(350) 85 | local miniTests = 0 86 | local miniErr = 0 87 | for i,batch in ipairs(miniBatches) do 88 | local alpha = blob_size / totalTests 89 | --local alpha = blob_size / getNTests(batch) 90 | local err, tests, maxNorm = rnn:calcGrad(batch, rate, alpha) 91 | sumErr = sumErr + err 92 | numTests = numTests + tests 93 | collectgarbage() 94 | done = done + blob_size 95 | miniErr = miniErr + err 96 | miniTests = miniTests + tests 97 | if done % mini_batch_size == 0 then 98 | rnn:update(350, rate) 99 | rnn:zeroGrad(350) 100 | print('trainMini', i/#miniBatches, miniErr/miniTests, sumErr/numTests, rate) 101 | miniErr = 0 102 | miniTests = 0 103 | --rate = rate * decay_rate 104 | end 105 | end 106 | local auc, accuracy = evaluate(rnn, data) 107 | local avgErr = sumErr / numTests 108 | local outline = epochIndex .. '\t' 109 | outline = outline .. avgErr .. '\t' 110 | outline = outline .. auc .. '\t' 111 | outline = outline .. accuracy .. '\t' 112 | outline = outline .. rate .. '\t' 113 | outline = outline .. os.clock() 114 | file:write(outline .. '\n') 115 | file:flush() 116 | print(outline); 117 | rnn:save(OUTPUT_DIR .. 'models/' .. modelId .. '_' .. epochIndex) 118 | epochIndex = epochIndex + 1 119 | end 120 | end 121 | 122 | function getLearningRate(epochIndex) 123 | local rate = MIN_LEARNING_RATE 124 | local rateIndex = math.floor((epochIndex - 1) / LEARNING_RATE_REPEATS) + 1 125 | if(rateIndex <= #LEARNING_RATES) then 126 | rate = LEARNING_RATES[rateIndex] 127 | end 128 | return rate 129 | end 130 | 131 | 132 | run() 133 | -------------------------------------------------------------------------------- /scripts/trainSynthetic.lua: -------------------------------------------------------------------------------- 1 | require 'dataSynthetic' 2 | require 'rnn' 3 | require 'lfs' 4 | 5 | seconds_per_hour = 3600 6 | 7 | outputRoot = "../output/synthetic/" 8 | CONCEPT_NUM = 5 9 | VERSION = 1 10 | 11 | function run() 12 | math.randomseed(os.time()) 13 | 14 | local n_hidden = 400 15 | local decay_rate = 1.0 16 | local init_rate = 0.5 17 | local mini_batch_size = 50 18 | local blob_size = mini_batch_size 19 | 20 | local data = Data{n=4000,q=50,c=CONCEPT_NUM,v=VERSION} 21 | 22 | local rnn = RNN{ 23 | dropout = true, 24 | n_hidden = n_hidden, 25 | n_questions = data.n_questions, 26 | max_grad = 100, 27 | max_steps = data.n_questions, 28 | --modelDir = outputRoot .. '/models/result_c5_v0_98' 29 | } 30 | 31 | local name = "result_c" .. CONCEPT_NUM .. "_v" .. VERSION 32 | lfs.mkdir(outputRoot) 33 | lfs.mkdir(outputRoot .. "models") 34 | 35 | file = io.open(outputRoot .. name .. ".txt", "w"); 36 | print(dropout) 37 | file:write('version,', VERSION, '\n') 38 | file:write('n_hidden,', n_hidden, '\n'); 39 | file:write('init_rate,' , init_rate, '\n'); 40 | file:write('decay_rate,', decay_rate, '\n'); 41 | file:write('dropout,', tostring(dropout), '\n'); 42 | file:write('mini_batch_size,', mini_batch_size, '\n'); 43 | trainMiniBatch(rnn, data, init_rate, decay_rate, mini_batch_size, blob_size, file, name) 44 | file:close() 45 | 46 | end 47 | 48 | function trainMiniBatch(rnn, data, init_rate, decay_rate, mini_batch_size, blob_size, file, modelId) 49 | print('train') 50 | local rate = init_rate 51 | local epochs = 1 52 | --getAccuracy(rnn, data, mini_batch_size) 53 | while(true) do 54 | local startTime = os.time() 55 | local miniBatches = semiSortedMiniBatches(data:getTrainData(), blob_size, true) 56 | local totalTests = getTotalTests(miniBatches) 57 | collectgarbage() 58 | local sumErr = 0 59 | local numTests = 0 60 | local done = 0 61 | rnn:zeroGrad() 62 | local miniTests = 0 63 | local miniErr = 0 64 | for i,batch in ipairs(miniBatches) do 65 | local alpha = blob_size / totalTests 66 | --local alpha = blob_size / getNTests(batch) 67 | local err, tests, maxNorm = rnn:calcGrad(batch, rate, alpha) 68 | sumErr = sumErr + err 69 | numTests = numTests + tests 70 | collectgarbage() 71 | done = done + blob_size 72 | miniErr = miniErr + err 73 | miniTests = miniTests + tests 74 | if done % mini_batch_size == 0 then 75 | rnn:update(rate) 76 | rnn:zeroGrad() 77 | print(i/#miniBatches, sumErr/numTests) 78 | miniErr = 0 79 | miniTests = 0 80 | --rate = rate * decay_rate 81 | end 82 | end 83 | local avgErr = sumErr / numTests 84 | local testPred = getAccuracy(rnn, data, mini_batch_size); 85 | file:write(epochs .. '\t' .. avgErr .. '\t' .. testPred .. '\t' .. rate .. '\t' .. os.clock() .. '\n') 86 | file:flush() 87 | print(epochs, avgErr, testPred, rate, os.time() - startTime); 88 | rate = rate * decay_rate 89 | rnn:save(outputRoot .. 'models/' .. modelId .. '_' .. epochs) 90 | epochs = epochs + 1 91 | end 92 | end 93 | 94 | function getAccuracy(rnn, data) 95 | local nTest = math.floor(data.nTest / 50) 96 | local miniBatches = semiSortedMiniBatches(data:getTestData(), nTest, false) 97 | local sumCorrect = 0 98 | local sumTested = 0 99 | for i,batch in ipairs(miniBatches) do 100 | local correct, tested, avgAcc = rnn:accuracy(batch) 101 | sumCorrect = sumCorrect + correct 102 | sumTested = sumTested + tested 103 | print('testMini', i/#miniBatches, sumCorrect/sumTested) 104 | collectgarbage() 105 | end 106 | return sumCorrect/sumTested 107 | end 108 | 109 | function semiSortedMiniBatches(dataset, mini_batch_size, trimToBatchSize) 110 | 111 | -- round down so that minibatches are the same size 112 | local trimmedAns = {} 113 | if(trimToBatchSize) then 114 | local nTemp = #dataset 115 | local maxNum = nTemp - (nTemp % mini_batch_size) 116 | local shuffled = shuffle(getKeyset(dataset)) 117 | for i,s in ipairs(shuffled) do 118 | if(i <= maxNum) then 119 | table.insert(trimmedAns, dataset[s]) 120 | end 121 | end 122 | else 123 | trimmedAns = dataset; 124 | end 125 | 126 | -- sort answers 127 | function compare(a,b) 128 | return a['n_answers'] < b['n_answers'] 129 | end 130 | table.sort(trimmedAns, compare) 131 | 132 | -- make minibatches 133 | local miniBatches = {} 134 | for j=1,#trimmedAns,mini_batch_size do 135 | miniBatch = {} 136 | for k = j, j + mini_batch_size - 1 do 137 | table.insert(miniBatch, trimmedAns[k]) 138 | end 139 | table.insert(miniBatches, miniBatch) 140 | end 141 | 142 | -- shuffle minibatches 143 | local shuffledBatches = {} 144 | for i, s in ipairs(shuffle(getKeyset(miniBatches))) do 145 | table.insert(shuffledBatches, miniBatches[s]) 146 | end 147 | 148 | return shuffledBatches 149 | end 150 | run() -------------------------------------------------------------------------------- /scripts/util.lua: -------------------------------------------------------------------------------- 1 | function selfDot(m) 2 | return torch.dot(m, m) 3 | end 4 | 5 | function tableToVec(gradList) 6 | local vec = gradList[1] 7 | for i = 2,#gradList do 8 | vec = torch.cat(vec, gradList[i], 1) 9 | end 10 | return vec; 11 | end 12 | 13 | function saveMatrixAsCsv(path, matrix) 14 | local file = io.open(path, "w"); 15 | for r = 1, matrix:size(1) do 16 | for c = 1, matrix:size(2) do 17 | file:write(matrix[r][c]) 18 | if(c ~= matrix:size(2)) then 19 | file:write(',') 20 | end 21 | end 22 | file:write('\n') 23 | end 24 | file:close() 25 | end 26 | 27 | function split(str, sep) 28 | local sep, fields = sep or ":", {} 29 | local pattern = string.format("([^%s]+)", sep) 30 | str:gsub(pattern, function(c) fields[#fields+1] = c end) 31 | return fields 32 | end 33 | 34 | function saveMatrixAsGephi(path, matrix) 35 | local file = io.open(path, "w"); 36 | -- write first line 37 | for c = 1, matrix:size(2) do 38 | file:write(';' .. c) 39 | end 40 | file:write('\n') 41 | 42 | for r = 1, matrix:size(1) do 43 | file:write(r) 44 | for c = 1, matrix:size(2) do 45 | file:write(';') 46 | file:write(matrix[r][c]) 47 | end 48 | file:write('\n') 49 | end 50 | file:close() 51 | end 52 | 53 | function copyWithoutRow(m, index) 54 | local nRows = m:size(1) 55 | if(index == 1) then 56 | return m:narrow(1, 2, nRows - 1):clone() 57 | end 58 | if(index == nRows) then 59 | return m:narrow(1, 1, nRows - 1):clone() 60 | end 61 | local first = m:narrow(1,1,index -1) 62 | local second = m:narrow(1, index + 1, nRows - index) 63 | return torch.cat(first, second, 1) 64 | end 65 | 66 | function getKeyset(map) 67 | local keyset={} 68 | local n=1 69 | for k,v in pairs(map) do 70 | keyset[n]=k 71 | n=n+1 72 | end 73 | return keyset 74 | end 75 | 76 | function maskRows(m, y) 77 | local nonZero = {} 78 | for i = 1,y:size(1) do 79 | print(y[i]) 80 | if y[i] ~= 0 then 81 | table.insert(nonZero, i) 82 | end 83 | end 84 | return m:index(1,torch.LongTensor(nonZero)) 85 | end 86 | 87 | 88 | function shuffle(a) 89 | local rnd,trem,getn,ins = math.random,table.remove,table.getn,table.insert; 90 | local r = {}; 91 | while getn(a) > 0 do 92 | ins(r, trem(a, rnd(getn(a)))); 93 | end 94 | return r; 95 | end 96 | 97 | function getParamLinScale(min, max) 98 | local x = math.random(); 99 | return min + x * (max - min) 100 | end 101 | 102 | function getParamLogScale(min, max) 103 | local x = math.random(); 104 | return min * math.exp(math.log(max/min) * x); 105 | end 106 | 107 | function cloneOnce(net) 108 | local params, gradParams = net:parameters() 109 | if params == nil then 110 | params = {} 111 | end 112 | local paramsNoGrad 113 | if net.parametersNoGrad then 114 | paramsNoGrad = net:parametersNoGrad() 115 | end 116 | local mem = torch.MemoryFile("w"):binary() 117 | mem:writeObject(net) 118 | -- We need to use a new reader for each clone. 119 | -- We don't want to use the pointers to already read objects. 120 | local reader = torch.MemoryFile(mem:storage(), "r"):binary() 121 | local clone = reader:readObject() 122 | reader:close() 123 | local cloneParams, cloneGradParams = clone:parameters() 124 | local cloneParamsNoGrad 125 | for i = 1, #params do 126 | cloneParams[i]:set(params[i]) 127 | cloneGradParams[i]:set(gradParams[i]) 128 | end 129 | if paramsNoGrad then 130 | cloneParamsNoGrad = clone:parametersNoGrad() 131 | for i =1,#paramsNoGrad do 132 | cloneParamsNoGrad[i]:set(paramsNoGrad[i]) 133 | end 134 | end 135 | mem:close() 136 | return clone 137 | end 138 | 139 | --[[ Creates clones of the given network. 140 | The clones share all weights and gradWeights with the original network. 141 | Accumulating of the gradients sums the gradients properly. 142 | The clone also allows parameters for which gradients are never computed 143 | to be shared. Such parameters must be returns by the parametersNoGrad 144 | method, which can be null. 145 | --]] 146 | function cloneManyTimes(net, T) 147 | print(collectgarbage("count")) 148 | local clones = {} 149 | local params, gradParams = net:parameters() 150 | if params == nil then 151 | params = {} 152 | end 153 | local paramsNoGrad 154 | if net.parametersNoGrad then 155 | paramsNoGrad = net:parametersNoGrad() 156 | end 157 | local mem = torch.MemoryFile("w"):binary() 158 | mem:writeObject(net) 159 | for t = 1, T do 160 | -- We need to use a new reader for each clone. 161 | -- We don't want to use the pointers to already read objects. 162 | local reader = torch.MemoryFile(mem:storage(), "r"):binary() 163 | local clone = reader:readObject() 164 | reader:close() 165 | local cloneParams, cloneGradParams = clone:parameters() 166 | local cloneParamsNoGrad 167 | for i = 1, #params do 168 | cloneParams[i]:set(params[i]) 169 | cloneGradParams[i]:set(gradParams[i]) 170 | end 171 | if paramsNoGrad then 172 | cloneParamsNoGrad = clone:parametersNoGrad() 173 | for i =1,#paramsNoGrad do 174 | cloneParamsNoGrad[i]:set(paramsNoGrad[i]) 175 | end 176 | end 177 | clones[t] = clone 178 | print('clone', t) 179 | -- print(collectgarbage("count")) 180 | end 181 | mem:close() 182 | collectgarbage() 183 | return clones 184 | end 185 | 186 | 187 | function run() 188 | a = torch.randn(5,5) 189 | y = torch.zeros(5) 190 | y[2] = 1 191 | y[3] = 1 192 | m = maskRows(a, y) 193 | print(a) 194 | print(m) 195 | end 196 | -------------------------------------------------------------------------------- /scripts/utilExp.lua: -------------------------------------------------------------------------------- 1 | function getNTests(batch) 2 | local n_steps = getNSteps(batch) 3 | local n_students = #batch 4 | local m = torch.zeros(n_students, n_steps) 5 | for i = 1,n_steps do 6 | local mask = getMask(batch, i) 7 | for j = 1, n_students do 8 | m[j][i] = mask[j] 9 | end 10 | end 11 | return m:sum() 12 | end 13 | 14 | function getTotalTests(batches, maxSteps) 15 | local total = 0 16 | for i,batch in ipairs(batches) do 17 | local nTests = getNTests(batch) 18 | if(maxSteps ~= nil and maxSteps > 0) then 19 | nTests = math.min(maxSteps, nTests) 20 | end 21 | total = total + nTests 22 | end 23 | return total 24 | end 25 | 26 | function getNSteps(batch) 27 | local maxSteps = 0 28 | for i,ans in ipairs(batch) do 29 | if(ans['n_answers'] > maxSteps) then 30 | maxSteps = ans['n_answers'] 31 | end 32 | end 33 | return maxSteps - 1 34 | end 35 | 36 | function getMask(batch, k) 37 | local mask = torch.zeros(#batch) 38 | for i,ans in ipairs(batch) do 39 | if(k + 1 <= ans['n_answers']) then 40 | mask[i] = 1 41 | end 42 | end 43 | return mask 44 | end 45 | 46 | function evaluate(rnn, data) 47 | local miniBatches = semiSortedMiniBatches(data:getTestData(), 100, false) 48 | local allPredictions = {} 49 | local totalPositives = 0 50 | local totalNegatives = 0 51 | 52 | for i, batch in ipairs(miniBatches) do 53 | local batch = miniBatches[i] 54 | local pps = rnn:getPredictionTruth(batch) 55 | for i,prediction in ipairs(pps) do 56 | if(prediction['truth'] == 1) then 57 | totalPositives = totalPositives + 1 58 | else 59 | totalNegatives = totalNegatives + 1 60 | end 61 | 62 | table.insert(allPredictions, prediction) 63 | end 64 | collectgarbage() 65 | end 66 | 67 | function compare(a,b) 68 | return a['pred'] > b['pred'] 69 | end 70 | table.sort(allPredictions, compare) 71 | 72 | local truePositives = 0 73 | local falsePositives = 0 74 | local correct = 0 75 | local auc = 0 76 | local lastFpr = nil 77 | local lastTpr = nil 78 | for i,p in ipairs(allPredictions) do 79 | if(p['truth'] == 1) then 80 | truePositives = truePositives + 1 81 | else 82 | falsePositives = falsePositives + 1 83 | end 84 | 85 | local guess = 0 86 | if(p['pred'] > 0.5) then guess = 1 end 87 | if(guess == p['truth']) then correct = correct + 1 end 88 | 89 | local fpr = falsePositives / totalNegatives 90 | local tpr = truePositives / totalPositives 91 | if(i % 500 == 0) then 92 | if lastFpr ~= nil then 93 | local trapezoid = (tpr + lastTpr) * (fpr - lastFpr) *.5 94 | auc = auc + trapezoid 95 | end 96 | lastFpr = fpr 97 | lastTpr = tpr 98 | end 99 | if(recall == 1) then break end 100 | end 101 | 102 | local accuracy = correct / #allPredictions 103 | return auc, accuracy 104 | end 105 | 106 | function semiSortedMiniBatches(dataset, mini_batch_size, trimToBatchSize) 107 | 108 | -- round down so that minibatches are the same size 109 | local trimmedAns = {} 110 | if(trimToBatchSize) then 111 | local nTemp = #dataset 112 | local maxNum = nTemp - (nTemp % mini_batch_size) 113 | local shuffled = shuffle(getKeyset(dataset)) 114 | for i,s in ipairs(shuffled) do 115 | if(i <= maxNum) then 116 | table.insert(trimmedAns, dataset[s]) 117 | end 118 | end 119 | else 120 | trimmedAns = dataset; 121 | end 122 | 123 | -- sort answers 124 | function compare(a,b) 125 | return a['n_answers'] < b['n_answers'] 126 | end 127 | table.sort(trimmedAns, compare) 128 | 129 | -- make minibatches 130 | local miniBatches = {} 131 | for j=1,#trimmedAns,mini_batch_size do 132 | miniBatch = {} 133 | for k = j, j + mini_batch_size - 1 do 134 | table.insert(miniBatch, trimmedAns[k]) 135 | end 136 | table.insert(miniBatches, miniBatch) 137 | end 138 | 139 | -- shuffle minibatches 140 | local shuffledBatches = {} 141 | for i, s in ipairs(shuffle(getKeyset(miniBatches))) do 142 | table.insert(shuffledBatches, miniBatches[s]) 143 | end 144 | 145 | return shuffledBatches 146 | end --------------------------------------------------------------------------------