notebook.community

Edit and run



In [1]:

    
%matplotlib inline
import alpenglow as ag
import alpenglow.Getter as rs
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import alpenglow.experiments
import alpenglow.evaluation



In [2]:

    
data = pd.read_csv(
    "python/test_alpenglow/test_data_4",
    sep=' ',
    header=None,
    names=['time', 'user', 'item', 'id', 'score', 'eval']
)



In [3]:

    
exp = ag.OfflineModel()



In [4]:

    
exp.fit(data)



In [5]:

    
def predict(model, user, item):
    rd = rs.RecDat()
    rd.user = user
    rd.item = item
    return model.prediction(rd)



In [6]:

    
errors = [(1-predict(exp.model, u, i))**2 for (u,i) in data[['user','item']].values]



In [7]:

    
np.sqrt(pd.Series(errors)).mean()









    Out[7]:





0.29222296412604876



In [8]:

    
exp.predict(data[['user','item']])









    Out[8]:





[0.9668979386476292,
 0.9417665718844833,
 0.9933654125393688,
 0.9848558523804352,
 0.9817566774001023,
 1.1661315139038164,
 0.9887016690548583,
 1.005446495808034,
 0.998162584108844,
 1.025667531262195,
 0.9546953942929735,
 1.0153770824302297,
 1.0012088354291004,
 0.987442505729575,
 1.07442279064002,
 1.1967690719512991,
 0.9289185214383435,
 1.3144657578380183,
 0.5677282889661542,
 0.9858796370953651,
 0.9767453168603903,
 0.9913658974393601,
 0.9850473952762439,
 1.0010229778103816,
 0.9920124806074466,
 0.9768897177062688,
 1.0428739365006705,
 0.8406116830662264,
 1.0067363938924705,
 0.9947762899930859,
 1.0001232932472992,
 0.9626645742068772,
 1.0015976130870494,
 1.0047549500765862,
 1.0149210348663684,
 1.0528142628370105,
 1.022678696255703,
 1.0668660082880184,
 1.10756681460296,
 1.129991901577677,
 1.2217188047387706,
 1.0173990626522456,
 1.0243126505445133,
 1.0110498874509384,
 1.0047807377818927,
 1.02934874723683,
 1.2051516011534065,
 0.9443268639004005,
 1.0139688943313452,
 0.9297978533393105,
 1.0498196820074353,
 1.1910601628477808,
 1.181128319577348,
 0.7955167025166885,
 1.1545734879583494,
 1.0086604468940812,
 0.8517209189211262,
 0.7979393919986282,
 1.0011176997050715,
 1.329608149510958,
 0.968576363700838,
 0.9934268200806663,
 0.14090692194456075,
 0.7334287086214033,
 0.8619069651774763,
 0.9767692732871204,
 0.9004018581984069,
 1.0301658486251144,
 1.1245026751704468,
 0.8141504862697765,
 1.1017539003431724,
 0.994961062700447,
 0.8808643128248355,
 1.1818768812213971,
 0.994557568980589,
 1.0619189495393324,
 0.9980363458198626,
 1.2325243811657574,
 1.0111379699037921,
 1.0309919311966673,
 1.2718978980750668,
 0.8878880672511008,
 1.0048894091966631,
 1.0041262439796446,
 1.1619388749282926,
 1.1388144904001634,
 1.0280044293055293,
 0.5180403578949422,
 0.8362032376744525,
 0.9940995023107049,
 1.1452793927955778,
 0.9036028917158961,
 0.9954870664652663,
 0.7156852461505194,
 0.9851867679577496,
 0.9903172218342943,
 0.5599027722236564,
 1.000705113555397,
 1.0167000791993166,
 0.7841111485304516,
 0.999811564461526,
 0.6842669924875352,
 0.9865220294756671,
 0.9735977201908889,
 0.7632992550147092,
 0.7700464490829491,
 0.9278910011268477,
 1.0306402511140556,
 0.7465874989684936,
 0.9378267411611338,
 1.0248304424436767,
 0.9956995526470441,
 0.6252788203719136,
 1.2672180196900684,
 0.624218989639406,
 0.7424666716542577,
 1.222001809919349,
 1.0160094538287283,
 0.968065603300367,
 0.8707511490880583,
 1.0244052436780746,
 0.995790874558099,
 1.0102734707461383,
 1.0169668036617452,
 0.8439260989182114,
 0.9942704587667761,
 0.47962030015032947,
 1.0222815121757072,
 0.7420977719197046,
 0.8418851004983643,
 1.0239260783245645,
 0.4571631664726956,
 0.6770593717741222,
 1.0190091665140577,
 0.9147717861416718,
 1.0011477126603288,
 0.7972713460260588,
 0.9909278352004742,
 1.063188624134487,
 0.9305335539209336,
 1.0116052091203376,
 0.4081221011208849,
 0.8835413115094495,
 0.8834866964570112,
 0.6118514756455755,
 0.7617259525226137,
 1.034974367687762,
 0.925706098102179,
 0.9001604995933967,
 0.8978729511728369,
 0.7838230157433904,
 0.8107751509168869,
 0.9936127045303875,
 0.962937191165287,
 1.0253006353213994,
 0.6278499493635025,
 0.5003931011808083,
 0.6410245084651937,
 0.7655244282965342,
 0.5678103172123311,
 0.8804212887782576,
 0.8627638014209753,
 1.0163368297990967,
 0.6291313295542504,
 1.1643465534415445,
 1.1046420640220203,
 1.0041167754276872,
 0.9769881520514165,
 0.9914542825087487,
 0.996293269628101,
 0.8206845200913784,
 0.8391164569570715,
 0.9927153953201826,
 1.003173700606979,
 0.6274241370540626,
 1.0592165527118902,
 1.029301445526826,
 0.9561512855822396,
 0.636643840690182,
 0.9874319077236287,
 0.5044426130192147,
 0.5478953408335214,
 0.9925756649035233,
 1.001151209572267,
 1.2508108815299956,
 1.2431623585283396,
 0.994814500968998,
 0.9927050563410778,
 0.40437569521245686,
 0.9895876397613277,
 0.5115045388655762,
 0.9912096244812107,
 0.6114679739775993,
 0.7824709434984155,
 0.4128006890372931,
 1.175165435124455,
 0.996430429437069,
 0.5950534490377226,
 0.7252825738779571,
 1.1075800778494798,
 0.48422171462360947,
 0.7635850308997817,
 1.4160963496209902,
 0.7722113224330106,
 1.0083971180794848,
 0.4812297946182207,
 0.9799013230956237,
 0.9849812702814409,
 0.9905988782750331,
 1.0115873004307492,
 0.9928126367136921,
 0.8186330311562665,
 0.9955559903397772,
 0.8190671687140811,
 0.7824578314250202,
 0.941200577187872,
 0.9849548089847826,
 0.9823289878114968,
 0.9333114873247745,
 0.9985245096476565,
 0.7560142967517309,
 1.3207889724573092,
 0.9259810822815213,
 0.8101312322292284,
 0.9944565410047387,
 0.9929405391965888,
 0.9926862090970994,
 0.9747417795412441,
 0.8223013785805008,
 0.9151342915285405,
 0.7026163539278283,
 0.38162652025074034,
 0.6554166152544033,
 0.9929143624366644,
 0.893430577159698,
 0.4445825176098178,
 0.9363549305768065,
 0.3588359525530993,
 0.7726833263671435,
 1.0340008321064866,
 0.4922443236936404,
 1.0081566515099945,
 0.9929629595199511,
 0.9794287670249792,
 0.20590757591045314,
 0.9915148956780787,
 0.7815988084251069,
 0.9885576328761778,
 0.9951019579276861,
 0.5401250138278265,
 0.5764305209314831,
 0.5813860283604577,
 0.9295921901429682,
 1.0409572984980193,
 0.995549379237315,
 0.6152924597282377,
 1.25269868834454,
 0.6544496832701182,
 1.104025596710712,
 1.0121866327205173,
 0.5741845057523199,
 0.7623236830401847,
 0.4929869916161028,
 0.939403304820931,
 0.9869253801217361,
 0.5490423057295514,
 0.9999989857361814,
 0.588689132566965,
 0.7582978754890565,
 0.4736736855419251,
 1.139883017557731,
 0.8264074566492269,
 0.9943655036136014,
 0.9909579705145205,
 0.4611523902507778,
 0.7808970711757292,
 1.0085735329254972,
 0.9969741848306202,
 0.5567208753346444,
 0.5779148820006459,
 1.2782575520464474,
 0.7027862308867202,
 1.014679172806849,
 0.5393112050141056,
 0.7338878448343301,
 0.8158896975771106,
 0.9937534869180273,
 0.7005873673439025,
 0.8505634744863275,
 0.9845616681400515,
 0.9559932316374212,
 0.6964093271935954,
 0.2372265172956455,
 0.7674776460763553,
 1.25668863956612,
 0.9671462679483644,
 1.0085095769076415,
 0.664429056955493,
 1.0062005206709939,
 1.0214652579828292,
 0.4636979713670938,
 0.24841585656080375,
 1.0210437154624323,
 0.4157586266301777,
 0.980413327532856,
 0.6579363092411338,
 1.0230337788916217,
 1.0822767515344711,
 1.0028812807073053,
 0.9961617710166245,
 0.7229151210354425,
 0.9569372543861427,
 0.48403165519943236,
 0.5998118770177243,
 1.0196284862729341,
 0.692261498069244,
 1.018022150524315,
 1.0339697633139293,
 0.5538839747249926,
 0.9911465753799502,
 0.9692400237678122,
 0.635105302760046,
 0.701300432432054,
 0.9928143449207437,
 0.7743340400178346,
 0.9919619648144967,
 0.882458168130733,
 1.0021880895399224,
 0.8269643536864187,
 0.9955085087308573,
 0.8868461567260743,
 1.0462581475007873,
 0.5827822112677723,
 0.4845029342699123,
 0.7474558538666597,
 0.9901235346924122,
 1.0273257140917995,
 0.8078950164254951,
 0.5143498343847698,
 0.6503671339843822,
 0.6951650604986064,
 1.0176279151250038,
 0.9924602462323483,
 0.6081174783080103,
 0.6794615749039463,
 0.11910665761694259,
 0.7600311272589212,
 0.9174949763186511,
 1.020394414923931,
 0.13731154108952198,
 0.5594314669969243,
 0.993168513588002,
 0.4933070175554733,
 0.9962748915267224,
 0.9648319731459865,
 0.9957885015299559,
 1.0377209184545453,
 0.44233001139913336,
 0.7088959153569021,
 1.2035755515958824,
 0.4296258817042737,
 0.49084423310045094,
 0.11922869190610427,
 0.9894126240591179,
 1.0105696119992083,
 0.29868347977151544,
 0.620710937978606,
 0.9930347933596273,
 0.9796614764831939,
 0.7774044019014547,
 0.6063399857677886,
 0.43271107595881875,
 0.45181409208533674,
 0.40708587086827097,
 0.32197846249806494,
 0.9832924764244655,
 1.0884388977045336,
 0.9997107493627494,
 0.8795354832913714,
 0.6834693562125742,
 0.6201050357429738,
 0.7480742920646166,
 1.0823022689132713,
 0.73578219451689,
 0.8465894780651082,
 0.3648271279540217,
 0.6322249721058765,
 0.9979053966131358,
 0.3056572585802792,
 0.5278248855269563,
 0.9919927420585241,
 1.0138916970614316,
 0.9926286489832782,
 0.9153613058460373,
 0.8251883656559553,
 0.5504572740361453,
 1.0660756064304189,
 0.8611555864289576,
 0.49024009575762806,
 0.6859583966558175,
 0.6340441327702635,
 0.9467487671576902,
 0.8432682628420034,
 0.9952891332177403,
 0.6261048852027712,
 0.7065584969876265,
 0.5832988160683612,
 0.15212086417940127,
 0.9692368505275881,
 0.37101110408397875,
 0.7170290178067726,
 0.9960919286622106,
 0.9935595632634991,
 0.8139568228184566,
 0.45211848419494655,
 0.9942497292075129,
 0.6406067568405251,
 0.9910246729771206,
 0.9949656095703345,
 0.10882972254422812,
 0.7138707925290203,
 0.994144563802043,
 0.7902590983736617,
 0.7719277406824916,
 0.8192862629345518,
 0.7969172745181972,
 1.2287730254791536,
 0.9949404668468763,
 0.5027461346053405,
 0.8917015001697172,
 0.8546544162516576,
 0.23838624439896688,
 0.5903395608159484,
 1.0316335803275314,
 0.998547399554429,
 0.47663154930183077,
 0.8756610000697115,
 0.784736584272907,
 0.5812756601972621,
 0.6452224890452201,
 0.7664571334650818,
 1.0015507970415165,
 0.9930264654288679,
 1.0001483147473413,
 1.22536131132786,
 0.3390055264015336,
 0.131948501375094,
 0.9153540145979084,
 1.302192317352297,
 0.7536706574417384,
 0.8084624257617279,
 0.46244213157911085,
 0.9697080130838464,
 1.4802616752465023,
 0.5694930922586036,
 0.3503085583703781,
 0.8223208352621483,
 0.23444715903768232,
 0.4372787611802593,
 0.3742397896215375,
 0.8732951637969902,
 0.2491070383576216,
 0.6880224680543431,
 0.9570738822677248,
 0.9921276818487579,
 0.7104094863925342,
 0.7939499029972068,
 0.36236554844947055,
 0.9970194411857779,
 0.48395638542539027,
 0.6496552483160901,
 0.5226841478064309,
 0.23875898653759436,
 0.909593035227269,
 0.2622841171522623,
 0.7903291728400352,
 1.0734695867243749,
 0.9913536830576402,
 0.9114152479246257,
 1.0060722231156478,
 0.6983287133699405,
 0.9933902527771573,
 0.6222120695143282,
 0.42048190513857864,
 0.5763672435514863,
 0.48536711816682787,
 0.9915675075900311,
 0.9941328347444273,
 0.9949919540052701,
 0.8860424510431131,
 0.4787185268843402,
 0.3226742329357719,
 1.0282245177756253,
 0.5589548668257036,
 0.4137993044111544,
 1.051278144603223,
 0.9945349176371012,
 0.8927347796919158,
 0.5584510663025547,
 0.3229354468606759,
 0.5659032049503229,
 0.6291074511646948,
 0.9941303682287431,
 1.0974690242939684,
 0.2787324446222392,
 1.008201548738091,
 0.5626228239714046,
 0.6769126760760327,
 0.6886542842024763,
 0.2557619904163428,
 1.0230336323143054,
 0.5833205447718715,
 0.3646978687229872,
 0.2238776116822787,
 0.9961331409944685,
 0.9397316985390511,
 0.23614881488454767,
 0.7766652921374498,
 0.3228319622220781,
 0.5743230613463323,
 0.7275363374941132,
 0.3971037771180726,
 0.09633563462586735,
 0.5197578683942442,
 0.6227994177370587,
 0.6869353616747491,
 0.9940838535593288,
 0.8544133845013484,
 1.0387736368749354,
 0.5669377474322583,
 0.6306400382533658,
 0.3614499453485479,
 0.5355062466658618,
 0.9901575126373081,
 0.8131730007678086,
 0.39257918112591556,
 0.700674869934137,
 0.583630652459832,
 0.9914570176061263,
 0.5413979167635314,
 0.9920517289425983,
 0.9932963251553221,
 0.9598081730861556,
 0.6935978464483968,
 0.6119396328919758,
 0.5859174509071852,
 0.7365505397316126,
 0.5145425894458513,
 0.3146579089954367,
 0.6791630291533487,
 0.2851986953478226,
 0.624607520452698,
 0.32466512858825897,
 0.7759452120566667,
 1.0221240380012988,
 0.40716835540486296,
 0.5188121813451578,
 0.5327827482742261,
 0.7170249666014702,
 0.7013633260953267,
 0.5214609388751896,
 0.6311205522589861,
 1.0039634978100596,
 0.5881755114418861,
 0.5874631243067694,
 0.7554101997100772,
 0.6004774379371789,
 0.6073717176104506,
 0.5496085148429259,
 0.9935557164943025,
 0.38618721139820467,
 0.9938373834968042,
 0.4495884125951758,
 0.5003119451560089,
 0.5476322954700664,
 0.9963710052503392,
 0.5866154183381893,
 0.7104100678109505,
 0.3659794864314444,
 0.9076543451845256,
 0.9944329542903781,
 0.3912871864967312,
 0.9914196871376538,
 0.5444291984165189,
 0.81900898120909,
 1.0180777468531028,
 0.4466800220100937,
 0.45522676955358854,
 0.3431045369609377,
 0.28852904460029954,
 0.9950384931760504,
 0.5999402624686274,
 0.41278461703302827,
 0.27814397726802115,
 0.5307611666930843,
 0.9952297599683464,
 0.23361964592078688,
 0.5002563346484132,
 0.9938641498677602,
 0.7467249221704548,
 0.6212259452446474,
 1.0179000078642744,
 0.6708871631064665,
 0.48590528485761864,
 0.7540089245896937,
 0.20143537104144552,
 0.6228132111516869,
 0.7382592020912494,
 0.4611083319383724,
 0.6218270273687649,
 0.9923903657072033,
 0.39105685930561357,
 0.14400570468097992,
 0.9794314661143138,
 0.5465001301510551,
 0.41342138040126597,
 0.537849982364847,
 0.9908966523263832,
 0.5942633416737061,
 0.49842642050635055,
 0.4631382767421957,
 0.8386639598532091,
 0.9943888506654087,
 0.12874063659574217,
 0.8728294534326121,
 0.9707289078644122,
 0.9375384749679707,
 0.9951671817742004,
 0.9899358560718833,
 0.5651855517539044,
 0.5165302582522246,
 0.4459068358086142,
 0.5616165728710999,
 0.5960007403789773,
 0.40710379029791666,
 0.33076286864393006,
 0.658800451021776,
 0.7138652206969645,
 0.8420124705328695,
 0.23817639576130376,
 0.7513779024110705,
 0.4304636478977268,
 0.48595975507306177,
 0.6251821544276857,
 0.03223766003916849,
 0.99369777808227,
 0.2068778072431196,
 0.9928039980604896,
 1.3334923279789723,
 0.7856321343539796,
 0.5959157178162509,
 0.9912437797271128,
 0.3447921621873315,
 0.29801944693475824,
 0.4619139378868955,
 1.1150540215400893,
 0.16355759700462583,
 0.9922930557203172,
 0.5689291945306862,
 0.7164827127123491,
 0.9942196292685397,
 1.1364148319118383,
 0.47813875217775725,
 0.2995150100199036,
 0.277690524200533,
 0.3626859225239481,
 0.4586031833919535,
 0.08056314147139933,
 0.6536560706416813,
 0.6372695917965714,
 0.7084020559593186,
 0.608237865961373,
 0.9130442218729786,
 0.9935689824140225,
 0.7230541995516246,
 0.27750639670333294,
 1.0039044086536,
 0.06910993330911828,
 0.5372171071867065,
 0.3719924598494384,
 0.7252229740305955,
 0.4811722184642709,
 0.47970933471881216,
 0.7951094757962199,
 0.909875883241578,
 0.9954502297754981,
 0.8095709267738218,
 0.5977451684163917,
 0.9925163936066613,
 0.2644847959474258,
 0.9664180832326759,
 0.6620168009348842,
 0.7103518653392862,
 0.21236096726162168,
 0.6308116903933049,
 0.9935202451339438,
 0.25792119624479287,
 0.7272387098671871,
 0.7687195306584957,
 0.7181574640223471,
 0.7079856347602176,
 0.3644927107106283,
 0.7817793556514121,
 0.6922214953872138,
 0.6689967127054955,
 0.7117716062961037,
 0.992543480719216,
 1.1157866568931347,
 0.6671772396650105,
 0.41581757717240997,
 0.5189551547526736,
 0.05450655594023559,
 0.8571400867641998,
 0.9956343218685672,
 0.2985079503718906,
 0.4909926849312424,
 0.4372956316727346,
 0.9097215339537147,
 0.5522631876232973,
 0.5816409258094362,
 0.9935567256155452,
 0.4971585080787668,
 0.40405488248464433,
 0.6806943459775465,
 0.7307803715612374,
 0.5789520428860501,
 0.9950579289777237,
 0.7505310016704005,
 0.7775806405079226,
 0.8543504309417497,
 0.9938186066065836,
 0.8616904301640509,
 0.4105688343037995,
 0.9349464501595548,
 0.3346546613320382,
 0.7273068983404928,
 0.34124045770500966,
 0.613724021400325,
 0.3132812930944461,
 0.7797565732548996,
 0.4847614008382401,
 0.5284722766476058,
 0.8503608365864237,
 0.8262853358888869,
 0.4608143254570044,
 0.4773359858400499,
 0.664711761278808,
 0.2018665047460244,
 0.9952879225010319,
 0.8042169966206982,
 0.7936608694455264,
 0.6002467302209181,
 0.6834944880670579,
 0.5425555122912001,
 0.9914078386620112,
 0.7617560514127468,
 0.9848454829871749,
 0.22653465418135202,
 0.6083103741581191,
 0.9365421535061715,
 0.4807236727270948,
 0.7941060223405716,
 0.18581117650681994,
 0.9918555280556091,
 0.8241745515393184,
 0.6893304070492371,
 0.44852407696279883,
 1.1269461477340803,
 0.37644667533054504,
 0.5504534237898309,
 0.48422484132659954,
 0.9959166472670373,
 0.9892304290096905,
 0.56324169053635,
 0.5317722241248543,
 0.5645719702183021,
 0.7252433460406795,
 0.9920480362739116,
 0.6551030014845165,
 0.9940852097770895,
 0.9015238613474194,
 0.42337982401776286,
 1.0709694893900168,
 0.45090114893059446,
 0.6630878053179701,
 0.23470623899261928,
 0.091684773068847,
 0.2506543392908341,
 0.17778279862278337,
 0.587774856549172,
 0.461858576005941,
 0.5788666071331854,
 0.8030930373398002,
 0.4811741848547142,
 0.3223595892478394,
 0.8901946272761536,
 0.5079163220746603,
 0.5062289892112122,
 0.4057713041291733,
 0.5078761009050569,
 0.10473139185911276,
 0.3948259753362592,
 0.5496187136198555,
 0.8466812718368556,
 0.7293387644309931,
 0.4763083741227085,
 0.30436813666228113,
 0.42851841441526345,
 0.6049719251912037,
 0.40655310183320664,
 0.7679886625604273,
 0.5758051358405618,
 0.21520220501292542,
 1.1864381921811877,
 0.7859263484999899,
 0.6590712442089142,
 0.45350050006163867,
 1.070386387487356,
 0.3522740114908841,
 0.5786315214821188,
 0.7396138056807348,
 0.9658270632499937,
 0.7221811572535575,
 0.43038290626405457,
 0.9886248306697563,
 0.6178402178217258,
 0.4744443873652353,
 0.4888533307128739,
 0.6132948374809983,
 1.0637161131374189,
 1.1308922376982875,
 0.4816046854160443,
 0.5003337894739142,
 0.5585322951558966,
 0.5381801914528636,
 0.8122541445659242,
 0.3533072821915063,
 0.5097325057934994,
 0.6618766617254462,
 0.8018331976053105,
 0.30089184798033053,
 0.26096545662131715,
 0.6704870620581674,
 0.9929591178063935,
 0.9936703386336523,
 0.8488263478969102,
 0.7887606153725975,
 0.516626685942295,
 0.5225061000595935,
 0.9973695100859479,
 0.3821697081692576,
 0.7285189925102216,
 0.31503347216436955,
 0.1883792039279331,
 0.4950739521083153,
 0.8718158130458709,
 0.4799498797089861,
 0.5600315806270872,
 1.0188346107678241,
 0.37497436143641755,
 0.9921083213924276,
 0.35778630126305166,
 0.3495989872597073,
 0.5550430083842889,
 0.7288062976139977,
 0.4627641258753461,
 0.3117847536741448,
 0.4866557406045209,
 0.642937718109889,
 0.22639117893523147,
 0.4553345743817794,
 0.7422929186360507,
 0.4504916646922842,
 0.30286558130073177,
 0.995404870982279,
 0.5202948918189393,
 1.0153075748549079,
 0.7082800052201612,
 0.482340624099526,
 0.33928085874445424,
 0.7129748123478008,
 0.4915152424485474,
 0.3569048127685336,
 0.33284765922747817,
 0.907527295045581,
 0.684485266717401,
 0.2347770721581054,
 0.08012648476644446,
 0.6086591076866014,
 0.8158060751208763,
 0.9181498383534014,
 0.46831651322306517,
 0.9220134753895146,
 0.6132592900522229,
 0.45500672623309857,
 0.9926410747530324,
 0.38335092311794916,
 0.48598827621349727,
 0.7874436350593684,
 0.3847932695008507,
 0.08740298129029926,
 0.16937849258362325,
 0.6304627425387672,
 0.6486017437880635,
 0.7977110682119414,
 1.0496049019129785,
 0.9904356144403061,
 0.6583656243508824,
 0.31518897551602115,
 0.49780600734476055,
 0.5383591263066627,
 0.9485578998012862,
 0.6618502085951135,
 0.5917832542098278,
 0.4410164010294805,
 0.28570559711466176,
 0.5050274684393892,
 0.0501600697351402,
 0.6440847424759337,
 0.18491585090500604,
 0.5918529600834761,
 0.9895109013996575,
 0.38258469367439646,
 0.5952282231504675,
 0.4728359416976405,
 0.9944799403101328,
 0.4436808646909036,
 0.7539669888062949,
 0.06339574171038923,
 0.46947917684918616,
 0.9932192337137195,
 0.7216485259935048,
 0.6274722019251826,
 0.6586233501910114,
 0.30170692221009043,
 0.4130511792573808,
 0.9950724797978893,
 0.412440810295081,
 0.3705936426138471,
 0.9919510633412855,
 0.9960416066870093,
 0.9923825117824091,
 0.9953921724335213,
 0.6872484059631494,
 0.3564175620464834,
 0.5439296904182321,
 0.41254689235146264,
 0.5558148172111058,
 0.9807934601365277,
 0.7134110477858728,
 0.48785212509720205,
 1.139828245290775,
 0.9554825641758898,
 0.5513276331204633,
 0.5863633810699134,
 1.0468958118859895,
 0.40698802572251797,
 0.9793083415137196,
 0.9941458634310801,
 0.7047668426633855,
 0.8010907071977234,
 0.2830207663384474,
 0.4819115886824565,
 0.562241869810363,
 0.9182409406586161,
 0.8152912492124861,
 0.20247165918311763,
 0.1642186244840756,
 0.5531762870858873,
 0.634165772526808,
 0.5974331023539423,
 0.6823018800986533,
 0.46428244233002564,
 0.2712244173656073,
 0.7204463435727168,
 0.6703538868016805,
 0.2229572722916418,
 0.865327630402202,
 0.4562495417365665,
 0.40253182636606394,
 0.3453759317073886,
 0.06749728666819357,
 0.7396042535746459,
 0.598344600686742,
 0.40517948177334695,
 0.4141082477615071,
 0.9983816360703651,
 0.8292563324705696,
 0.36763254379512983,
 1.2158829653136118,
 0.5922733154893518,
 0.01250609922172299,
 0.6250386092204286,
 0.5171928304880089,
 0.4040585771910688,
 0.4878426590788986,
 0.3441277997217525,
 0.24540729894859797,
 0.3354703477695785,
 0.9950011314696559]



In [9]:

    
predictor = rs.MassPredictor()
predictor.set_model(exp.model)



In [10]:

    
predictor.predict(data[['user','item']]['user'].tolist(),list([4,5,6]))









    Out[10]:





[0.987442505729575, 0.9767692732871204, 0.9850473952762439]



In [11]:

    
type(list(data[['user','item']]['user']))









    Out[11]:





list



In [12]:

    
type(list([1,2,34]))









    Out[12]:





list



In [6]:

    
df = pd.DataFrame.from_records([
    (1,[1,2,3,4]),
    (2,[5,6,7,8])
], columns=["user","item"])



In [11]:

    
df['item'].apply(np.ravel)









    Out[11]:





0    [1, 2, 3, 4]
1    [5, 6, 7, 8]
Name: item, dtype: object

test



In [2]:

    
experiment = alpenglow.experiments.AsymmetricFactorModelExperiment(
    top_k=100,
    seed=254938879,
    dimension=10,
    learning_rate=0.1,
    negative_rate=10
)
rankings = experiment.run("python/test_alpenglow/test_data_4", experimentType="online_id", verbose=False)
print(rankings["rank"].fillna(101).tolist())









    



[101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 12.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 3.0, 101.0, 101.0, 4.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 18.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 12.0, 2.0, 33.0, 101.0, 101.0, 15.0, 101.0, 7.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 25.0, 101.0, 23.0, 11.0, 35.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 18.0, 101.0, 9.0, 101.0, 7.0, 101.0, 101.0, 101.0, 5.0, 101.0, 101.0, 8.0, 101.0, 43.0, 6.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 54.0, 101.0, 9.0, 43.0, 101.0, 10.0, 6.0, 22.0, 48.0, 101.0, 101.0, 59.0, 101.0, 101.0, 101.0, 31.0, 11.0, 1.0, 48.0, 101.0, 44.0, 39.0, 101.0, 101.0, 1.0, 3.0, 101.0, 101.0, 101.0, 51.0, 4.0, 58.0, 101.0, 11.0, 28.0, 46.0, 70.0, 12.0, 14.0, 8.0, 101.0, 23.0, 101.0, 101.0, 15.0, 25.0, 10.0, 101.0, 63.0, 43.0, 21.0, 25.0, 59.0, 9.0, 101.0, 65.0, 101.0, 101.0, 6.0, 101.0, 37.0, 30.0, 101.0, 19.0, 26.0, 44.0, 74.0, 5.0, 101.0, 2.0, 101.0, 101.0, 47.0, 101.0, 101.0, 101.0, 101.0, 23.0, 101.0, 10.0, 40.0, 101.0, 101.0, 101.0, 101.0, 73.0, 34.0, 75.0, 101.0, 101.0, 39.0, 101.0, 71.0, 101.0, 14.0, 21.0, 42.0, 4.0, 101.0, 101.0, 6.0, 70.0, 101.0, 8.0, 51.0, 81.0, 6.0, 1.0, 14.0, 101.0, 101.0, 101.0, 101.0, 24.0, 101.0, 7.0, 101.0, 10.0, 101.0, 33.0, 6.0, 101.0, 101.0, 41.0, 67.0, 15.0, 92.0, 101.0, 101.0, 30.0, 22.0, 59.0, 2.0, 101.0, 76.0, 101.0, 30.0, 90.0, 21.0, 27.0, 6.0, 94.0, 34.0, 101.0, 101.0, 101.0, 101.0, 101.0, 24.0, 24.0, 101.0, 71.0, 47.0, 59.0, 40.0, 30.0, 101.0, 87.0, 11.0, 10.0, 85.0, 101.0, 101.0, 59.0, 101.0, 41.0, 16.0, 86.0, 101.0, 65.0, 56.0, 101.0, 43.0, 2.0, 10.0, 101.0, 66.0, 16.0, 101.0, 101.0, 101.0, 101.0, 42.0, 67.0, 9.0, 101.0, 101.0, 40.0, 101.0, 70.0, 59.0, 101.0, 3.0, 95.0, 16.0, 1.0, 8.0, 25.0, 101.0, 101.0, 101.0, 15.0, 101.0, 64.0, 13.0, 101.0, 2.0, 7.0, 101.0, 101.0, 101.0, 101.0, 47.0, 21.0, 58.0, 52.0, 9.0, 43.0, 19.0, 99.0, 101.0, 101.0, 101.0, 101.0, 93.0, 101.0, 46.0, 101.0, 2.0, 101.0, 86.0, 101.0, 101.0, 6.0, 56.0, 101.0, 52.0, 8.0, 24.0, 77.0, 5.0, 101.0, 94.0, 5.0, 101.0, 61.0, 101.0, 48.0, 15.0, 3.0, 9.0, 101.0, 101.0, 12.0, 15.0, 101.0, 18.0, 101.0, 11.0, 22.0, 19.0, 6.0, 39.0, 35.0, 2.0, 101.0, 1.0, 101.0, 101.0, 101.0, 56.0, 62.0, 3.0, 75.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 10.0, 32.0, 101.0, 17.0, 24.0, 101.0, 69.0, 101.0, 39.0, 44.0, 101.0, 90.0, 43.0, 13.0, 101.0, 30.0, 2.0, 1.0, 90.0, 101.0, 87.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 90.0, 101.0, 3.0, 38.0, 54.0, 101.0, 101.0, 60.0, 8.0, 101.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 11.0, 4.0, 101.0, 101.0, 98.0, 101.0, 41.0, 101.0, 4.0, 101.0, 27.0, 101.0, 18.0, 101.0, 4.0, 101.0, 70.0, 101.0, 101.0, 2.0, 101.0, 9.0, 25.0, 53.0, 31.0, 1.0, 16.0, 101.0, 12.0, 66.0, 6.0, 1.0, 101.0, 88.0, 19.0, 42.0, 101.0, 53.0, 101.0, 101.0, 49.0, 34.0, 101.0, 27.0, 101.0, 58.0, 94.0, 101.0, 101.0, 101.0, 95.0, 5.0, 90.0, 14.0, 101.0, 101.0, 2.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 49.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 62.0, 101.0, 69.0, 6.0, 101.0, 101.0, 3.0, 88.0, 86.0, 101.0, 101.0, 38.0, 19.0, 101.0, 48.0, 11.0, 101.0, 13.0, 15.0, 101.0, 31.0, 58.0, 101.0, 10.0, 101.0, 85.0, 101.0, 101.0, 1.0, 78.0, 39.0, 10.0, 80.0, 101.0, 101.0, 101.0, 2.0, 1.0, 101.0, 35.0, 101.0, 101.0, 69.0, 101.0, 101.0, 4.0, 101.0, 19.0, 101.0, 101.0, 81.0, 57.0, 101.0, 11.0, 47.0, 101.0, 61.0, 101.0, 101.0, 101.0, 101.0, 4.0, 14.0, 101.0, 7.0, 24.0, 4.0, 88.0, 66.0, 101.0, 101.0, 48.0, 101.0, 6.0, 52.0, 10.0, 101.0, 101.0, 101.0, 101.0, 21.0, 101.0, 4.0, 101.0, 3.0, 2.0, 101.0, 12.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 84.0, 69.0, 77.0, 101.0, 15.0, 33.0, 80.0, 97.0, 101.0, 101.0, 16.0, 101.0, 31.0, 101.0, 19.0, 101.0, 85.0, 6.0, 28.0, 14.0, 101.0, 101.0, 1.0, 101.0, 7.0, 101.0, 101.0, 100.0, 24.0, 23.0, 101.0, 90.0, 101.0, 27.0, 101.0, 101.0, 51.0, 101.0, 4.0, 101.0, 101.0, 52.0, 6.0, 62.0, 4.0, 12.0, 28.0, 83.0, 101.0, 13.0, 101.0, 1.0, 12.0, 15.0, 101.0, 101.0, 39.0, 101.0, 101.0, 24.0, 101.0, 7.0, 101.0, 101.0, 101.0, 95.0, 38.0, 101.0, 101.0, 11.0, 101.0, 101.0, 101.0, 101.0, 4.0, 101.0, 101.0, 42.0, 101.0, 54.0, 24.0, 2.0, 41.0, 11.0, 1.0, 24.0, 101.0, 24.0, 19.0, 14.0, 48.0, 2.0, 101.0, 9.0, 65.0, 90.0, 2.0, 34.0, 101.0, 5.0, 89.0, 101.0, 101.0, 101.0, 80.0, 4.0, 101.0, 7.0, 101.0, 24.0, 7.0, 4.0, 63.0, 27.0, 86.0, 101.0, 101.0, 74.0, 101.0, 101.0, 1.0, 7.0, 101.0, 7.0, 101.0, 95.0, 101.0, 95.0, 101.0, 46.0, 101.0, 21.0, 44.0, 101.0, 101.0, 38.0, 40.0, 65.0, 76.0, 101.0, 5.0, 7.0, 5.0, 101.0, 10.0, 25.0, 12.0, 17.0, 95.0, 101.0, 82.0, 50.0, 66.0, 101.0, 33.0, 101.0, 9.0, 81.0, 8.0, 9.0, 101.0, 101.0, 65.0, 101.0, 26.0, 2.0, 101.0, 101.0, 3.0, 2.0, 101.0, 75.0, 30.0, 88.0, 4.0, 101.0, 101.0, 95.0, 53.0, 11.0, 3.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 83.0, 4.0, 14.0, 30.0, 19.0, 101.0, 3.0, 31.0, 3.0, 83.0, 101.0, 53.0, 101.0, 101.0, 101.0, 101.0, 21.0, 83.0, 81.0, 97.0, 101.0, 1.0, 12.0, 90.0, 7.0, 13.0, 101.0, 66.0, 20.0, 5.0, 2.0, 6.0, 101.0, 12.0, 2.0, 101.0, 101.0, 2.0, 50.0, 1.0, 2.0, 101.0, 101.0, 1.0, 28.0, 25.0, 3.0, 12.0, 1.0, 101.0, 101.0, 43.0, 101.0, 101.0, 101.0, 9.0, 6.0, 101.0, 11.0, 101.0, 14.0, 22.0, 96.0, 4.0, 65.0, 101.0, 101.0, 101.0, 77.0, 101.0, 101.0, 4.0, 25.0, 19.0, 65.0, 101.0, 81.0, 101.0, 100.0, 88.0, 15.0, 3.0, 101.0, 11.0, 13.0, 21.0, 101.0, 101.0, 12.0, 101.0, 101.0, 50.0, 20.0, 35.0, 19.0, 101.0, 101.0, 23.0, 12.0, 17.0, 101.0, 46.0, 3.0, 6.0, 7.0, 47.0, 25.0, 10.0, 7.0, 46.0, 65.0, 6.0, 101.0, 101.0, 4.0, 11.0, 4.0, 101.0, 27.0, 15.0, 9.0, 101.0, 93.0, 7.0, 13.0, 69.0, 66.0, 101.0, 101.0, 13.0, 12.0, 13.0, 101.0, 80.0, 101.0, 87.0, 15.0, 21.0, 9.0, 74.0, 16.0, 101.0, 3.0, 58.0, 48.0, 31.0, 101.0, 101.0, 35.0, 14.0, 8.0, 101.0, 101.0, 1.0, 101.0, 15.0, 101.0, 101.0, 13.0, 4.0, 101.0, 101.0, 101.0, 11.0, 61.0, 101.0, 101.0, 41.0, 101.0, 101.0, 101.0, 22.0, 12.0, 2.0, 27.0, 5.0, 25.0, 2.0, 101.0, 101.0, 5.0, 101.0, 74.0, 8.0, 101.0, 50.0, 101.0, 101.0, 10.0, 7.0, 42.0, 1.0, 39.0, 101.0, 30.0, 2.0, 101.0, 60.0, 25.0, 88.0, 22.0, 1.0, 101.0, 4.0, 9.0, 101.0, 21.0, 101.0, 2.0, 6.0, 8.0, 101.0, 28.0, 1.0, 101.0, 1.0, 6.0, 101.0, 30.0, 13.0, 13.0, 101.0, 30.0, 101.0, 101.0, 101.0]



In [16]:

    
boModelExperiment = alpenglow.experiments.BatchAndOnlineExperiment(
    top_k=100,
    seed=254938879,
    dimension=10,
    period_length=1000,
    batch_learning_rate=0.07,
    batch_negative_rate=20,
    online_learning_rate=0.15,
    online_negative_rate=120,
    number_of_iterations=3,
    clear_model=True,
)
boRankings = boModelExperiment.run("python/test_alpenglow/test_data_4", experimentType="online_id", verbose=True)
list(boRankings["rank"].fillna(101).values)









    



reading data...
data reading finished
running experiment...






    Out[16]:





[101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 8.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 2.0,
 101.0,
 101.0,
 2.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 22.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 2.0,
 101.0,
 101.0,
 101.0,
 23.0,
 14.0,
 33.0,
 101.0,
 101.0,
 35.0,
 101.0,
 41.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 15.0,
 101.0,
 4.0,
 24.0,
 5.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 29.0,
 101.0,
 4.0,
 101.0,
 37.0,
 101.0,
 101.0,
 101.0,
 6.0,
 101.0,
 101.0,
 56.0,
 101.0,
 47.0,
 51.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 57.0,
 101.0,
 10.0,
 7.0,
 101.0,
 25.0,
 32.0,
 9.0,
 36.0,
 101.0,
 101.0,
 16.0,
 101.0,
 101.0,
 101.0,
 30.0,
 2.0,
 11.0,
 50.0,
 101.0,
 19.0,
 1.0,
 101.0,
 101.0,
 18.0,
 14.0,
 101.0,
 101.0,
 101.0,
 28.0,
 52.0,
 63.0,
 101.0,
 40.0,
 34.0,
 56.0,
 48.0,
 4.0,
 69.0,
 7.0,
 101.0,
 33.0,
 101.0,
 101.0,
 67.0,
 70.0,
 64.0,
 101.0,
 74.0,
 26.0,
 20.0,
 8.0,
 56.0,
 73.0,
 101.0,
 74.0,
 101.0,
 101.0,
 56.0,
 101.0,
 44.0,
 36.0,
 101.0,
 4.0,
 21.0,
 26.0,
 4.0,
 12.0,
 101.0,
 15.0,
 101.0,
 101.0,
 49.0,
 101.0,
 101.0,
 101.0,
 101.0,
 77.0,
 101.0,
 2.0,
 16.0,
 101.0,
 101.0,
 101.0,
 101.0,
 1.0,
 16.0,
 75.0,
 101.0,
 101.0,
 62.0,
 101.0,
 23.0,
 101.0,
 56.0,
 67.0,
 31.0,
 27.0,
 101.0,
 101.0,
 53.0,
 80.0,
 101.0,
 8.0,
 33.0,
 68.0,
 15.0,
 97.0,
 5.0,
 101.0,
 101.0,
 101.0,
 101.0,
 22.0,
 101.0,
 11.0,
 101.0,
 3.0,
 101.0,
 62.0,
 8.0,
 101.0,
 101.0,
 3.0,
 17.0,
 44.0,
 82.0,
 101.0,
 101.0,
 26.0,
 63.0,
 23.0,
 1.0,
 101.0,
 6.0,
 101.0,
 23.0,
 33.0,
 68.0,
 20.0,
 24.0,
 101.0,
 30.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 28.0,
 14.0,
 101.0,
 65.0,
 8.0,
 4.0,
 5.0,
 95.0,
 101.0,
 49.0,
 38.0,
 47.0,
 10.0,
 101.0,
 101.0,
 42.0,
 101.0,
 16.0,
 9.0,
 6.0,
 101.0,
 1.0,
 19.0,
 36.0,
 5.0,
 14.0,
 9.0,
 101.0,
 32.0,
 90.0,
 101.0,
 101.0,
 101.0,
 101.0,
 27.0,
 58.0,
 1.0,
 101.0,
 101.0,
 72.0,
 101.0,
 92.0,
 68.0,
 101.0,
 87.0,
 47.0,
 47.0,
 3.0,
 12.0,
 37.0,
 101.0,
 101.0,
 101.0,
 65.0,
 101.0,
 71.0,
 1.0,
 101.0,
 77.0,
 16.0,
 101.0,
 101.0,
 101.0,
 101.0,
 1.0,
 101.0,
 101.0,
 38.0,
 10.0,
 50.0,
 40.0,
 24.0,
 101.0,
 101.0,
 101.0,
 101.0,
 30.0,
 101.0,
 8.0,
 101.0,
 93.0,
 101.0,
 11.0,
 101.0,
 101.0,
 5.0,
 95.0,
 101.0,
 96.0,
 57.0,
 24.0,
 101.0,
 18.0,
 101.0,
 92.0,
 3.0,
 101.0,
 28.0,
 101.0,
 101.0,
 8.0,
 101.0,
 13.0,
 18.0,
 101.0,
 13.0,
 21.0,
 101.0,
 4.0,
 101.0,
 10.0,
 58.0,
 79.0,
 34.0,
 54.0,
 96.0,
 9.0,
 101.0,
 1.0,
 101.0,
 101.0,
 101.0,
 23.0,
 5.0,
 57.0,
 101.0,
 101.0,
 101.0,
 101.0,
 39.0,
 37.0,
 101.0,
 2.0,
 16.0,
 101.0,
 4.0,
 35.0,
 101.0,
 7.0,
 101.0,
 41.0,
 99.0,
 101.0,
 101.0,
 7.0,
 17.0,
 101.0,
 84.0,
 59.0,
 46.0,
 55.0,
 101.0,
 101.0,
 101.0,
 101.0,
 12.0,
 27.0,
 101.0,
 101.0,
 101.0,
 100.0,
 101.0,
 101.0,
 101.0,
 19.0,
 101.0,
 101.0,
 47.0,
 1.0,
 101.0,
 25.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 32.0,
 41.0,
 16.0,
 29.0,
 4.0,
 101.0,
 79.0,
 84.0,
 36.0,
 101.0,
 101.0,
 101.0,
 6.0,
 101.0,
 45.0,
 88.0,
 9.0,
 101.0,
 101.0,
 1.0,
 101.0,
 4.0,
 32.0,
 58.0,
 67.0,
 4.0,
 56.0,
 82.0,
 18.0,
 101.0,
 1.0,
 101.0,
 31.0,
 21.0,
 69.0,
 101.0,
 79.0,
 83.0,
 64.0,
 101.0,
 58.0,
 101.0,
 101.0,
 66.0,
 3.0,
 31.0,
 15.0,
 20.0,
 98.0,
 80.0,
 49.0,
 6.0,
 101.0,
 3.0,
 5.0,
 101.0,
 1.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 93.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 44.0,
 93.0,
 101.0,
 61.0,
 101.0,
 56.0,
 7.0,
 46.0,
 33.0,
 101.0,
 101.0,
 44.0,
 101.0,
 101.0,
 6.0,
 46.0,
 45.0,
 41.0,
 16.0,
 29.0,
 101.0,
 78.0,
 101.0,
 2.0,
 89.0,
 36.0,
 91.0,
 101.0,
 1.0,
 101.0,
 101.0,
 4.0,
 38.0,
 70.0,
 101.0,
 29.0,
 16.0,
 101.0,
 53.0,
 91.0,
 101.0,
 101.0,
 1.0,
 101.0,
 74.0,
 39.0,
 101.0,
 9.0,
 101.0,
 101.0,
 7.0,
 19.0,
 101.0,
 29.0,
 16.0,
 67.0,
 101.0,
 101.0,
 101.0,
 59.0,
 101.0,
 1.0,
 13.0,
 101.0,
 101.0,
 15.0,
 64.0,
 101.0,
 14.0,
 22.0,
 101.0,
 17.0,
 101.0,
 101.0,
 21.0,
 101.0,
 101.0,
 101.0,
 101.0,
 101.0,
 1.0,
 101.0,
 90.0,
 101.0,
 4.0,
 1.0,
 16.0,
 23.0,
 101.0,
 101.0,
 101.0,
 49.0,
 56.0,
 101.0,
 101.0,
 5.0,
 21.0,
 101.0,
 101.0,
 17.0,
 5.0,
 68.0,
 101.0,
 101.0,
 54.0,
 18.0,
 101.0,
 9.0,
 101.0,
 16.0,
 8.0,
 19.0,
 41.0,
 62.0,
 8.0,
 27.0,
 101.0,
 4.0,
 101.0,
 45.0,
 50.0,
 61.0,
 7.0,
 16.0,
 14.0,
 101.0,
 101.0,
 101.0,
 10.0,
 101.0,
 101.0,
 101.0,
 101.0,
 11.0,
 10.0,
 101.0,
 44.0,
 101.0,
 18.0,
 2.0,
 85.0,
 101.0,
 17.0,
 101.0,
 16.0,
 101.0,
 29.0,
 21.0,
 101.0,
 101.0,
 101.0,
 20.0,
 101.0,
 101.0,
 32.0,
 101.0,
 12.0,
 23.0,
 101.0,
 101.0,
 101.0,
 31.0,
 49.0,
 43.0,
 101.0,
 101.0,
 23.0,
 75.0,
 101.0,
 2.0,
 101.0,
 48.0,
 20.0,
 8.0,
 87.0,
 5.0,
 1.0,
 30.0,
 25.0,
 2.0,
 2.0,
 101.0,
 101.0,
 26.0,
 4.0,
 101.0,
 56.0,
 28.0,
 36.0,
 101.0,
 34.0,
 5.0,
 17.0,
 101.0,
 19.0,
 24.0,
 101.0,
 16.0,
 101.0,
 101.0,
 10.0,
 80.0,
 101.0,
 101.0,
 39.0,
 47.0,
 101.0,
 9.0,
 101.0,
 101.0,
 28.0,
 101.0,
 2.0,
 101.0,
 101.0,
 24.0,
 30.0,
 101.0,
 5.0,
 101.0,
 16.0,
 101.0,
 41.0,
 5.0,
 55.0,
 4.0,
 101.0,
 38.0,
 101.0,
 101.0,
 101.0,
 11.0,
 101.0,
 101.0,
 101.0,
 25.0,
 2.0,
 7.0,
 101.0,
 7.0,
 30.0,
 16.0,
 8.0,
 42.0,
 101.0,
 5.0,
 32.0,
 38.0,
 101.0,
 101.0,
 27.0,
 90.0,
 101.0,
 82.0,
 13.0,
 101.0,
 101.0,
 62.0,
 65.0,
 51.0,
 6.0,
 7.0,
 101.0,
 28.0,
 1.0,
 101.0,
 14.0,
 10.0,
 5.0,
 1.0,
 101.0,
 101.0,
 29.0,
 12.0,
 9.0,
 1.0,
 101.0,
 85.0,
 17.0,
 101.0,
 101.0,
 6.0,
 93.0,
 11.0,
 13.0,
 45.0,
 13.0,
 101.0,
 13.0,
 13.0,
 7.0,
 32.0,
 101.0,
 40.0,
 91.0,
 101.0,
 46.0,
 101.0,
 16.0,
 3.0,
 101.0,
 93.0,
 18.0,
 1.0,
 44.0,
 9.0,
 11.0,
 101.0,
 101.0,
 38.0,
 19.0,
 48.0,
 1.0,
 15.0,
 12.0,
 11.0,
 6.0,
 101.0,
 12.0,
 5.0,
 101.0,
 1.0,
 55.0,
 24.0,
 101.0,
 1.0,
 37.0,
 101.0,
 5.0,
 20.0,
 1.0,
 101.0,
 101.0,
 95.0,
 101.0,
 101.0,
 6.0,
 16.0,
 4.0,
 101.0,
 13.0,
 8.0,
 27.0,
 11.0,
 101.0,
 1.0,
 94.0,
 27.0,
 101.0,
 51.0,
 101.0,
 101.0,
 101.0,
 2.0,
 8.0,
 21.0,
 20.0,
 101.0,
 78.0,
 31.0,
 25.0,
 22.0,
 6.0,
 5.0,
 92.0,
 19.0,
 25.0,
 35.0,
 101.0,
 101.0,
 25.0,
 101.0,
 101.0,
 20.0,
 14.0,
 93.0,
 15.0,
 26.0,
 101.0,
 2.0,
 36.0,
 58.0,
 101.0,
 22.0,
 20.0,
 6.0,
 35.0,
 38.0,
 12.0,
 13.0,
 7.0,
 51.0,
 6.0,
 4.0,
 48.0,
 24.0,
 2.0,
 13.0,
 3.0,
 101.0,
 23.0,
 10.0,
 9.0,
 101.0,
 21.0,
 19.0,
 8.0,
 22.0,
 101.0,
 58.0,
 101.0,
 22.0,
 21.0,
 23.0,
 101.0,
 97.0,
 101.0,
 101.0,
 22.0,
 22.0,
 17.0,
 20.0,
 58.0,
 101.0,
 17.0,
 101.0,
 62.0,
 76.0,
 101.0,
 101.0,
 20.0,
 25.0,
 1.0,
 101.0,
 65.0,
 5.0,
 101.0,
 94.0,
 101.0,
 10.0,
 5.0,
 34.0,
 37.0,
 93.0,
 101.0,
 26.0,
 96.0,
 101.0,
 101.0,
 19.0,
 101.0,
 4.0,
 101.0,
 11.0,
 21.0,
 5.0,
 10.0,
 4.0,
 42.0,
 1.0,
 58.0,
 101.0,
 4.0,
 95.0,
 13.0,
 14.0,
 101.0,
 8.0,
 94.0,
 101.0,
 17.0,
 4.0,
 46.0,
 1.0,
 70.0,
 101.0,
 15.0,
 8.0,
 17.0,
 66.0,
 56.0,
 101.0,
 10.0,
 1.0,
 101.0,
 4.0,
 8.0,
 25.0,
 21.0,
 101.0,
 5.0,
 2.0,
 32.0,
 92.0,
 14.0,
 1.0,
 101.0,
 1.0,
 13.0,
 55.0,
 101.0,
 4.0,
 28.0,
 29.0,
 101.0,
 101.0,
 101.0,
 101.0]



In [19]:

    
factorModelExperiment = alpenglow.experiments.FactorModelExperiment(
    top_k=100,
    seed=254938879,
    dimension=10,
    learning_rate=0.1,
    negative_rate=10
)
facRankings = factorModelExperiment.run("python/test_alpenglow/test_data_4", experimentType="online_id", verbose=True)
print(facRankings["rank"].fillna(101).tolist())









    



reading data...
data reading finished
running experiment...
[101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 8.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 23.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 101.0, 101.0, 101.0, 24.0, 14.0, 33.0, 101.0, 101.0, 38.0, 101.0, 41.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 17.0, 101.0, 4.0, 23.0, 5.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 28.0, 101.0, 4.0, 101.0, 41.0, 101.0, 101.0, 101.0, 14.0, 101.0, 101.0, 57.0, 101.0, 46.0, 52.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 53.0, 101.0, 10.0, 7.0, 101.0, 25.0, 33.0, 9.0, 40.0, 101.0, 101.0, 13.0, 101.0, 101.0, 101.0, 27.0, 2.0, 14.0, 51.0, 101.0, 22.0, 1.0, 101.0, 101.0, 24.0, 21.0, 101.0, 101.0, 101.0, 27.0, 55.0, 62.0, 101.0, 42.0, 37.0, 58.0, 47.0, 3.0, 71.0, 7.0, 101.0, 36.0, 101.0, 101.0, 62.0, 71.0, 64.0, 101.0, 75.0, 36.0, 20.0, 11.0, 68.0, 73.0, 101.0, 72.0, 101.0, 101.0, 56.0, 101.0, 47.0, 33.0, 101.0, 3.0, 22.0, 22.0, 4.0, 23.0, 101.0, 20.0, 101.0, 101.0, 54.0, 101.0, 101.0, 101.0, 101.0, 77.0, 101.0, 3.0, 21.0, 101.0, 101.0, 101.0, 101.0, 1.0, 17.0, 78.0, 101.0, 101.0, 61.0, 101.0, 19.0, 101.0, 44.0, 69.0, 37.0, 40.0, 101.0, 101.0, 51.0, 83.0, 101.0, 47.0, 91.0, 19.0, 5.0, 1.0, 9.0, 101.0, 101.0, 101.0, 101.0, 42.0, 101.0, 11.0, 101.0, 12.0, 101.0, 98.0, 87.0, 101.0, 101.0, 39.0, 31.0, 57.0, 57.0, 101.0, 101.0, 62.0, 66.0, 16.0, 101.0, 101.0, 81.0, 101.0, 77.0, 57.0, 25.0, 26.0, 53.0, 29.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 38.0, 10.0, 101.0, 30.0, 97.0, 101.0, 43.0, 57.0, 101.0, 99.0, 41.0, 1.0, 2.0, 101.0, 101.0, 16.0, 101.0, 73.0, 97.0, 89.0, 101.0, 82.0, 86.0, 4.0, 75.0, 29.0, 8.0, 101.0, 10.0, 4.0, 101.0, 101.0, 101.0, 101.0, 80.0, 60.0, 9.0, 101.0, 101.0, 72.0, 101.0, 78.0, 45.0, 101.0, 78.0, 21.0, 70.0, 22.0, 20.0, 85.0, 101.0, 101.0, 101.0, 6.0, 101.0, 61.0, 1.0, 101.0, 47.0, 66.0, 101.0, 101.0, 101.0, 101.0, 101.0, 33.0, 101.0, 12.0, 2.0, 19.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 85.0, 101.0, 101.0, 101.0, 98.0, 101.0, 101.0, 101.0, 78.0, 15.0, 93.0, 101.0, 88.0, 99.0, 89.0, 101.0, 101.0, 101.0, 2.0, 10.0, 101.0, 91.0, 101.0, 57.0, 83.0, 60.0, 15.0, 20.0, 101.0, 9.0, 101.0, 101.0, 25.0, 101.0, 4.0, 6.0, 20.0, 90.0, 76.0, 99.0, 20.0, 101.0, 9.0, 69.0, 101.0, 101.0, 1.0, 3.0, 10.0, 100.0, 101.0, 101.0, 101.0, 51.0, 14.0, 101.0, 8.0, 101.0, 101.0, 50.0, 89.0, 101.0, 73.0, 21.0, 54.0, 97.0, 79.0, 59.0, 101.0, 12.0, 101.0, 15.0, 101.0, 101.0, 76.0, 8.0, 59.0, 101.0, 101.0, 43.0, 101.0, 101.0, 2.0, 101.0, 23.0, 101.0, 42.0, 21.0, 59.0, 101.0, 101.0, 86.0, 86.0, 101.0, 57.0, 101.0, 101.0, 101.0, 101.0, 101.0, 73.0, 34.0, 101.0, 48.0, 7.0, 101.0, 79.0, 28.0, 47.0, 101.0, 101.0, 101.0, 4.0, 101.0, 33.0, 22.0, 60.0, 101.0, 101.0, 9.0, 101.0, 12.0, 101.0, 53.0, 24.0, 3.0, 101.0, 25.0, 44.0, 78.0, 43.0, 66.0, 101.0, 93.0, 99.0, 101.0, 101.0, 74.0, 11.0, 101.0, 24.0, 90.0, 101.0, 40.0, 22.0, 101.0, 68.0, 21.0, 101.0, 101.0, 93.0, 84.0, 33.0, 68.0, 38.0, 101.0, 15.0, 101.0, 68.0, 101.0, 10.0, 101.0, 101.0, 61.0, 101.0, 101.0, 101.0, 32.0, 101.0, 55.0, 20.0, 29.0, 62.0, 23.0, 101.0, 19.0, 97.0, 52.0, 59.0, 101.0, 101.0, 101.0, 65.0, 101.0, 101.0, 101.0, 101.0, 101.0, 14.0, 101.0, 91.0, 40.0, 101.0, 94.0, 101.0, 101.0, 101.0, 22.0, 99.0, 5.0, 51.0, 101.0, 14.0, 3.0, 101.0, 101.0, 17.0, 101.0, 89.0, 69.0, 101.0, 101.0, 52.0, 101.0, 13.0, 101.0, 101.0, 37.0, 101.0, 101.0, 2.0, 97.0, 101.0, 101.0, 59.0, 27.0, 17.0, 101.0, 101.0, 101.0, 58.0, 70.0, 18.0, 101.0, 72.0, 37.0, 90.0, 79.0, 7.0, 66.0, 101.0, 101.0, 99.0, 101.0, 101.0, 50.0, 101.0, 101.0, 100.0, 101.0, 14.0, 101.0, 101.0, 101.0, 66.0, 94.0, 101.0, 23.0, 101.0, 91.0, 101.0, 100.0, 11.0, 101.0, 101.0, 99.0, 101.0, 101.0, 101.0, 101.0, 2.0, 92.0, 98.0, 101.0, 71.0, 10.0, 101.0, 101.0, 101.0, 9.0, 66.0, 101.0, 101.0, 101.0, 101.0, 5.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 21.0, 101.0, 101.0, 101.0, 101.0, 9.0, 101.0, 101.0, 101.0, 101.0, 101.0, 11.0, 101.0, 32.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 46.0, 101.0, 71.0, 46.0, 76.0, 101.0, 101.0, 94.0, 101.0, 101.0, 101.0, 101.0, 4.0, 101.0, 101.0, 101.0, 15.0, 92.0, 31.0, 61.0, 47.0, 101.0, 50.0, 101.0, 101.0, 43.0, 101.0, 71.0, 101.0, 101.0, 74.0, 101.0, 18.0, 50.0, 101.0, 101.0, 10.0, 101.0, 63.0, 101.0, 16.0, 101.0, 43.0, 44.0, 16.0, 13.0, 83.0, 54.0, 53.0, 101.0, 77.0, 19.0, 101.0, 101.0, 101.0, 58.0, 69.0, 101.0, 101.0, 101.0, 84.0, 101.0, 101.0, 101.0, 101.0, 101.0, 89.0, 101.0, 13.0, 101.0, 101.0, 23.0, 14.0, 101.0, 1.0, 4.0, 77.0, 101.0, 17.0, 9.0, 2.0, 44.0, 101.0, 101.0, 101.0, 101.0, 84.0, 28.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 88.0, 91.0, 94.0, 101.0, 101.0, 101.0, 101.0, 101.0, 12.0, 101.0, 101.0, 8.0, 34.0, 101.0, 101.0, 101.0, 76.0, 101.0, 91.0, 101.0, 77.0, 101.0, 7.0, 101.0, 101.0, 101.0, 101.0, 58.0, 35.0, 101.0, 43.0, 101.0, 101.0, 101.0, 101.0, 101.0, 13.0, 101.0, 26.0, 101.0, 101.0, 101.0, 97.0, 100.0, 47.0, 101.0, 101.0, 34.0, 101.0, 101.0, 101.0, 20.0, 41.0, 101.0, 87.0, 98.0, 12.0, 101.0, 101.0, 21.0, 101.0, 101.0, 101.0, 3.0, 101.0, 101.0, 101.0, 88.0, 83.0, 101.0, 59.0, 71.0, 101.0, 11.0, 74.0, 101.0, 101.0, 101.0, 16.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 64.0, 92.0, 101.0, 6.0, 4.0, 4.0, 101.0, 101.0, 71.0, 101.0, 101.0, 101.0, 10.0, 61.0, 101.0, 83.0, 17.0, 101.0, 101.0, 101.0, 101.0, 101.0, 53.0, 101.0, 9.0, 67.0, 101.0, 101.0, 7.0, 67.0, 101.0, 101.0, 101.0, 101.0, 18.0, 101.0, 47.0, 101.0, 13.0, 15.0, 101.0, 20.0, 19.0, 101.0, 66.0, 54.0, 101.0, 101.0, 56.0, 101.0, 3.0, 101.0, 101.0, 101.0, 101.0, 101.0, 36.0, 101.0, 101.0, 4.0, 88.0, 90.0, 101.0, 21.0, 101.0, 101.0, 87.0, 2.0, 17.0, 88.0, 101.0, 97.0, 78.0, 101.0, 101.0, 101.0, 98.0, 44.0, 101.0, 3.0, 35.0, 17.0, 2.0, 101.0, 27.0, 101.0, 94.0, 90.0, 101.0, 101.0, 101.0, 101.0, 101.0, 86.0, 18.0, 101.0, 88.0, 101.0, 101.0, 33.0, 101.0, 101.0, 59.0, 101.0, 101.0, 35.0, 101.0, 30.0, 101.0, 86.0, 84.0, 101.0, 101.0, 101.0, 101.0, 39.0, 71.0, 101.0, 97.0, 101.0, 101.0, 101.0, 101.0, 101.0, 32.0, 101.0, 101.0, 101.0, 96.0, 101.0, 101.0, 30.0, 101.0, 101.0, 3.0, 59.0, 33.0, 46.0, 1.0, 101.0, 2.0, 101.0, 18.0, 68.0, 101.0, 89.0, 43.0, 95.0, 15.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 62.0, 101.0, 15.0, 101.0, 101.0, 62.0, 75.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 14.0, 11.0, 101.0, 6.0, 101.0, 101.0, 59.0, 101.0, 90.0, 17.0, 101.0, 52.0, 101.0, 101.0]



In [22]:

    
data = pd.read_csv(
    "python/test_alpenglow/test_data_4",
    sep=' ',
    header=None,
    names=['time', 'user', 'item', 'id', 'score', 'eval']
)
sbExperiment = alpenglow.experiments.SimulatedBatchExperiment(
    top_k=100,
    negative_rate=3,
    seed=254938879,
    period_length=1000
)
rankings = sbExperiment.run(data, verbose=True)
print(rankings['rank'].fillna(101).tolist())









    



reading data...
data reading finished
running experiment...
[101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 49.0, 91.0, 19.0, 4.0, 1.0, 12.0, 101.0, 101.0, 101.0, 101.0, 33.0, 101.0, 12.0, 101.0, 11.0, 101.0, 98.0, 86.0, 101.0, 101.0, 44.0, 23.0, 43.0, 62.0, 101.0, 101.0, 62.0, 60.0, 10.0, 101.0, 101.0, 76.0, 101.0, 83.0, 61.0, 17.0, 28.0, 38.0, 23.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 42.0, 8.0, 101.0, 101.0, 99.0, 101.0, 39.0, 75.0, 101.0, 95.0, 12.0, 1.0, 2.0, 101.0, 101.0, 11.0, 101.0, 84.0, 80.0, 88.0, 101.0, 89.0, 88.0, 3.0, 46.0, 7.0, 9.0, 101.0, 101.0, 4.0, 101.0, 101.0, 101.0, 101.0, 88.0, 43.0, 10.0, 101.0, 101.0, 46.0, 101.0, 87.0, 31.0, 101.0, 90.0, 16.0, 50.0, 15.0, 23.0, 82.0, 101.0, 101.0, 101.0, 5.0, 101.0, 101.0, 1.0, 101.0, 79.0, 87.0, 101.0, 101.0, 101.0, 101.0, 101.0, 27.0, 101.0, 101.0, 101.0, 13.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 89.0, 16.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 11.0, 101.0, 101.0, 101.0, 101.0, 101.0, 43.0, 15.0, 19.0, 101.0, 12.0, 101.0, 101.0, 46.0, 101.0, 101.0, 101.0, 9.0, 101.0, 93.0, 101.0, 13.0, 101.0, 21.0, 101.0, 101.0, 101.0, 1.0, 3.0, 6.0, 101.0, 101.0, 101.0, 101.0, 33.0, 15.0, 101.0, 7.0, 101.0, 101.0, 21.0, 91.0, 101.0, 101.0, 16.0, 101.0, 101.0, 37.0, 101.0, 101.0, 6.0, 101.0, 9.0, 101.0, 101.0, 34.0, 10.0, 20.0, 101.0, 101.0, 62.0, 43.0, 101.0, 5.0, 101.0, 13.0, 101.0, 24.0, 23.0, 13.0, 101.0, 101.0, 22.0, 101.0, 101.0, 34.0, 101.0, 101.0, 101.0, 101.0, 101.0, 4.0, 38.0, 101.0, 68.0, 48.0, 101.0, 40.0, 32.0, 52.0, 101.0, 101.0, 101.0, 1.0, 101.0, 5.0, 8.0, 48.0, 101.0, 101.0, 101.0, 101.0, 101.0, 99.0, 41.0, 36.0, 1.0, 58.0, 31.0, 101.0, 54.0, 1.0, 87.0, 50.0, 96.0, 19.0, 67.0, 101.0, 28.0, 4.0, 101.0, 28.0, 47.0, 101.0, 49.0, 90.0, 74.0, 70.0, 55.0, 101.0, 91.0, 43.0, 6.0, 34.0, 1.0, 2.0, 101.0, 5.0, 101.0, 11.0, 101.0, 26.0, 101.0, 101.0, 12.0, 101.0, 101.0, 101.0, 4.0, 101.0, 47.0, 4.0, 26.0, 73.0, 100.0, 101.0, 75.0, 101.0, 78.0, 23.0, 101.0, 101.0, 101.0, 76.0, 101.0, 69.0, 87.0, 101.0, 51.0, 2.0, 8.0, 37.0, 47.0, 101.0, 3.0, 101.0, 10.0, 47.0, 17.0, 1.0, 17.0, 34.0, 11.0, 93.0, 13.0, 101.0, 101.0, 18.0, 13.0, 101.0, 29.0, 101.0, 101.0, 47.0, 101.0, 22.0, 101.0, 101.0, 1.0, 101.0, 101.0, 3.0, 3.0, 101.0, 101.0, 61.0, 10.0, 23.0, 101.0, 101.0, 54.0, 68.0, 79.0, 8.0, 101.0, 9.0, 101.0, 12.0, 69.0, 1.0, 20.0, 101.0, 101.0, 82.0, 47.0, 101.0, 101.0, 54.0, 101.0, 75.0, 101.0, 1.0, 101.0, 55.0, 101.0, 101.0, 2.0, 101.0, 13.0, 101.0, 101.0, 101.0, 101.0, 8.0, 101.0, 101.0, 53.0, 77.0, 101.0, 101.0, 101.0, 2.0, 43.0, 101.0, 101.0, 35.0, 101.0, 101.0, 30.0, 101.0, 14.0, 6.0, 22.0, 19.0, 13.0, 2.0, 10.0, 101.0, 1.0, 101.0, 14.0, 51.0, 23.0, 28.0, 16.0, 5.0, 101.0, 101.0, 101.0, 10.0, 101.0, 101.0, 46.0, 12.0, 18.0, 12.0, 101.0, 27.0, 101.0, 29.0, 1.0, 9.0, 101.0, 39.0, 94.0, 40.0, 101.0, 3.0, 17.0, 6.0, 101.0, 101.0, 4.0, 101.0, 101.0, 18.0, 101.0, 11.0, 21.0, 101.0, 101.0, 93.0, 19.0, 36.0, 23.0, 101.0, 101.0, 12.0, 39.0, 101.0, 2.0, 101.0, 33.0, 36.0, 21.0, 19.0, 25.0, 2.0, 16.0, 7.0, 5.0, 14.0, 101.0, 16.0, 13.0, 12.0, 101.0, 5.0, 39.0, 5.0, 48.0, 12.0, 2.0, 14.0, 101.0, 29.0, 26.0, 101.0, 16.0, 101.0, 58.0, 3.0, 81.0, 13.0, 101.0, 62.0, 2.0, 2.0, 21.0, 63.0, 101.0, 40.0, 101.0, 12.0, 101.0, 101.0, 1.0, 5.0, 101.0, 5.0, 93.0, 10.0, 101.0, 16.0, 30.0, 34.0, 7.0, 101.0, 9.0, 101.0, 101.0, 29.0, 19.0, 101.0, 101.0, 101.0, 2.0, 2.0, 3.0, 101.0, 17.0, 14.0, 13.0, 5.0, 14.0, 20.0, 14.0, 25.0, 27.0, 101.0, 65.0, 30.0, 14.0, 57.0, 5.0, 2.0, 101.0, 101.0, 53.0, 20.0, 15.0, 1.0, 34.0, 101.0, 2.0, 1.0, 101.0, 43.0, 5.0, 21.0, 2.0, 101.0, 101.0, 34.0, 4.0, 20.0, 1.0, 101.0, 15.0, 48.0, 101.0, 101.0, 2.0, 51.0, 16.0, 15.0, 14.0, 6.0, 101.0, 2.0, 13.0, 2.0, 16.0, 101.0, 101.0, 69.0, 37.0, 47.0, 101.0, 7.0, 10.0, 101.0, 101.0, 18.0, 2.0, 101.0, 14.0, 6.0, 101.0, 101.0, 37.0, 14.0, 3.0, 2.0, 12.0, 31.0, 8.0, 4.0, 101.0, 8.0, 3.0, 101.0, 1.0, 2.0, 21.0, 101.0, 1.0, 47.0, 62.0, 3.0, 25.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 12.0, 22.0, 3.0, 101.0, 6.0, 19.0, 18.0, 13.0, 84.0, 6.0, 70.0, 28.0, 101.0, 37.0, 101.0, 101.0, 101.0, 7.0, 8.0, 7.0, 15.0, 101.0, 101.0, 18.0, 15.0, 26.0, 10.0, 1.0, 79.0, 9.0, 21.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 11.0, 16.0, 37.0, 18.0, 29.0, 101.0, 4.0, 19.0, 31.0, 101.0, 25.0, 4.0, 1.0, 13.0, 28.0, 11.0, 8.0, 4.0, 36.0, 8.0, 101.0, 101.0, 40.0, 8.0, 16.0, 2.0, 101.0, 13.0, 16.0, 5.0, 101.0, 20.0, 6.0, 4.0, 27.0, 101.0, 33.0, 101.0, 16.0, 16.0, 23.0, 101.0, 34.0, 101.0, 101.0, 13.0, 6.0, 28.0, 16.0, 32.0, 101.0, 10.0, 101.0, 101.0, 101.0, 101.0, 101.0, 22.0, 31.0, 13.0, 101.0, 38.0, 1.0, 101.0, 10.0, 101.0, 17.0, 2.0, 7.0, 21.0, 69.0, 101.0, 101.0, 28.0, 101.0, 101.0, 11.0, 101.0, 20.0, 101.0, 8.0, 12.0, 1.0, 19.0, 3.0, 44.0, 1.0, 101.0, 71.0, 3.0, 18.0, 12.0, 2.0, 101.0, 16.0, 60.0, 101.0, 11.0, 8.0, 24.0, 1.0, 32.0, 101.0, 17.0, 4.0, 25.0, 75.0, 20.0, 47.0, 14.0, 2.0, 60.0, 1.0, 7.0, 33.0, 20.0, 101.0, 1.0, 10.0, 14.0, 99.0, 1.0, 1.0, 101.0, 1.0, 3.0, 56.0, 101.0, 8.0, 32.0, 22.0, 52.0, 77.0, 101.0, 101.0]



In [5]:

    
experiment = alpenglow.experiments.SvdppModelExperiment(
    top_k=100,
    seed=254938879,
    dimension=10,
    learning_rate=0.1,
    negative_rate=10
)
rankings = experiment.run("python/test_alpenglow/test_data_4", experimentType="online_id", verbose=True)
print(alpenglow.evaluation.DcgScore(rankings).mean())
print(rankings['rank'].fillna(101).tolist())









    



reading data...
data reading finished
running experiment...
0.11076980880439599
[101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 10.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 4.0, 101.0, 101.0, 2.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 10.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 34.0, 8.0, 6.0, 101.0, 101.0, 1.0, 101.0, 13.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 43.0, 101.0, 5.0, 30.0, 46.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 1.0, 101.0, 4.0, 101.0, 37.0, 101.0, 101.0, 101.0, 19.0, 101.0, 101.0, 21.0, 101.0, 47.0, 9.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 54.0, 101.0, 2.0, 15.0, 101.0, 58.0, 32.0, 28.0, 58.0, 101.0, 101.0, 17.0, 101.0, 101.0, 101.0, 4.0, 67.0, 32.0, 30.0, 101.0, 25.0, 7.0, 101.0, 101.0, 40.0, 54.0, 101.0, 101.0, 101.0, 57.0, 37.0, 55.0, 101.0, 44.0, 55.0, 15.0, 35.0, 8.0, 6.0, 8.0, 101.0, 9.0, 101.0, 101.0, 70.0, 36.0, 11.0, 101.0, 11.0, 27.0, 45.0, 24.0, 64.0, 44.0, 101.0, 21.0, 101.0, 101.0, 5.0, 101.0, 57.0, 13.0, 101.0, 44.0, 9.0, 50.0, 17.0, 45.0, 101.0, 59.0, 101.0, 101.0, 56.0, 101.0, 101.0, 101.0, 101.0, 26.0, 101.0, 11.0, 51.0, 101.0, 101.0, 101.0, 101.0, 84.0, 37.0, 77.0, 101.0, 101.0, 11.0, 101.0, 5.0, 101.0, 42.0, 55.0, 59.0, 81.0, 101.0, 101.0, 2.0, 21.0, 101.0, 71.0, 60.0, 60.0, 2.0, 42.0, 2.0, 101.0, 101.0, 101.0, 101.0, 22.0, 101.0, 9.0, 101.0, 12.0, 101.0, 65.0, 19.0, 101.0, 101.0, 61.0, 15.0, 25.0, 93.0, 101.0, 101.0, 29.0, 33.0, 13.0, 9.0, 101.0, 30.0, 101.0, 73.0, 48.0, 66.0, 76.0, 96.0, 6.0, 27.0, 101.0, 101.0, 101.0, 101.0, 101.0, 67.0, 16.0, 101.0, 83.0, 33.0, 55.0, 68.0, 11.0, 101.0, 68.0, 89.0, 20.0, 54.0, 101.0, 101.0, 46.0, 101.0, 37.0, 51.0, 101.0, 101.0, 19.0, 100.0, 31.0, 26.0, 90.0, 12.0, 101.0, 92.0, 84.0, 101.0, 101.0, 101.0, 101.0, 5.0, 101.0, 4.0, 101.0, 101.0, 96.0, 101.0, 74.0, 50.0, 101.0, 69.0, 53.0, 31.0, 32.0, 15.0, 29.0, 101.0, 101.0, 101.0, 41.0, 101.0, 21.0, 10.0, 101.0, 101.0, 26.0, 101.0, 101.0, 101.0, 101.0, 67.0, 62.0, 3.0, 86.0, 14.0, 52.0, 96.0, 27.0, 101.0, 101.0, 101.0, 101.0, 77.0, 101.0, 39.0, 101.0, 50.0, 101.0, 101.0, 101.0, 38.0, 89.0, 64.0, 101.0, 74.0, 76.0, 25.0, 101.0, 29.0, 101.0, 3.0, 1.0, 101.0, 30.0, 101.0, 35.0, 20.0, 27.0, 15.0, 101.0, 101.0, 12.0, 23.0, 101.0, 101.0, 101.0, 11.0, 9.0, 2.0, 101.0, 101.0, 18.0, 35.0, 101.0, 2.0, 77.0, 101.0, 101.0, 101.0, 47.0, 101.0, 55.0, 101.0, 101.0, 101.0, 98.0, 76.0, 101.0, 101.0, 18.0, 101.0, 45.0, 60.0, 101.0, 91.0, 89.0, 101.0, 85.0, 52.0, 70.0, 16.0, 93.0, 101.0, 101.0, 64.0, 101.0, 101.0, 76.0, 53.0, 101.0, 101.0, 29.0, 60.0, 101.0, 77.0, 101.0, 8.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 74.0, 101.0, 64.0, 101.0, 101.0, 101.0, 101.0, 101.0, 87.0, 22.0, 62.0, 31.0, 101.0, 101.0, 101.0, 36.0, 25.0, 101.0, 14.0, 101.0, 18.0, 101.0, 46.0, 101.0, 84.0, 101.0, 101.0, 20.0, 101.0, 10.0, 101.0, 2.0, 3.0, 93.0, 37.0, 8.0, 101.0, 27.0, 4.0, 25.0, 101.0, 101.0, 101.0, 101.0, 57.0, 30.0, 82.0, 101.0, 71.0, 16.0, 101.0, 101.0, 101.0, 12.0, 92.0, 19.0, 101.0, 22.0, 101.0, 80.0, 56.0, 17.0, 28.0, 101.0, 2.0, 101.0, 63.0, 101.0, 86.0, 101.0, 66.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 45.0, 9.0, 51.0, 101.0, 101.0, 33.0, 54.0, 93.0, 101.0, 101.0, 101.0, 2.0, 76.0, 101.0, 28.0, 26.0, 101.0, 101.0, 9.0, 42.0, 101.0, 36.0, 101.0, 15.0, 61.0, 101.0, 101.0, 101.0, 101.0, 78.0, 13.0, 48.0, 88.0, 101.0, 101.0, 45.0, 17.0, 101.0, 39.0, 5.0, 101.0, 101.0, 20.0, 101.0, 101.0, 101.0, 101.0, 34.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 5.0, 31.0, 96.0, 101.0, 101.0, 101.0, 98.0, 101.0, 17.0, 101.0, 101.0, 101.0, 69.0, 101.0, 8.0, 101.0, 101.0, 52.0, 88.0, 101.0, 79.0, 41.0, 83.0, 101.0, 47.0, 101.0, 101.0, 101.0, 101.0, 101.0, 66.0, 1.0, 82.0, 96.0, 101.0, 29.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 42.0, 30.0, 53.0, 101.0, 19.0, 8.0, 101.0, 30.0, 101.0, 19.0, 101.0, 101.0, 32.0, 46.0, 57.0, 101.0, 101.0, 101.0, 101.0, 29.0, 73.0, 101.0, 92.0, 84.0, 101.0, 101.0, 22.0, 101.0, 101.0, 101.0, 101.0, 101.0, 89.0, 101.0, 101.0, 101.0, 34.0, 67.0, 101.0, 24.0, 101.0, 89.0, 23.0, 101.0, 45.0, 101.0, 71.0, 101.0, 101.0, 101.0, 101.0, 65.0, 101.0, 101.0, 101.0, 101.0, 36.0, 53.0, 101.0, 101.0, 101.0, 28.0, 101.0, 65.0, 101.0, 101.0, 34.0, 101.0, 101.0, 68.0, 101.0, 85.0, 101.0, 101.0, 101.0, 9.0, 60.0, 42.0, 101.0, 97.0, 87.0, 101.0, 28.0, 82.0, 7.0, 32.0, 90.0, 54.0, 101.0, 36.0, 91.0, 41.0, 33.0, 101.0, 101.0, 41.0, 101.0, 53.0, 101.0, 101.0, 101.0, 12.0, 85.0, 101.0, 101.0, 101.0, 89.0, 30.0, 101.0, 2.0, 26.0, 53.0, 99.0, 101.0, 101.0, 67.0, 51.0, 101.0, 50.0, 26.0, 101.0, 101.0, 68.0, 101.0, 11.0, 101.0, 68.0, 11.0, 101.0, 101.0, 42.0, 101.0, 13.0, 101.0, 101.0, 20.0, 27.0, 58.0, 101.0, 101.0, 101.0, 101.0, 79.0, 81.0, 92.0, 101.0, 101.0, 101.0, 101.0, 101.0, 60.0, 101.0, 101.0, 76.0, 26.0, 33.0, 101.0, 47.0, 101.0, 85.0, 10.0, 32.0, 101.0, 101.0, 56.0, 101.0, 101.0, 28.0, 101.0, 99.0, 95.0, 101.0, 101.0, 101.0, 101.0, 30.0, 101.0, 101.0, 101.0, 101.0, 101.0, 80.0, 21.0, 101.0, 101.0, 101.0, 51.0, 101.0, 101.0, 101.0, 34.0, 59.0, 101.0, 101.0, 13.0, 36.0, 101.0, 101.0, 38.0, 95.0, 89.0, 101.0, 101.0, 101.0, 4.0, 101.0, 101.0, 36.0, 46.0, 101.0, 12.0, 101.0, 1.0, 26.0, 100.0, 89.0, 4.0, 60.0, 101.0, 89.0, 56.0, 67.0, 27.0, 89.0, 101.0, 44.0, 80.0, 57.0, 88.0, 4.0, 2.0, 101.0, 101.0, 101.0, 101.0, 101.0, 78.0, 10.0, 26.0, 101.0, 16.0, 55.0, 90.0, 101.0, 101.0, 6.0, 101.0, 40.0, 101.0, 101.0, 101.0, 101.0, 101.0, 67.0, 48.0, 31.0, 101.0, 101.0, 78.0, 61.0, 9.0, 101.0, 101.0, 101.0, 101.0, 101.0, 23.0, 101.0, 101.0, 88.0, 93.0, 101.0, 101.0, 101.0, 101.0, 101.0, 11.0, 4.0, 101.0, 67.0, 85.0, 101.0, 101.0, 101.0, 6.0, 25.0, 66.0, 101.0, 101.0, 101.0, 14.0, 101.0, 101.0, 2.0, 101.0, 8.0, 24.0, 19.0, 101.0, 101.0, 90.0, 58.0, 83.0, 101.0, 13.0, 42.0, 61.0, 61.0, 89.0, 101.0, 101.0, 97.0, 73.0, 89.0, 101.0, 38.0, 101.0, 69.0, 74.0, 101.0, 33.0, 84.0, 45.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 68.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 65.0, 101.0, 101.0, 38.0, 6.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 65.0, 83.0, 12.0, 33.0, 21.0, 8.0, 101.0, 8.0, 39.0, 8.0, 101.0, 26.0, 101.0, 2.0, 101.0, 101.0, 101.0, 101.0, 89.0, 101.0, 57.0, 101.0, 24.0, 101.0, 101.0, 72.0, 101.0, 101.0, 101.0, 4.0, 101.0, 10.0, 101.0, 101.0, 25.0, 101.0, 101.0, 43.0, 21.0, 101.0, 2.0, 34.0, 101.0, 97.0, 4.0, 62.0, 101.0, 7.0, 70.0, 101.0, 101.0, 31.0, 101.0, 101.0]



In [7]:

    
data = pd.read_csv(
    "python/test_alpenglow/test_data_4",
    sep=' ',
    header=None,
    names=['time', 'user', 'item', 'id', 'score', 'eval']
)
model = ag.OfflineModel()
model.fit(data)

def predict(model, user, item):
    rd = rs.RecDat()
    rd.user = user
    rd.item = item
    return model.prediction(rd)

errors = [(1 - predict(model.model, u, i))**2 for (u, i) in data[['user', 'item']].values]
rmse = np.sqrt(pd.Series(errors)).mean()



In [8]:

    
rmse









    Out[8]:





0.31307693145764515

offline rankings



In [1]:

    
%matplotlib inline
import alpenglow as ag
import alpenglow.Getter as rs
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import alpenglow.experiments
import alpenglow.evaluation



In [2]:

    
data = pd.read_csv(
    "python/test_alpenglow/test_data_4",
    sep=' ',
    header=None,
    names=['time', 'user', 'item', 'id', 'score', 'eval']
)
exp = ag.OfflineModel()
exp.fit(data)



In [3]:

    
preds = exp.recommend()



In [5]:

    
exp.model.write("model_save")



In [6]:

    
exp2 = ag.OfflineModel()



In [ ]:



In [10]:

    
print(list(preds['item']))









    



[94, 30, 166, 225, 300, 299, 442, 196, 455, 337, 372, 462, 250, 256, 427, 204, 38, 247, 429, 62, 36, 338, 496, 215, 177, 293, 255, 128, 40, 156, 165, 86, 375, 102, 483, 371, 444, 211, 383, 450, 266, 292, 108, 25, 120, 468, 251, 97, 497, 282, 414, 4, 168, 69, 491, 298, 197, 277, 452, 181, 479, 81, 325, 16, 421, 263, 330, 54, 161, 434, 295, 271, 99, 464, 351, 400, 395, 5, 458, 348, 245, 22, 216, 118, 105, 236, 221, 377, 440, 191, 425, 17, 436, 205, 127, 278, 265, 77, 80, 284, 197, 300, 16, 427, 86, 116, 165, 440, 29, 6, 491, 234, 204, 215, 403, 318, 356, 436, 139, 181, 99, 452, 296, 425, 58, 295, 468, 95, 108, 266, 281, 483, 271, 78, 351, 299, 112, 429, 419, 250, 12, 434, 478, 284, 192, 93, 127, 362, 254, 488, 205, 105, 330, 178, 142, 15, 445, 206, 302, 409, 497, 260, 390, 273, 481, 111, 450, 152, 72, 63, 294, 146, 19, 45, 232, 237, 65, 48, 276, 426, 7, 252, 380, 185, 268, 136, 228, 406, 144, 446, 381, 122, 459, 270, 35, 27, 349, 441, 49, 87, 166, 225, 30, 38, 196, 299, 177, 293, 442, 250, 247, 256, 483, 292, 337, 36, 25, 156, 444, 211, 450, 215, 108, 266, 414, 491, 204, 330, 375, 181, 351, 282, 251, 348, 62, 102, 86, 128, 271, 4, 165, 97, 40, 69, 96, 298, 146, 174, 16, 277, 99, 197, 120, 421, 168, 325, 454, 245, 425, 284, 161, 221, 236, 216, 383, 205, 54, 127, 22, 464, 233, 400, 81, 80, 118, 112, 78, 278, 191, 403, 479, 5, 436, 478, 318, 377, 356, 17, 234, 458, 6, 419, 265, 58, 95, 77, 289, 122, 139, 296, 30, 225, 166, 94, 299, 300, 372, 337, 98, 462, 196, 215, 36, 496, 250, 40, 338, 62, 455, 256, 429, 444, 204, 120, 255, 38, 247, 293, 211, 383, 292, 434, 165, 427, 128, 156, 25, 483, 263, 97, 177, 491, 81, 251, 86, 102, 181, 452, 414, 497, 400, 168, 375, 99, 4, 271, 395, 295, 450, 371, 277, 325, 108, 479, 266, 188, 351, 197, 421, 298, 464, 246, 195, 69, 162, 440, 494, 498, 53, 206, 330, 418, 410, 447, 425, 23, 281, 205, 127, 54, 192, 161, 5, 93, 348, 314, 12, 78, 105, 112, 94, 225, 30, 166, 300, 442, 299, 98, 337, 250, 62, 372, 496, 40, 215, 462, 204, 293, 255, 429, 427, 38, 36, 455, 247, 165, 128, 338, 25, 444, 156, 292, 86, 256, 266, 450, 177, 108, 371, 375, 251, 483, 97, 497, 211, 120, 383, 434, 491, 102, 468, 263, 81, 181, 400, 395, 99, 4, 452, 414, 271, 421, 325, 464, 277, 22, 245, 216, 298, 295, 351, 168, 118, 127, 191, 425, 197, 195, 479, 221, 236, 16, 330, 54, 206, 69, 278, 188, 282, 80, 161, 436, 478, 146, 440, 5, 162, 246, 377, 78, 204, 225, 166, 30, 196, 337, 247, 98, 62, 375, 250, 462, 102, 299, 497, 256, 40, 372, 429, 468, 156, 427, 266, 108, 450, 496, 395, 38, 455, 452, 251, 371, 177, 483, 282, 263, 255, 168, 215, 298, 245, 118, 16, 197, 325, 338, 216, 81, 22, 97, 69, 464, 54, 188, 191, 236, 161, 479, 278, 36, 80, 221, 25, 458, 211, 400, 5, 195, 277, 246, 162, 53, 292, 498, 410, 418, 414, 23, 494, 447, 116, 29, 284, 377, 434, 17, 77, 4, 440, 403, 289, 265, 120, 318, 488, 58, 6, 296, 397, 234, 94, 225, 30, 166, 442, 299, 98, 337, 204, 372, 250, 62, 247, 128, 40, 496, 455, 256, 215, 86, 429, 36, 383, 427, 38, 102, 338, 293, 375, 255, 156, 497, 177, 251, 483, 452, 371, 211, 266, 450, 108, 292, 263, 395, 25, 468, 444, 81, 120, 97, 400, 434, 168, 414, 282, 69, 298, 197, 421, 188, 277, 325, 479, 4, 54, 464, 16, 491, 161, 195, 246, 245, 162, 118, 5, 22, 498, 53, 216, 418, 181, 410, 236, 494, 458, 271, 221, 23, 447, 191, 278, 99, 80, 295, 436, 330, 377, 440, 116, 17, 225, 166, 94, 442, 300, 98, 337, 372, 215, 196, 462, 250, 36, 40, 496, 62, 338, 455, 429, 204, 256, 255, 444, 120, 38, 427, 247, 292, 165, 293, 483, 383, 434, 211, 97, 128, 414, 25, 156, 251, 452, 86, 81, 497, 177, 263, 491, 181, 102, 400, 298, 371, 450, 395, 325, 99, 271, 375, 266, 168, 295, 108, 4, 421, 188, 464, 330, 351, 197, 498, 246, 205, 162, 348, 479, 494, 53, 440, 195, 410, 418, 277, 281, 447, 23, 206, 425, 69, 192, 314, 127, 93, 468, 12, 105, 78, 245, 22, 112, 353, 166, 442, 300, 215, 337, 196, 250, 462, 372, 496, 36, 429, 165, 247, 455, 256, 338, 383, 128, 452, 86, 255, 497, 251, 434, 483, 292, 395, 427, 414, 38, 263, 444, 102, 120, 188, 293, 400, 97, 298, 25, 211, 246, 410, 162, 53, 494, 418, 371, 447, 23, 156, 181, 195, 450, 325, 491, 421, 375, 266, 108, 271, 464, 348, 99, 278, 295, 236, 330, 351, 221, 468, 168, 197, 205, 80, 245, 22, 118, 105, 216, 206, 281, 191, 101, 440, 488, 69, 479, 78, 93, 314, 192, 112, 96, 174, 12, 454, 353, 127, 166, 30, 94, 225, 462, 455, 337, 442, 98, 102, 177, 300, 36, 299, 247, 168, 211, 69, 282, 196, 204, 128, 215, 375, 479, 197, 86, 120, 429, 161, 156, 38, 5, 165, 54, 452, 277, 458, 444, 427, 483, 4, 263, 250, 16, 62, 17, 77, 81, 377, 265, 289, 496, 330, 497, 397, 440, 40, 132, 29, 414, 116, 293, 113, 205, 251, 371, 295, 357, 400, 6, 403, 454, 348, 234, 468, 233, 281, 318, 436, 356, 12, 271, 488, 58, 192, 188, 93, 95, 491, 434, 296, 314, 255, 298, 139, 146, 195, 362, 353, 166, 225, 94, 442, 98, 462, 337, 256, 36, 196, 215, 338, 250, 204, 429, 496, 247, 40, 427, 62, 38, 177, 383, 211, 102, 444, 255, 120, 483, 128, 156, 165, 293, 414, 86, 292, 375, 168, 452, 371, 69, 282, 251, 298, 97, 81, 181, 197, 497, 25, 4, 479, 263, 491, 434, 277, 450, 348, 330, 295, 266, 400, 351, 271, 108, 161, 54, 5, 395, 105, 188, 99, 325, 205, 468, 458, 440, 421, 16, 498, 162, 246, 96, 418, 494, 53, 78, 357, 174, 410, 454, 112, 464, 281, 23, 17, 447, 377, 139, 265, 30, 225, 94, 299, 337, 372, 215, 98, 62, 40, 250, 496, 204, 36, 165, 247, 128, 383, 429, 338, 86, 256, 263, 455, 434, 293, 444, 255, 497, 452, 81, 292, 102, 251, 120, 38, 211, 188, 25, 427, 156, 483, 375, 246, 498, 177, 162, 181, 53, 410, 97, 494, 418, 23, 447, 414, 195, 491, 168, 108, 266, 99, 271, 450, 371, 298, 277, 421, 479, 325, 4, 197, 351, 464, 468, 69, 278, 236, 54, 221, 206, 161, 295, 5, 80, 22, 78, 118, 458, 105, 245, 112, 216, 348, 191, 488, 440, 127, 425, 281, 166, 94, 300, 40, 98, 250, 496, 215, 372, 36, 462, 204, 338, 255, 429, 292, 444, 120, 293, 434, 165, 38, 97, 25, 455, 427, 128, 247, 156, 395, 256, 414, 497, 181, 483, 251, 86, 263, 450, 383, 211, 491, 81, 452, 266, 298, 108, 400, 99, 325, 188, 464, 246, 421, 498, 162, 375, 494, 271, 53, 351, 410, 447, 418, 371, 23, 4, 278, 221, 236, 102, 195, 78, 112, 80, 295, 177, 127, 22, 206, 168, 425, 245, 216, 419, 118, 281, 191, 440, 468, 192, 277, 93, 314, 105, 348, 12, 205, 353, 478, 414, 53, 348, 181, 447, 246, 292, 494, 78, 410, 174, 139, 351, 96, 278, 101, 236, 162, 23, 498, 369, 188, 357, 105, 395, 221, 80, 419, 418, 298, 112, 94, 30, 225, 166, 299, 98, 442, 196, 337, 250, 204, 372, 62, 462, 455, 496, 247, 40, 165, 429, 427, 215, 128, 256, 38, 36, 86, 293, 255, 338, 156, 177, 102, 375, 383, 483, 450, 371, 266, 497, 108, 292, 25, 251, 444, 211, 468, 97, 452, 120, 414, 81, 395, 298, 263, 434, 421, 491, 400, 325, 282, 168, 4, 197, 464, 277, 69, 181, 245, 22, 99, 216, 118, 16, 271, 479, 191, 236, 221, 295, 188, 54, 278, 330, 161, 80, 351, 195, 5, 436, 127, 162, 458, 246, 498, 440, 425, 348, 53, 205, 225, 166, 94, 299, 442, 300, 372, 98, 337, 462, 196, 215, 36, 455, 250, 338, 496, 256, 40, 62, 204, 429, 247, 444, 255, 38, 427, 383, 120, 211, 165, 128, 293, 483, 177, 102, 156, 292, 86, 434, 414, 452, 251, 97, 81, 25, 497, 263, 375, 168, 491, 371, 400, 181, 4, 450, 298, 69, 197, 271, 479, 295, 395, 325, 266, 99, 277, 108, 330, 282, 421, 188, 351, 464, 205, 440, 348, 54, 161, 468, 246, 498, 5, 195, 162, 281, 494, 53, 418, 410, 458, 447, 314, 192, 206, 23, 425, 105, 93, 12, 94, 165, 86, 102, 455, 225, 30, 462, 375, 196, 337, 177, 372, 427, 468, 69, 429, 156, 497, 371, 452, 38, 16, 197, 62, 266, 442, 168, 161, 450, 483, 54, 108, 250, 5, 458, 479, 251, 298, 299, 395, 293, 277, 211, 29, 81, 188, 116, 236, 284, 377, 263, 421, 221, 80, 17, 338, 77, 118, 245, 278, 289, 265, 436, 216, 191, 36, 357, 414, 22, 418, 498, 53, 4, 410, 403, 397, 162, 6, 23, 246, 318, 494, 325, 234, 447, 330, 356, 348, 454, 113, 464, 58, 139, 195, 101, 132, 233, 296, 488, 94, 225, 30, 166, 300, 299, 442, 337, 250, 62, 372, 455, 165, 247, 462, 427, 128, 429, 40, 496, 86, 38, 256, 293, 215, 255, 36, 156, 375, 102, 450, 371, 266, 177, 338, 108, 483, 383, 497, 468, 251, 25, 292, 97, 452, 211, 298, 395, 444, 414, 81, 282, 421, 120, 325, 263, 464, 197, 400, 245, 22, 434, 216, 16, 69, 118, 168, 4, 491, 236, 221, 277, 191, 278, 80, 188, 181, 479, 54, 99, 161, 271, 330, 295, 436, 5, 351, 195, 284, 498, 162, 458, 246, 348, 127, 53, 410, 418, 447, 494, 166, 442, 196, 337, 215, 40, 372, 496, 429, 462, 427, 256, 247, 165, 255, 38, 86, 483, 338, 128, 414, 292, 293, 371, 251, 497, 156, 97, 383, 266, 102, 25, 108, 177, 395, 81, 444, 120, 375, 468, 211, 434, 325, 421, 181, 188, 400, 464, 348, 278, 221, 236, 498, 330, 491, 263, 80, 245, 351, 162, 246, 22, 197, 53, 118, 410, 216, 494, 418, 295, 447, 99, 282, 271, 205, 105, 191, 23, 112, 78, 195, 454, 16, 69, 96, 168, 4, 101, 440, 419, 281, 174, 233, 127, 314, 369, 192, 425, 488, 436, 300, 442, 372, 250, 256, 196, 429, 462, 36, 38, 204, 338, 215, 496, 40, 255, 62, 156, 483, 414, 247, 292, 293, 177, 371, 450, 97, 120, 444, 165, 266, 128, 375, 211, 86, 25, 108, 102, 181, 251, 468, 325, 348, 383, 351, 497, 168, 282, 491, 4, 330, 421, 236, 221, 295, 464, 197, 105, 278, 80, 395, 452, 69, 112, 78, 99, 271, 205, 96, 434, 419, 245, 479, 440, 174, 81, 277, 139, 216, 369, 22, 118, 16, 454, 101, 191, 281, 314, 127, 425, 233, 357, 192, 436, 403, 400, 498, 162, 188, 12, 284, 16, 436, 455, 102, 127, 215, 99, 177, 112, 78, 434, 452, 318, 403, 58, 195, 188, 419, 425, 162, 254, 491, 296, 478, 400, 498, 447, 246, 122, 351, 95, 362, 356, 53, 410, 440, 314, 81, 494, 15, 4, 234, 418, 197, 6, 499, 181, 369, 105, 23, 1, 256, 129, 228, 270, 481, 290, 407, 361, 52, 273, 282, 336, 35, 90, 281, 485, 227, 305, 493, 232, 139, 7, 230, 50, 268, 404, 48, 171, 473, 231, 460, 79, 19, 408, 131, 206, 72, 459, 150, 186, 136, 183, 474, 381, 164, 210, 43, 426, 225, 166, 299, 300, 442, 196, 98, 250, 337, 496, 40, 215, 62, 372, 204, 462, 429, 36, 247, 427, 128, 86, 293, 38, 455, 251, 338, 483, 292, 497, 256, 25, 383, 444, 371, 414, 434, 97, 452, 395, 400, 211, 156, 102, 263, 450, 108, 266, 120, 375, 177, 491, 325, 468, 298, 421, 181, 188, 271, 464, 195, 295, 99, 245, 498, 22, 330, 246, 216, 118, 162, 494, 410, 53, 191, 418, 447, 278, 23, 236, 221, 351, 348, 80, 168, 197, 206, 205, 105, 440, 146, 4, 314, 436, 127, 281, 488, 425, 479, 69, 429, 98, 256, 483, 488, 421, 211, 206, 278, 116, 118, 101, 191, 414, 22, 245, 271, 29, 325, 298, 197, 254, 464, 338, 216, 93, 236, 80, 292, 221, 69, 479, 375, 353, 5, 25, 281, 205, 12, 54, 255, 161, 192, 436, 468, 314, 168, 19, 231, 458, 1, 114, 120, 79, 461, 227, 302, 493, 83, 378, 35, 323, 294, 104, 27, 276, 388, 125, 232, 347, 485, 228, 361, 49, 390, 0, 404, 481, 144, 268, 57, 63, 222, 290, 131, 136, 408, 405, 406, 155, 142, 441, 460, 444, 270, 237, 283, 152, 7, 134, 30, 225, 299, 442, 455, 337, 372, 38, 300, 36, 156, 250, 429, 483, 496, 444, 462, 215, 293, 196, 292, 371, 177, 450, 375, 25, 168, 266, 298, 62, 108, 295, 4, 464, 491, 421, 440, 204, 468, 205, 247, 479, 197, 271, 99, 181, 281, 69, 192, 245, 12, 93, 277, 216, 251, 348, 351, 436, 454, 403, 118, 22, 425, 318, 353, 191, 233, 127, 86, 128, 6, 165, 356, 234, 58, 95, 105, 362, 102, 146, 296, 478, 434, 236, 54, 221, 497, 17, 161, 16, 80, 278, 206, 132, 458, 377, 397, 5, 499, 29, 30, 94, 166, 300, 204, 337, 442, 462, 196, 247, 86, 98, 256, 102, 250, 40, 215, 452, 429, 455, 497, 36, 496, 375, 263, 81, 395, 251, 338, 177, 188, 483, 156, 282, 168, 427, 69, 211, 38, 468, 400, 197, 498, 479, 410, 246, 53, 418, 293, 195, 162, 494, 23, 266, 434, 371, 54, 161, 447, 298, 450, 108, 5, 16, 458, 421, 255, 414, 120, 277, 325, 97, 464, 116, 118, 29, 245, 236, 278, 191, 22, 216, 436, 377, 221, 77, 444, 80, 440, 17, 488, 292, 25, 265, 289, 4, 397, 205, 403, 281, 98, 300, 211, 338, 204, 337, 452, 81, 38, 197, 168, 295, 105, 479, 196, 161, 250, 444, 400, 16, 5, 146, 128, 96, 271, 54, 174, 458, 165, 496, 375, 29, 255, 116, 298, 351, 497, 440, 491, 277, 263, 289, 6, 17, 265, 403, 181, 488, 314, 77, 377, 362, 356, 188, 318, 101, 234, 120, 95, 397, 58, 139, 113, 15, 281, 122, 4, 12, 357, 93, 132, 369, 192, 436, 296, 284, 498, 353, 418, 468, 494, 53, 156, 308, 134, 410, 380, 200, 23, 157, 65, 273, 70, 162, 445, 441, 144, 406, 39, 246, 94, 166, 30, 225, 462, 442, 98, 372, 337, 299, 196, 204, 256, 455, 247, 165, 128, 102, 250, 86, 36, 62, 215, 429, 338, 177, 427, 496, 40, 375, 38, 452, 497, 211, 156, 483, 282, 293, 251, 69, 168, 371, 255, 81, 263, 197, 468, 444, 479, 266, 120, 450, 161, 108, 54, 400, 277, 5, 16, 395, 414, 458, 25, 4, 298, 188, 97, 434, 292, 421, 330, 325, 195, 377, 17, 116, 29, 77, 464, 265, 295, 440, 498, 246, 289, 162, 418, 271, 53, 436, 491, 205, 410, 494, 245, 118, 23, 397, 447, 348, 225, 94, 98, 300, 462, 196, 338, 36, 215, 256, 250, 496, 40, 62, 444, 38, 120, 429, 427, 177, 255, 211, 204, 247, 156, 292, 383, 102, 375, 168, 483, 25, 181, 165, 97, 434, 491, 414, 263, 277, 86, 479, 450, 69, 99, 251, 351, 266, 371, 452, 197, 108, 497, 81, 271, 298, 295, 282, 400, 54, 161, 5, 395, 325, 348, 78, 458, 112, 105, 440, 330, 425, 357, 188, 421, 17, 419, 464, 377, 246, 127, 77, 162, 265, 468, 205, 132, 96, 494, 281, 498, 397, 53, 418, 289, 206, 447, 410, 174, 23, 225, 166, 94, 442, 300, 98, 372, 337, 215, 462, 36, 196, 250, 40, 496, 338, 455, 256, 62, 429, 204, 255, 444, 120, 38, 247, 427, 383, 483, 211, 292, 165, 293, 414, 434, 97, 128, 156, 177, 452, 251, 25, 86, 102, 81, 497, 263, 491, 181, 298, 168, 400, 371, 375, 325, 450, 295, 271, 99, 4, 395, 330, 266, 197, 188, 421, 479, 108, 351, 205, 464, 69, 348, 440, 277, 498, 246, 281, 162, 494, 53, 418, 410, 192, 195, 314, 447, 23, 93, 206, 425, 282, 12, 105, 127, 78, 454, 353, 112, 468, 94, 30, 225, 166, 299, 98, 442, 196, 337, 372, 250, 204, 462, 455, 62, 247, 496, 429, 215, 40, 165, 427, 256, 36, 128, 38, 86, 255, 338, 293, 156, 177, 383, 102, 483, 375, 371, 497, 450, 292, 251, 266, 25, 444, 211, 108, 468, 97, 452, 120, 414, 81, 395, 263, 298, 434, 282, 400, 168, 491, 421, 325, 197, 69, 4, 464, 277, 181, 271, 479, 16, 245, 99, 22, 330, 295, 118, 216, 54, 188, 161, 191, 236, 221, 351, 278, 5, 80, 195, 348, 458, 436, 440, 162, 246, 205, 498, 105, 127, 53, 30, 225, 166, 299, 94, 196, 98, 337, 250, 215, 40, 372, 62, 36, 462, 255, 429, 338, 204, 293, 292, 444, 38, 427, 455, 25, 165, 434, 247, 120, 97, 128, 251, 256, 483, 491, 156, 211, 181, 497, 86, 414, 81, 263, 383, 400, 395, 450, 108, 266, 371, 99, 452, 271, 177, 325, 351, 298, 421, 375, 464, 295, 188, 102, 4, 246, 162, 195, 498, 494, 53, 127, 447, 410, 206, 425, 418, 22, 468, 23, 245, 216, 278, 221, 277, 118, 330, 78, 168, 236, 105, 112, 191, 348, 80, 281, 440, 205, 192, 419, 166, 225, 30, 300, 98, 256, 247, 455, 337, 165, 196, 462, 128, 86, 372, 299, 442, 102, 427, 383, 375, 429, 177, 250, 62, 38, 156, 371, 483, 468, 497, 36, 452, 266, 69, 338, 450, 40, 251, 108, 293, 215, 168, 496, 197, 211, 16, 255, 298, 161, 479, 54, 81, 414, 5, 395, 458, 421, 263, 277, 325, 245, 97, 118, 464, 330, 216, 29, 4, 22, 191, 400, 116, 436, 25, 236, 188, 377, 284, 17, 221, 77, 278, 80, 440, 289, 265, 403, 454, 120, 318, 6, 195, 205, 292, 356, 233, 348, 234, 397, 299, 30, 166, 94, 442, 300, 196, 250, 496, 337, 98, 215, 204, 372, 36, 429, 462, 255, 165, 338, 292, 293, 434, 25, 427, 444, 38, 97, 247, 128, 251, 497, 120, 86, 483, 395, 81, 455, 450, 156, 452, 414, 263, 400, 266, 108, 383, 256, 491, 325, 181, 371, 211, 298, 464, 421, 99, 188, 271, 195, 375, 498, 22, 246, 162, 245, 216, 410, 494, 53, 118, 447, 468, 102, 418, 295, 278, 191, 23, 221, 206, 236, 351, 127, 80, 425, 177, 4, 281, 314, 440, 192, 93, 205, 330, 78, 112, 12, 353, 488, 30, 300, 299, 442, 196, 337, 372, 455, 462, 38, 429, 496, 255, 256, 36, 62, 215, 293, 338, 204, 247, 40, 177, 292, 156, 444, 371, 128, 483, 165, 25, 211, 450, 375, 414, 86, 266, 108, 102, 120, 97, 181, 251, 491, 298, 383, 468, 351, 4, 497, 168, 282, 348, 271, 295, 105, 395, 99, 325, 277, 330, 434, 197, 69, 421, 81, 236, 221, 464, 278, 400, 80, 96, 78, 479, 112, 452, 16, 263, 174, 245, 419, 22, 216, 54, 161, 425, 127, 118, 440, 191, 139, 205, 5, 146, 357, 436, 162, 188, 458, 94, 166, 225, 300, 98, 299, 337, 442, 427, 247, 250, 256, 372, 462, 165, 128, 38, 62, 429, 86, 177, 156, 375, 293, 102, 40, 496, 255, 371, 450, 36, 338, 483, 215, 108, 468, 383, 497, 282, 251, 298, 292, 25, 211, 97, 414, 452, 69, 197, 16, 444, 168, 421, 395, 4, 325, 120, 277, 464, 245, 81, 236, 479, 54, 161, 216, 118, 22, 221, 278, 80, 191, 5, 263, 330, 458, 491, 284, 400, 348, 436, 295, 181, 351, 99, 377, 105, 29, 271, 17, 403, 188, 440, 434, 205, 454, 116, 318, 265, 77, 166, 299, 462, 98, 300, 36, 256, 215, 455, 196, 496, 250, 40, 444, 120, 383, 211, 62, 177, 247, 38, 102, 204, 168, 427, 255, 414, 483, 292, 156, 128, 293, 434, 263, 181, 165, 452, 375, 479, 69, 81, 4, 491, 86, 97, 197, 277, 251, 400, 25, 295, 282, 271, 497, 351, 348, 188, 161, 5, 54, 330, 298, 99, 440, 246, 395, 162, 494, 458, 418, 53, 105, 498, 410, 23, 371, 357, 205, 447, 325, 96, 281, 174, 139, 78, 17, 77, 112, 265, 377, 192, 93, 289, 132, 314, 12, 101, 397, 206, 419, 215, 444, 196, 177, 496, 168, 250, 40, 383, 429, 427, 414, 483, 102, 62, 4, 255, 479, 375, 97, 69, 247, 292, 204, 197, 293, 277, 181, 282, 491, 434, 295, 440, 128, 161, 5, 54, 458, 452, 263, 205, 330, 351, 99, 348, 25, 325, 271, 281, 357, 192, 17, 12, 78, 314, 93, 139, 112, 77, 265, 377, 105, 132, 289, 86, 397, 419, 450, 165, 425, 371, 81, 454, 403, 353, 113, 6, 369, 464, 96, 101, 233, 497, 251, 234, 174, 266, 356, 16, 318, 29, 127, 400, 206, 436, 116, 108, 95, 58, 478, 337, 255, 483, 166, 225, 298, 497, 236, 221, 245, 80, 216, 22, 118, 25, 278, 30, 69, 191, 197, 421, 251, 54, 161, 464, 277, 292, 395, 4, 458, 325, 5, 383, 436, 97, 377, 403, 29, 168, 318, 414, 479, 234, 372, 356, 116, 58, 6, 17, 105, 296, 77, 95, 265, 289, 462, 139, 357, 362, 348, 112, 330, 397, 211, 419, 351, 78, 127, 454, 96, 113, 254, 442, 478, 233, 132, 146, 174, 122, 290, 268, 252, 228, 1, 369, 144, 183, 15, 390, 90, 445, 388, 499, 230, 484, 425, 72, 273, 237, 70, 338, 40, 337, 99, 62, 256, 434, 4, 425, 120, 205, 454, 127, 206, 233, 97, 108, 78, 112, 478, 450, 362, 156, 419, 263, 277, 266, 102, 383, 69, 282, 122, 314, 284, 468, 497, 15, 369, 132, 403, 488, 268, 356, 101, 139, 308, 204, 237, 35, 65, 445, 144, 484, 48, 361, 157, 353, 318, 437, 265, 323, 347, 404, 200, 302, 95, 0, 70, 231, 446, 294, 113, 407, 16, 7, 473, 129, 43, 45, 485, 136, 298, 134, 409, 499, 59, 39, 344, 104, 145, 331, 276, 406, 290, 283, 252, 125, 130, 1, 405, 94, 30, 225, 166, 299, 196, 442, 337, 204, 250, 372, 455, 462, 62, 427, 247, 429, 165, 496, 256, 38, 40, 128, 215, 36, 293, 255, 86, 338, 156, 177, 375, 102, 371, 450, 483, 266, 383, 108, 292, 25, 497, 251, 444, 468, 211, 97, 414, 120, 452, 298, 395, 282, 81, 263, 491, 421, 4, 168, 325, 434, 197, 69, 400, 181, 277, 464, 16, 245, 22, 216, 236, 99, 118, 479, 221, 271, 278, 54, 351, 191, 80, 295, 330, 161, 188, 5, 348, 105, 458, 436, 127, 162, 246, 440, 195, 498, 284, 425, 377, 166, 225, 299, 372, 98, 462, 196, 256, 36, 215, 455, 250, 40, 62, 496, 204, 247, 429, 383, 128, 120, 165, 38, 444, 156, 211, 102, 293, 427, 177, 255, 375, 86, 292, 168, 483, 414, 263, 97, 434, 497, 452, 395, 25, 4, 479, 251, 181, 277, 81, 298, 69, 197, 450, 266, 108, 188, 491, 400, 282, 54, 371, 325, 161, 246, 162, 5, 494, 53, 418, 498, 99, 421, 410, 458, 23, 351, 447, 271, 468, 440, 464, 295, 357, 236, 78, 221, 112, 348, 278, 17, 80, 377, 195, 77, 105, 139, 281, 419, 265, 30, 94, 166, 299, 300, 442, 98, 196, 337, 250, 372, 40, 496, 215, 462, 204, 36, 429, 165, 455, 255, 247, 427, 338, 38, 128, 293, 256, 86, 292, 444, 483, 156, 25, 383, 251, 497, 97, 120, 450, 434, 371, 102, 211, 177, 266, 108, 375, 81, 452, 414, 395, 263, 400, 491, 298, 468, 325, 181, 421, 464, 99, 271, 188, 4, 168, 295, 22, 245, 195, 216, 118, 197, 498, 351, 246, 277, 162, 191, 330, 236, 53, 494, 278, 410, 221, 418, 447, 479, 23, 69, 127, 80, 425, 206, 440, 282, 205, 348, 16, 225, 166, 94, 299, 442, 300, 372, 98, 337, 462, 196, 215, 36, 455, 250, 256, 338, 496, 40, 62, 204, 429, 247, 383, 444, 38, 120, 255, 427, 165, 211, 128, 177, 293, 483, 102, 156, 86, 292, 452, 434, 414, 97, 375, 251, 263, 81, 497, 168, 25, 491, 400, 181, 371, 4, 298, 69, 479, 450, 197, 395, 266, 277, 271, 325, 295, 99, 108, 282, 188, 330, 421, 440, 464, 351, 205, 161, 54, 246, 498, 5, 348, 162, 494, 53, 468, 418, 195, 410, 281, 447, 23, 458, 192, 206, 425, 314, 93, 12, 105, 36, 38, 97, 351, 400, 455, 427, 4, 295, 81, 414, 425, 251, 127, 156, 78, 112, 483, 105, 395, 419, 478, 277, 177, 246, 96, 162, 174, 146, 195, 108, 325, 447, 494, 348, 464, 188, 498, 53, 281, 410, 450, 330, 23, 192, 418, 93, 266, 353, 314, 497, 440, 12, 383, 421, 205, 132, 371, 247, 168, 452, 256, 397, 488, 22, 113, 265, 357, 362, 481, 122, 19, 63, 17, 77, 323, 268, 232, 136, 237, 408, 221, 302, 499, 446, 409, 48, 87, 0, 473, 347, 361, 7, 479, 276, 142, 426, 65, 405, 260, 372, 337, 462, 250, 98, 300, 255, 196, 211, 491, 181, 429, 99, 25, 263, 271, 295, 400, 455, 483, 81, 325, 293, 281, 206, 192, 4, 38, 440, 93, 12, 351, 425, 452, 156, 383, 464, 127, 251, 246, 195, 494, 479, 78, 162, 330, 421, 188, 498, 112, 53, 410, 478, 418, 447, 23, 298, 395, 419, 277, 497, 122, 488, 15, 348, 146, 132, 427, 454, 105, 233, 369, 174, 481, 499, 7, 408, 460, 96, 380, 473, 441, 301, 150, 52, 19, 273, 63, 171, 232, 361, 48, 349, 1, 406, 185, 260, 65, 134, 393, 30, 225, 94, 442, 299, 372, 462, 98, 300, 337, 455, 196, 256, 36, 215, 338, 250, 496, 40, 62, 204, 247, 429, 383, 444, 177, 211, 38, 102, 128, 120, 427, 165, 255, 293, 156, 483, 86, 168, 375, 452, 263, 292, 434, 81, 414, 69, 251, 497, 479, 4, 97, 25, 197, 282, 277, 491, 400, 181, 371, 161, 54, 5, 271, 295, 298, 450, 395, 458, 330, 99, 266, 188, 108, 325, 440, 351, 348, 205, 421, 17, 246, 77, 377, 162, 468, 498, 16, 265, 494, 53, 418, 195, 410, 281, 289, 464, 23, 132, 397, 98, 128, 247, 338, 282, 383, 69, 468, 429, 293, 299, 479, 371, 62, 161, 54, 277, 16, 458, 5, 36, 120, 497, 255, 298, 97, 377, 17, 444, 77, 414, 325, 421, 265, 289, 40, 29, 397, 464, 452, 284, 116, 357, 132, 496, 236, 251, 245, 113, 25, 118, 221, 216, 80, 292, 22, 191, 215, 263, 278, 330, 395, 454, 205, 281, 295, 233, 362, 192, 12, 348, 122, 15, 254, 314, 488, 93, 78, 112, 425, 419, 105, 369, 127, 101, 491, 273, 99, 499, 478, 441, 252, 178, 408, 72, 380, 445, 481, 268, 260, 442, 372, 196, 62, 383, 165, 338, 86, 427, 128, 81, 251, 255, 414, 177, 211, 371, 298, 444, 292, 400, 263, 188, 120, 434, 293, 395, 330, 348, 156, 498, 282, 25, 375, 97, 69, 197, 246, 53, 410, 162, 418, 494, 181, 23, 168, 447, 295, 205, 450, 271, 491, 325, 195, 421, 105, 468, 351, 266, 454, 479, 108, 233, 96, 464, 16, 174, 99, 440, 101, 488, 278, 281, 236, 146, 314, 221, 161, 192, 93, 29, 12, 5, 277, 245, 4, 54, 116, 118, 80, 206, 353, 369, 139, 436, 22, 191, 78, 216, 403, 166, 30, 225, 94, 372, 442, 299, 337, 98, 455, 300, 256, 36, 338, 215, 196, 383, 247, 177, 211, 250, 496, 429, 444, 102, 204, 40, 62, 120, 38, 128, 168, 427, 69, 483, 263, 165, 293, 452, 255, 156, 86, 375, 81, 479, 282, 277, 4, 197, 434, 251, 491, 400, 161, 5, 54, 414, 292, 497, 181, 458, 271, 330, 295, 25, 371, 97, 99, 17, 77, 377, 348, 265, 440, 205, 289, 188, 397, 132, 16, 351, 113, 195, 105, 116, 146, 29, 281, 246, 454, 162, 494, 418, 357, 53, 498, 206, 395, 410, 23, 225, 94, 372, 337, 36, 98, 338, 455, 496, 40, 250, 62, 383, 429, 204, 444, 120, 247, 211, 434, 452, 102, 255, 263, 38, 292, 81, 177, 165, 128, 483, 414, 181, 293, 427, 168, 251, 400, 86, 188, 497, 491, 156, 97, 25, 395, 246, 498, 162, 479, 271, 69, 494, 53, 418, 410, 4, 23, 197, 447, 298, 351, 99, 295, 348, 375, 277, 330, 195, 205, 105, 440, 5, 161, 282, 54, 371, 325, 281, 78, 96, 206, 174, 112, 101, 357, 192, 458, 93, 421, 12, 314, 419, 425, 139, 488, 17, 353, 265, 464, 30, 98, 300, 299, 455, 196, 462, 337, 256, 250, 247, 204, 38, 36, 177, 215, 338, 165, 62, 496, 102, 86, 255, 293, 40, 483, 156, 383, 375, 211, 292, 414, 444, 251, 282, 450, 266, 108, 25, 497, 468, 168, 69, 298, 120, 452, 81, 197, 97, 181, 348, 4, 330, 491, 395, 479, 277, 351, 16, 105, 400, 263, 295, 161, 54, 271, 5, 325, 421, 236, 221, 96, 458, 188, 278, 80, 174, 434, 464, 99, 162, 205, 245, 440, 246, 357, 498, 494, 139, 418, 53, 377, 436, 410, 17, 118, 146, 78, 447, 454, 30, 166, 225, 94, 442, 372, 98, 337, 300, 215, 36, 462, 496, 196, 40, 455, 250, 256, 444, 120, 62, 429, 255, 38, 211, 427, 293, 204, 434, 292, 97, 156, 483, 383, 177, 247, 491, 25, 414, 168, 181, 4, 263, 99, 452, 81, 271, 128, 102, 295, 251, 165, 400, 375, 325, 479, 277, 497, 450, 197, 330, 440, 86, 371, 205, 69, 298, 351, 281, 425, 464, 266, 421, 108, 192, 206, 127, 314, 93, 12, 353, 348, 54, 161, 5, 478, 195, 454, 17, 132, 395, 78, 265, 146, 233, 77, 458, 488, 112, 397, 299, 442, 372, 300, 98, 337, 462, 196, 215, 455, 256, 36, 338, 250, 496, 40, 204, 62, 429, 247, 383, 38, 427, 165, 128, 444, 211, 120, 102, 177, 255, 483, 86, 156, 414, 293, 292, 452, 375, 168, 251, 497, 81, 263, 434, 97, 25, 298, 371, 181, 69, 479, 197, 400, 395, 282, 4, 491, 188, 277, 450, 348, 295, 325, 330, 271, 266, 246, 498, 108, 351, 162, 494, 53, 418, 161, 410, 54, 99, 5, 421, 23, 447, 440, 205, 105, 468, 458, 464, 281, 195, 96, 357, 16, 78, 236, 174, 112, 17, 192, 196, 156, 211, 442, 377, 77, 17, 289, 263, 427, 38, 265, 4, 483, 357, 397, 429, 113, 132, 371, 36, 266, 6, 188, 436, 403, 234, 284, 440, 318, 81, 108, 62, 298, 356, 395, 251, 58, 450, 139, 418, 296, 53, 23, 95, 410, 454, 488, 494, 246, 162, 498, 233, 101, 421, 195, 447, 330, 254, 205, 236, 120, 362, 293, 118, 80, 15, 281, 348, 12, 191, 221, 369, 245, 273, 122, 178, 445, 294, 72, 142, 400, 186, 1, 144, 252, 48, 390, 409, 406, 380, 441, 134, 231, 35, 228, 260, 270, 152, 185, 299, 372, 247, 337, 102, 128, 36, 62, 497, 40, 250, 496, 251, 98, 400, 455, 395, 498, 410, 53, 418, 246, 23, 494, 162, 195, 447, 69, 338, 483, 282, 434, 168, 197, 177, 375, 479, 161, 5, 54, 414, 458, 116, 29, 16, 298, 330, 348, 205, 454, 421, 371, 468, 233, 77, 120, 440, 277, 289, 377, 17, 265, 93, 281, 436, 254, 12, 271, 357, 314, 192, 353, 325, 397, 206, 278, 6, 146, 174, 113, 118, 295, 444, 132, 105, 139, 403, 96, 318, 58, 236, 1, 231, 95, 356, 191, 369, 19, 114, 294, 300, 455, 62, 211, 196, 434, 255, 292, 383, 38, 4, 263, 491, 181, 99, 314, 25, 293, 277, 205, 197, 271, 452, 427, 177, 298, 353, 421, 204, 206, 69, 425, 81, 400, 102, 127, 5, 497, 132, 54, 330, 17, 458, 161, 397, 77, 78, 351, 128, 122, 403, 265, 436, 6, 15, 112, 251, 289, 246, 478, 318, 377, 488, 494, 113, 356, 95, 195, 165, 162, 234, 139, 419, 58, 454, 247, 296, 53, 233, 395, 188, 418, 357, 369, 498, 450, 410, 23, 447, 499, 481, 29, 254, 408, 380, 7, 273, 441, 301, 473, 94, 225, 30, 166, 300, 299, 442, 98, 337, 250, 62, 496, 372, 40, 204, 215, 462, 293, 255, 429, 36, 38, 427, 455, 165, 247, 128, 338, 25, 156, 444, 86, 292, 256, 450, 266, 108, 177, 97, 497, 251, 375, 371, 483, 120, 383, 434, 211, 491, 102, 263, 468, 81, 400, 395, 452, 99, 181, 4, 421, 325, 271, 464, 414, 277, 22, 216, 245, 118, 298, 168, 295, 127, 191, 425, 197, 351, 195, 479, 206, 16, 54, 188, 221, 236, 69, 330, 278, 436, 161, 282, 478, 80, 440, 5, 377, 162, 246, 146, 281, 166, 225, 94, 299, 442, 300, 372, 98, 337, 462, 196, 215, 36, 455, 338, 250, 256, 496, 40, 62, 204, 429, 247, 444, 38, 120, 383, 255, 427, 211, 177, 128, 165, 293, 156, 483, 102, 292, 86, 434, 414, 97, 375, 452, 168, 25, 251, 263, 81, 497, 491, 4, 181, 371, 400, 69, 479, 450, 197, 277, 298, 271, 266, 295, 99, 108, 325, 282, 395, 330, 421, 351, 188, 54, 161, 440, 5, 464, 205, 348, 468, 458, 281, 246, 498, 162, 195, 494, 53, 425, 418, 410, 192, 206, 17, 314, 105, 447, 93, 23, 30, 166, 442, 300, 98, 196, 337, 250, 215, 372, 496, 40, 36, 62, 462, 429, 455, 204, 427, 338, 38, 292, 256, 293, 247, 444, 165, 483, 414, 25, 97, 120, 128, 251, 156, 371, 86, 211, 434, 450, 177, 497, 181, 491, 266, 298, 81, 383, 108, 395, 452, 400, 325, 102, 375, 271, 351, 295, 99, 263, 421, 464, 330, 468, 348, 4, 188, 105, 278, 221, 236, 245, 168, 498, 22, 246, 162, 80, 216, 205, 494, 118, 53, 410, 447, 418, 197, 96, 78, 127, 191, 112, 425, 440, 174, 23, 206, 195, 281, 314, 94, 30, 166, 225, 300, 299, 442, 196, 337, 372, 455, 250, 462, 204, 427, 256, 38, 62, 247, 429, 36, 496, 338, 215, 293, 40, 255, 128, 165, 177, 156, 86, 375, 102, 483, 371, 444, 450, 383, 266, 292, 211, 108, 25, 120, 97, 251, 468, 497, 414, 282, 4, 168, 298, 491, 69, 452, 197, 277, 181, 81, 325, 421, 263, 434, 479, 395, 16, 464, 99, 271, 400, 295, 330, 54, 161, 351, 245, 5, 22, 216, 348, 236, 118, 458, 221, 191, 105, 278, 80, 440, 425, 436, 127, 377, 17, 205, 77, 265, 284, 30, 225, 94, 300, 442, 98, 196, 337, 250, 372, 496, 215, 462, 40, 62, 204, 36, 429, 455, 427, 38, 338, 256, 293, 165, 292, 128, 483, 444, 86, 25, 156, 251, 371, 177, 414, 97, 211, 120, 383, 497, 450, 266, 108, 102, 434, 81, 375, 491, 452, 181, 298, 395, 400, 263, 468, 325, 271, 421, 99, 295, 464, 351, 330, 4, 348, 188, 168, 245, 22, 197, 216, 236, 221, 278, 118, 105, 498, 277, 246, 162, 191, 80, 195, 494, 205, 69, 53, 127, 282, 410, 447, 418, 425, 479, 440, 23, 206, 96, 78, 30, 225, 300, 299, 442, 337, 372, 196, 462, 455, 256, 250, 204, 247, 36, 62, 215, 429, 427, 338, 496, 38, 40, 128, 165, 177, 293, 255, 86, 102, 383, 156, 375, 483, 211, 444, 292, 371, 120, 414, 497, 251, 450, 25, 266, 108, 168, 97, 282, 452, 468, 298, 69, 81, 4, 263, 197, 395, 181, 277, 479, 491, 434, 400, 325, 54, 161, 421, 351, 348, 330, 16, 295, 5, 271, 188, 464, 458, 99, 105, 236, 221, 278, 80, 162, 246, 498, 440, 245, 494, 53, 418, 410, 377, 205, 17, 447, 118, 22, 96, 30, 225, 94, 299, 442, 300, 98, 372, 337, 462, 196, 455, 215, 36, 250, 256, 338, 496, 40, 62, 204, 429, 247, 38, 427, 444, 383, 255, 128, 177, 211, 165, 293, 120, 102, 156, 483, 86, 292, 375, 25, 251, 168, 434, 452, 414, 263, 97, 497, 81, 371, 491, 4, 181, 400, 69, 450, 479, 277, 197, 266, 108, 282, 271, 295, 395, 99, 298, 325, 330, 54, 161, 351, 5, 421, 468, 188, 440, 464, 458, 348, 205, 246, 162, 498, 195, 16, 494, 53, 105, 17, 281, 418, 425, 377, 410, 77, 265, 447, 23, 94, 98, 442, 337, 372, 462, 256, 250, 215, 36, 62, 247, 429, 40, 496, 165, 427, 338, 128, 86, 38, 383, 102, 483, 255, 177, 156, 375, 452, 293, 414, 298, 497, 371, 211, 450, 251, 120, 292, 266, 97, 444, 282, 108, 81, 468, 395, 168, 25, 69, 263, 325, 434, 421, 188, 479, 400, 348, 4, 464, 330, 181, 16, 498, 236, 277, 221, 278, 162, 246, 418, 53, 205, 410, 161, 491, 494, 295, 80, 54, 447, 245, 23, 351, 5, 440, 118, 105, 99, 22, 271, 216, 454, 191, 458, 281, 195, 101, 78, 112, 429, 211, 271, 462, 371, 295, 414, 483, 206, 464, 325, 375, 105, 22, 421, 263, 96, 221, 400, 216, 132, 146, 174, 251, 236, 245, 80, 281, 168, 191, 397, 468, 298, 278, 192, 440, 118, 265, 377, 17, 256, 357, 113, 348, 77, 247, 353, 128, 395, 330, 403, 93, 362, 436, 314, 12, 356, 479, 234, 318, 289, 95, 139, 296, 6, 284, 481, 87, 268, 122, 323, 409, 408, 63, 142, 499, 19, 446, 237, 441, 473, 48, 45, 0, 232, 136, 390, 228, 150, 65, 111, 276, 260, 58, 331, 405, 90, 344, 178, 461]



In [5]:

    
preds2 = exp.recommend(users=[1,2], exclude_known=False)



In [6]:

    
preds2









    Out[6]:







  
    
      
      user
      item
      rank
    
  
  
    
      0
      1
      94
      1
    
    
      1
      1
      30
      2
    
    
      2
      1
      166
      3
    
    
      3
      1
      225
      4
    
    
      4
      1
      98
      5
    
    
      5
      1
      300
      6
    
    
      6
      1
      299
      7
    
    
      7
      1
      442
      8
    
    
      8
      1
      196
      9
    
    
      9
      1
      455
      10
    
    
      10
      1
      337
      11
    
    
      11
      1
      372
      12
    
    
      12
      1
      462
      13
    
    
      13
      1
      250
      14
    
    
      14
      1
      256
      15
    
    
      15
      1
      427
      16
    
    
      16
      1
      204
      17
    
    
      17
      1
      38
      18
    
    
      18
      1
      247
      19
    
    
      19
      1
      429
      20
    
    
      20
      1
      62
      21
    
    
      21
      1
      36
      22
    
    
      22
      1
      338
      23
    
    
      23
      1
      496
      24
    
    
      24
      1
      215
      25
    
    
      25
      1
      177
      26
    
    
      26
      1
      293
      27
    
    
      27
      1
      255
      28
    
    
      28
      1
      128
      29
    
    
      29
      1
      40
      30
    
    
      ...
      ...
      ...
      ...
    
    
      170
      2
      36
      71
    
    
      171
      2
      156
      72
    
    
      172
      2
      488
      73
    
    
      173
      2
      485
      74
    
    
      174
      2
      102
      75
    
    
      175
      2
      177
      76
    
    
      176
      2
      62
      77
    
    
      177
      2
      362
      78
    
    
      178
      2
      16
      79
    
    
      179
      2
      319
      80
    
    
      180
      2
      444
      81
    
    
      181
      2
      38
      82
    
    
      182
      2
      122
      83
    
    
      183
      2
      336
      84
    
    
      184
      2
      125
      85
    
    
      185
      2
      79
      86
    
    
      186
      2
      113
      87
    
    
      187
      2
      434
      88
    
    
      188
      2
      318
      89
    
    
      189
      2
      296
      90
    
    
      190
      2
      251
      91
    
    
      191
      2
      409
      92
    
    
      192
      2
      380
      93
    
    
      193
      2
      270
      94
    
    
      194
      2
      420
      95
    
    
      195
      2
      493
      96
    
    
      196
      2
      1
      97
    
    
      197
      2
      97
      98
    
    
      198
      2
      99
      99
    
    
      199
      2
      30
      100
    
  

200 rows × 3 columns



In [7]:

    
len(exp.recommend(exclude_known=True)['item'])









    Out[7]:





6431



In [8]:

    
(exp.recommend(exclude_known=True)['item'] == exp.recommend(exclude_known=False)['item']).value_counts()









    



---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-c269627c5c46> in <module>()
----> 1 (exp.recommend(exclude_known=True)['item'] == exp.recommend(exclude_known=False)['item']).value_counts()

/home/kd/anaconda3/envs/py3-proba2/lib/python3.5/site-packages/pandas/core/ops.py in wrapper(self, other, axis)
    816             if not self._indexed_same(other):
    817                 msg = 'Can only compare identically-labeled Series objects'
--> 818                 raise ValueError(msg)
    819             return self._constructor(na_op(self.values, other.values),
    820                                      index=self.index, name=name)

ValueError: Can only compare identically-labeled Series objects



In [9]:

    
preds.join(data.set_index(['user','item']), on=['user','item'], how='inner').groupby('user')['eval'].count().mean()









    Out[9]:





nan



In [10]:

    
data









    Out[10]:







  
    
      
      time
      user
      item
      id
      score
      eval
    
  
  
    
      0
      100
      23
      30
      0
      1
      1
    
    
      1
      105
      23
      337
      1
      1
      1
    
    
      2
      110
      23
      372
      2
      1
      1
    
    
      3
      115
      23
      120
      3
      1
      1
    
    
      4
      120
      23
      168
      4
      1
      1
    
    
      5
      125
      34
      204
      5
      1
      1
    
    
      6
      130
      23
      468
      6
      1
      1
    
    
      7
      135
      23
      256
      7
      1
      1
    
    
      8
      140
      23
      225
      8
      1
      1
    
    
      9
      145
      23
      462
      9
      1
      1
    
    
      10
      150
      23
      455
      10
      1
      1
    
    
      11
      155
      23
      166
      11
      1
      1
    
    
      12
      160
      23
      128
      12
      1
      1
    
    
      13
      165
      23
      4
      13
      1
      1
    
    
      14
      170
      74
      166
      14
      1
      1
    
    
      15
      175
      70
      442
      15
      1
      1
    
    
      16
      180
      34
      383
      16
      1
      1
    
    
      17
      185
      34
      225
      17
      1
      1
    
    
      18
      190
      4
      98
      18
      1
      1
    
    
      19
      195
      23
      452
      19
      1
      1
    
    
      20
      200
      23
      293
      20
      1
      1
    
    
      21
      205
      23
      260
      21
      1
      1
    
    
      22
      210
      23
      6
      22
      1
      1
    
    
      23
      215
      23
      161
      23
      1
      1
    
    
      24
      220
      23
      234
      24
      1
      1
    
    
      25
      225
      23
      94
      25
      1
      1
    
    
      26
      230
      23
      375
      26
      1
      1
    
    
      27
      235
      32
      371
      27
      1
      1
    
    
      28
      240
      23
      204
      28
      1
      1
    
    
      29
      245
      23
      473
      29
      1
      1
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      970
      4950
      99
      108
      970
      1
      1
    
    
      971
      4955
      32
      325
      971
      1
      1
    
    
      972
      4960
      55
      120
      972
      1
      1
    
    
      973
      4965
      84
      263
      973
      1
      1
    
    
      974
      4970
      71
      496
      974
      1
      1
    
    
      975
      4975
      91
      94
      975
      1
      1
    
    
      976
      4980
      39
      205
      976
      1
      1
    
    
      977
      4985
      82
      30
      977
      1
      1
    
    
      978
      4990
      99
      177
      978
      1
      1
    
    
      979
      4995
      99
      277
      979
      1
      1
    
    
      980
      5000
      38
      62
      980
      1
      1
    
    
      981
      5005
      84
      101
      981
      1
      1
    
    
      982
      5010
      51
      166
      982
      1
      1
    
    
      983
      5015
      28
      98
      983
      1
      1
    
    
      984
      5020
      17
      204
      984
      1
      1
    
    
      985
      5025
      83
      16
      985
      1
      1
    
    
      986
      5030
      23
      22
      986
      1
      1
    
    
      987
      5035
      80
      94
      987
      1
      1
    
    
      988
      5040
      8
      196
      988
      1
      1
    
    
      989
      5045
      70
      299
      989
      1
      1
    
    
      990
      5050
      52
      30
      990
      1
      1
    
    
      991
      5055
      55
      421
      991
      1
      1
    
    
      992
      5060
      34
      395
      992
      1
      1
    
    
      993
      5065
      53
      337
      993
      1
      1
    
    
      994
      5070
      38
      128
      994
      1
      1
    
    
      995
      5075
      86
      168
      995
      1
      1
    
    
      996
      5080
      40
      383
      996
      1
      1
    
    
      997
      5085
      83
      29
      997
      1
      1
    
    
      998
      5090
      73
      234
      998
      1
      1
    
    
      999
      5095
      23
      390
      999
      1
      1
    
  

1000 rows × 6 columns



In [2]:

    
r=rs.FactorModelReader()



In [3]:

    
uif = r.read("model_save", 10)



In [6]:

    
users = []
user_factors = []
for f in uif.user_factors:
    users.append(f.entity)
    user_factors.append(f.factors)
items = []
item_factors = []
for f in uif.item_factors:
    items.append(f.entity)
    item_factors.append(f.factors)



In [13]:

    
user_df = pd.DataFrame.from_records(user_factors, columns=range(1,10+1))
user_df['user']=users
user_df.set_index('user',inplace=True)

item_df = pd.DataFrame.from_records(item_factors, columns=range(1,10+1))
item_df['item']=items
item_df.set_index('item',inplace=True)



In [15]:

    
item_df









    Out[15]:







  
    
      
      1
      2
      3
      4
      5
      6
      7
      8
      9
      10
    
    
      item
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      0
      0.093610
      0.092002
      0.041882
      0.109370
      -0.103590
      0.065632
      -0.113365
      0.089075
      0.110280
      0.071719
    
    
      1
      0.093194
      0.096220
      0.050682
      0.096436
      -0.101917
      0.079697
      -0.117589
      0.086036
      0.103890
      0.073094
    
    
      4
      0.096395
      -0.069351
      0.051472
      0.011227
      -0.426102
      -0.041697
      0.027440
      -0.113184
      0.110873
      0.242209
    
    
      5
      0.001814
      0.182096
      0.018791
      0.111336
      -0.190668
      -0.003389
      -0.008853
      -0.109414
      0.011959
      0.353851
    
    
      6
      0.049667
      0.072248
      0.011327
      0.111306
      -0.134521
      0.069018
      -0.125834
      0.001380
      0.095448
      0.143113
    
    
      7
      0.083668
      0.101772
      0.054034
      0.096514
      -0.100438
      0.072806
      -0.116940
      0.077727
      0.120039
      0.069309
    
    
      12
      0.000646
      0.066634
      0.024443
      0.037554
      -0.121900
      0.127679
      -0.108263
      0.108044
      0.171281
      0.107984
    
    
      15
      0.069869
      0.080188
      0.055549
      0.074867
      -0.094905
      0.099009
      -0.145672
      0.051607
      0.140944
      0.083084
    
    
      16
      0.091338
      0.076094
      0.000022
      0.233812
      -0.113537
      0.097707
      -0.157931
      -0.121617
      -0.078356
      0.227226
    
    
      17
      0.047451
      0.140318
      0.054805
      0.084570
      -0.204108
      0.003819
      -0.044212
      -0.042416
      0.071185
      0.228679
    
    
      19
      0.087238
      0.098669
      0.049238
      0.104640
      -0.106915
      0.067564
      -0.109175
      0.087966
      0.111473
      0.070341
    
    
      22
      0.033286
      -0.000654
      0.173198
      0.199046
      -0.163440
      0.176472
      -0.183354
      0.031309
      -0.011467
      -0.088938
    
    
      23
      -0.070479
      0.121597
      0.007689
      0.084306
      0.055208
      0.111745
      0.186667
      0.108758
      -0.048888
      -0.010410
    
    
      25
      0.275476
      -0.230280
      0.246899
      0.171006
      -0.302693
      0.157869
      0.102837
      -0.059534
      0.177327
      -0.182102
    
    
      27
      0.089195
      0.102056
      0.051737
      0.110445
      -0.104089
      0.057624
      -0.112806
      0.089226
      0.109461
      0.069108
    
    
      29
      0.034879
      0.123365
      0.014978
      0.149393
      -0.070228
      0.094729
      -0.110485
      0.000390
      0.052495
      0.185034
    
    
      30
      -0.082610
      -0.355950
      0.079619
      -0.125272
      -0.301729
      1.010372
      0.659684
      -0.248795
      0.117434
      0.470840
    
    
      33
      0.089734
      0.091777
      0.044150
      0.106371
      -0.102425
      0.067708
      -0.116865
      0.089550
      0.111210
      0.074599
    
    
      35
      0.091726
      0.099656
      0.056138
      0.104689
      -0.096596
      0.068928
      -0.113558
      0.079682
      0.113759
      0.071124
    
    
      36
      0.183615
      -0.129582
      -0.009280
      -0.128634
      -0.183836
      0.469065
      0.462965
      -0.005706
      0.368925
      0.287368
    
    
      38
      0.276116
      -0.368336
      0.157953
      0.153992
      -0.371499
      0.224039
      0.017487
      -0.384900
      0.016909
      0.092929
    
    
      39
      0.090349
      0.103528
      0.048020
      0.094973
      -0.098528
      0.072933
      -0.121648
      0.078060
      0.115203
      0.067754
    
    
      40
      -0.112042
      -0.174139
      0.243724
      0.045856
      -0.349329
      0.625671
      0.296915
      0.207385
      0.151393
      -0.188710
    
    
      43
      0.087703
      0.093278
      0.039341
      0.112302
      -0.100293
      0.066290
      -0.117577
      0.084413
      0.116441
      0.068222
    
    
      45
      0.095434
      0.105367
      0.045715
      0.097392
      -0.109944
      0.067066
      -0.114442
      0.080122
      0.110405
      0.065712
    
    
      48
      0.091624
      0.089534
      0.051559
      0.100573
      -0.099633
      0.070256
      -0.115347
      0.085518
      0.110642
      0.077814
    
    
      49
      0.090011
      0.100196
      0.042176
      0.098653
      -0.108228
      0.069636
      -0.116852
      0.089595
      0.106815
      0.070286
    
    
      50
      0.087062
      0.094266
      0.045454
      0.110902
      -0.102529
      0.062854
      -0.118697
      0.088485
      0.110456
      0.069471
    
    
      52
      0.083253
      0.092732
      0.048615
      0.102687
      -0.096652
      0.072535
      -0.121874
      0.086208
      0.118302
      0.069451
    
    
      53
      -0.074586
      0.118195
      -0.001786
      0.083203
      0.048812
      0.129365
      0.185033
      0.105815
      -0.051727
      -0.015784
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      447
      -0.052825
      0.104558
      0.001668
      0.081088
      0.031917
      0.112720
      0.180607
      0.101982
      -0.052730
      -0.035403
    
    
      450
      0.090784
      -0.265026
      0.170762
      0.231928
      -0.360185
      0.238125
      -0.249570
      -0.143541
      -0.181566
      -0.092046
    
    
      452
      -0.007944
      0.246385
      -0.017636
      0.178871
      0.077266
      0.429073
      0.134355
      0.171917
      -0.080726
      0.236309
    
    
      454
      0.148199
      0.066344
      -0.000891
      0.087365
      -0.006468
      0.152854
      -0.131126
      0.022376
      0.135121
      0.128504
    
    
      455
      0.387163
      -0.300269
      -0.063589
      -0.038878
      -0.166205
      0.346559
      0.114734
      -0.580486
      -0.028107
      0.664999
    
    
      458
      -0.022409
      0.180514
      0.036770
      0.121493
      -0.194443
      -0.012321
      -0.038436
      -0.107678
      0.011381
      0.347355
    
    
      459
      0.086537
      0.096211
      0.044022
      0.102844
      -0.106206
      0.067682
      -0.118965
      0.086229
      0.110985
      0.070918
    
    
      460
      0.086176
      0.094948
      0.052502
      0.095250
      -0.100135
      0.073225
      -0.119682
      0.090938
      0.111673
      0.073279
    
    
      461
      0.093453
      0.094726
      0.046016
      0.103331
      -0.103537
      0.068196
      -0.113851
      0.093328
      0.106020
      0.074212
    
    
      462
      -0.009144
      0.054628
      -0.045455
      0.133371
      -0.022795
      0.503285
      0.589281
      -0.242404
      0.184664
      0.660514
    
    
      464
      -0.049258
      -0.047589
      0.152541
      0.128306
      -0.202275
      0.219671
      -0.173160
      0.040970
      0.062192
      -0.049357
    
    
      468
      0.050379
      -0.046618
      0.200173
      0.328947
      -0.141044
      0.210893
      -0.257204
      -0.228634
      -0.214693
      0.032068
    
    
      469
      0.093028
      0.090585
      0.047421
      0.103511
      -0.098598
      0.068574
      -0.119860
      0.092650
      0.108269
      0.075087
    
    
      473
      0.092326
      0.092410
      0.042547
      0.098111
      -0.104181
      0.074588
      -0.117710
      0.086491
      0.110729
      0.072799
    
    
      474
      0.092048
      0.102665
      0.041481
      0.093755
      -0.109630
      0.072784
      -0.118755
      0.085089
      0.106730
      0.066362
    
    
      478
      0.161115
      -0.007041
      0.056669
      0.055352
      -0.216935
      0.026725
      -0.082211
      0.089330
      0.118223
      0.031433
    
    
      479
      -0.101068
      0.135038
      0.003041
      0.065909
      -0.237783
      0.054288
      -0.015715
      -0.110666
      0.077308
      0.382122
    
    
      481
      0.081967
      0.095540
      0.047519
      0.100817
      -0.109685
      0.070518
      -0.114137
      0.082708
      0.116141
      0.068827
    
    
      483
      0.128267
      -0.120323
      0.038312
      0.144115
      -0.002947
      0.430070
      -0.078708
      -0.206197
      0.144764
      0.090938
    
    
      484
      0.096415
      0.098917
      0.033990
      0.104391
      -0.103143
      0.069810
      -0.116475
      0.081806
      0.110683
      0.065098
    
    
      485
      0.093108
      0.096958
      0.048292
      0.102181
      -0.100979
      0.070902
      -0.116491
      0.086250
      0.109722
      0.069234
    
    
      486
      0.093019
      0.098077
      0.045700
      0.096967
      -0.100765
      0.072004
      -0.120247
      0.089644
      0.107845
      0.071343
    
    
      488
      0.071297
      0.108856
      0.035581
      0.119352
      -0.068418
      0.126829
      -0.065622
      0.108527
      0.092797
      0.111446
    
    
      491
      0.344091
      -0.144204
      0.101617
      0.063674
      -0.259098
      0.036117
      0.181888
      0.007086
      0.328418
      -0.022169
    
    
      493
      0.087989
      0.098752
      0.046831
      0.099545
      -0.102417
      0.072610
      -0.118356
      0.089564
      0.109259
      0.068012
    
    
      494
      -0.073800
      0.116293
      -0.006515
      0.075952
      0.045490
      0.127459
      0.186307
      0.096179
      -0.034882
      -0.023701
    
    
      496
      0.118371
      -0.236318
      0.261182
      0.095095
      -0.258123
      0.527516
      0.411485
      0.099284
      0.267055
      -0.181755
    
    
      497
      -0.066977
      0.128963
      0.178262
      0.289765
      -0.047914
      0.398260
      0.032021
      0.012872
      -0.121691
      0.032443
    
    
      498
      -0.052260
      0.141821
      0.001091
      0.056393
      0.055154
      0.160654
      0.187161
      0.107494
      -0.068163
      -0.036949
    
    
      499
      0.083557
      0.090731
      0.040724
      0.099736
      -0.106194
      0.078692
      -0.121653
      0.079857
      0.116108
      0.065122
    
  

260 rows × 10 columns



In [3]:

    
(u,i) = ag.readFactorModel('model_save', 10)

ranking test



In [1]:

    
%matplotlib inline
import alpenglow as ag
import alpenglow.Getter as rs
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import alpenglow.experiments
import alpenglow.evaluation



In [2]:

    
factorModelExperiment = alpenglow.experiments.FactorModelExperiment(
    top_k=100,
    seed=254938879,
    dimension=10,
    learning_rate=0.1,
    negative_rate=10
)
facRankings = factorModelExperiment.run(
    "python/test_alpenglow/test_data_4",
    experimentType="online_id",
    verbose=True,
    calculate_toplists=True
)









    



reading data...
data reading finished
logging predictions
running experiment...



In [3]:

    
data = pd.read_csv(
    "python/test_alpenglow/test_data_4",
    sep=' ',
    header=None,
    names=['time', 'user', 'item', 'id', 'score', 'eval']
)



In [4]:

    
preds = factorModelExperiment.get_predictions()



In [5]:

    
preds_joined = preds.join(data.reset_index().set_index(['index','item'])['score'], on=['record_id','item'], how="left")



In [12]:

    
preds_hits = (
    preds_joined
        .fillna(0)
        .sort_values('score', ascending=False)
        .drop_duplicates(subset=['record_id'])
        .sort_values('record_id')
)



In [15]:

    
preds_hits.loc[preds_hits['score']==0, 'rank']=101



In [16]:

    
preds_hits









    Out[16]:







  
    
      
      record_id
      time
      user
      item
      rank
      prediction
      score
    
  
  
    
      0
      1
      105
      23
      30
      101
      0.000151
      0.0
    
    
      2
      2
      110
      23
      337
      101
      0.000110
      0.0
    
    
      5
      3
      115
      23
      337
      101
      0.000111
      0.0
    
    
      9
      4
      120
      23
      120
      101
      -0.000059
      0.0
    
    
      14
      5
      125
      34
      30
      101
      0.000000
      0.0
    
    
      20
      6
      130
      23
      120
      101
      -0.000054
      0.0
    
    
      21
      7
      135
      23
      168
      101
      0.000328
      0.0
    
    
      34
      8
      140
      23
      204
      101
      0.000004
      0.0
    
    
      44
      9
      145
      23
      120
      101
      -0.000056
      0.0
    
    
      45
      10
      150
      23
      168
      101
      0.000331
      0.0
    
    
      65
      11
      155
      23
      120
      101
      -0.000042
      0.0
    
    
      66
      12
      160
      23
      168
      101
      0.000345
      0.0
    
    
      79
      13
      165
      23
      455
      101
      0.000254
      0.0
    
    
      101
      14
      170
      74
      166
      11
      0.000000
      1.0
    
    
      110
      15
      175
      70
      468
      101
      0.000000
      0.0
    
    
      133
      16
      180
      34
      372
      101
      -0.000149
      0.0
    
    
      142
      17
      185
      34
      225
      9
      -0.000005
      1.0
    
    
      156
      18
      190
      4
      256
      101
      0.000000
      0.0
    
    
      181
      19
      195
      23
      120
      101
      -0.000047
      0.0
    
    
      200
      20
      200
      23
      383
      101
      -0.000074
      0.0
    
    
      201
      21
      205
      23
      168
      101
      0.000370
      0.0
    
    
      226
      22
      210
      23
      256
      101
      0.000167
      0.0
    
    
      246
      23
      215
      23
      256
      101
      0.000182
      0.0
    
    
      268
      24
      220
      23
      30
      101
      0.000176
      0.0
    
    
      290
      25
      225
      23
      30
      101
      0.000174
      0.0
    
    
      329
      26
      230
      23
      94
      101
      -0.000269
      0.0
    
    
      335
      27
      235
      32
      468
      101
      0.000000
      0.0
    
    
      373
      28
      240
      23
      204
      19
      -0.000010
      1.0
    
    
      406
      29
      245
      23
      94
      101
      -0.000256
      0.0
    
    
      425
      30
      250
      23
      120
      101
      -0.000005
      0.0
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      88809
      970
      4950
      99
      271
      101
      0.000448
      0.0
    
    
      88968
      971
      4955
      32
      140
      101
      0.000232
      0.0
    
    
      89069
      972
      4960
      55
      120
      76
      0.000149
      1.0
    
    
      89148
      973
      4965
      84
      351
      101
      0.000179
      0.0
    
    
      89216
      974
      4970
      71
      496
      23
      0.000364
      1.0
    
    
      89371
      975
      4975
      91
      90
      101
      0.000112
      0.0
    
    
      89481
      976
      4980
      39
      118
      101
      0.000160
      0.0
    
    
      89557
      977
      4985
      82
      30
      64
      0.000081
      1.0
    
    
      89686
      978
      4990
      99
      43
      101
      0.000177
      0.0
    
    
      89706
      979
      4995
      99
      337
      101
      0.000489
      0.0
    
    
      89865
      980
      5000
      38
      43
      101
      0.000096
      0.0
    
    
      89899
      981
      5005
      84
      94
      101
      0.000357
      0.0
    
    
      89996
      982
      5010
      51
      27
      101
      0.000239
      0.0
    
    
      90178
      983
      5015
      28
      263
      101
      0.000064
      0.0
    
    
      90223
      984
      5020
      17
      323
      101
      0.000089
      0.0
    
    
      90356
      985
      5025
      83
      488
      101
      0.000199
      0.0
    
    
      90402
      986
      5030
      23
      445
      101
      0.000542
      0.0
    
    
      90505
      987
      5035
      80
      94
      12
      0.000217
      1.0
    
    
      90624
      988
      5040
      8
      196
      31
      0.000000
      1.0
    
    
      90710
      989
      5045
      70
      299
      17
      0.000460
      1.0
    
    
      90806
      990
      5050
      52
      418
      101
      0.000190
      0.0
    
    
      90941
      991
      5055
      55
      236
      101
      0.000204
      0.0
    
    
      91076
      992
      5060
      34
      395
      83
      0.000139
      1.0
    
    
      91121
      993
      5065
      53
      353
      101
      0.000197
      0.0
    
    
      91286
      994
      5070
      38
      128
      93
      0.000076
      1.0
    
    
      91324
      995
      5075
      86
      168
      31
      0.000248
      1.0
    
    
      91411
      996
      5080
      40
      357
      101
      0.000105
      0.0
    
    
      91564
      997
      5085
      83
      29
      71
      0.000187
      1.0
    
    
      91661
      998
      5090
      73
      152
      101
      0.000164
      0.0
    
    
      91695
      999
      5095
      23
      344
      101
      0.000707
      0.0
    
  

999 rows × 7 columns



In [ ]:

	user	item	rank
0	1	94	1
1	1	30	2
2	1	166	3
3	1	225	4
4	1	98	5
5	1	300	6
6	1	299	7
7	1	442	8
8	1	196	9
9	1	455	10
10	1	337	11
11	1	372	12
12	1	462	13
13	1	250	14
14	1	256	15
15	1	427	16
16	1	204	17
17	1	38	18
18	1	247	19
19	1	429	20
20	1	62	21
21	1	36	22
22	1	338	23
23	1	496	24
24	1	215	25
25	1	177	26
26	1	293	27
27	1	255	28
28	1	128	29
29	1	40	30
...	...	...	...
170	2	36	71
171	2	156	72
172	2	488	73
173	2	485	74
174	2	102	75
175	2	177	76
176	2	62	77
177	2	362	78
178	2	16	79
179	2	319	80
180	2	444	81
181	2	38	82
182	2	122	83
183	2	336	84
184	2	125	85
185	2	79	86
186	2	113	87
187	2	434	88
188	2	318	89
189	2	296	90
190	2	251	91
191	2	409	92
192	2	380	93
193	2	270	94
194	2	420	95
195	2	493	96
196	2	1	97
197	2	97	98
198	2	99	99
199	2	30	100

	time	user	item	id	score	eval
0	100	23	30	0	1	1
1	105	23	337	1	1	1
2	110	23	372	2	1	1
3	115	23	120	3	1	1
4	120	23	168	4	1	1
5	125	34	204	5	1	1
6	130	23	468	6	1	1
7	135	23	256	7	1	1
8	140	23	225	8	1	1
9	145	23	462	9	1	1
10	150	23	455	10	1	1
11	155	23	166	11	1	1
12	160	23	128	12	1	1
13	165	23	4	13	1	1
14	170	74	166	14	1	1
15	175	70	442	15	1	1
16	180	34	383	16	1	1
17	185	34	225	17	1	1
18	190	4	98	18	1	1
19	195	23	452	19	1	1
20	200	23	293	20	1	1
21	205	23	260	21	1	1
22	210	23	6	22	1	1
23	215	23	161	23	1	1
24	220	23	234	24	1	1
25	225	23	94	25	1	1
26	230	23	375	26	1	1
27	235	32	371	27	1	1
28	240	23	204	28	1	1
29	245	23	473	29	1	1
...	...	...	...	...	...	...
970	4950	99	108	970	1	1
971	4955	32	325	971	1	1
972	4960	55	120	972	1	1
973	4965	84	263	973	1	1
974	4970	71	496	974	1	1
975	4975	91	94	975	1	1
976	4980	39	205	976	1	1
977	4985	82	30	977	1	1
978	4990	99	177	978	1	1
979	4995	99	277	979	1	1
980	5000	38	62	980	1	1
981	5005	84	101	981	1	1
982	5010	51	166	982	1	1
983	5015	28	98	983	1	1
984	5020	17	204	984	1	1
985	5025	83	16	985	1	1
986	5030	23	22	986	1	1
987	5035	80	94	987	1	1
988	5040	8	196	988	1	1
989	5045	70	299	989	1	1
990	5050	52	30	990	1	1
991	5055	55	421	991	1	1
992	5060	34	395	992	1	1
993	5065	53	337	993	1	1
994	5070	38	128	994	1	1
995	5075	86	168	995	1	1
996	5080	40	383	996	1	1
997	5085	83	29	997	1	1
998	5090	73	234	998	1	1
999	5095	23	390	999	1	1

	1	2	3	4	5	6	7	8	9	10
item
0	0.093610	0.092002	0.041882	0.109370	-0.103590	0.065632	-0.113365	0.089075	0.110280	0.071719
1	0.093194	0.096220	0.050682	0.096436	-0.101917	0.079697	-0.117589	0.086036	0.103890	0.073094
4	0.096395	-0.069351	0.051472	0.011227	-0.426102	-0.041697	0.027440	-0.113184	0.110873	0.242209
5	0.001814	0.182096	0.018791	0.111336	-0.190668	-0.003389	-0.008853	-0.109414	0.011959	0.353851
6	0.049667	0.072248	0.011327	0.111306	-0.134521	0.069018	-0.125834	0.001380	0.095448	0.143113
7	0.083668	0.101772	0.054034	0.096514	-0.100438	0.072806	-0.116940	0.077727	0.120039	0.069309
12	0.000646	0.066634	0.024443	0.037554	-0.121900	0.127679	-0.108263	0.108044	0.171281	0.107984
15	0.069869	0.080188	0.055549	0.074867	-0.094905	0.099009	-0.145672	0.051607	0.140944	0.083084
16	0.091338	0.076094	0.000022	0.233812	-0.113537	0.097707	-0.157931	-0.121617	-0.078356	0.227226
17	0.047451	0.140318	0.054805	0.084570	-0.204108	0.003819	-0.044212	-0.042416	0.071185	0.228679
19	0.087238	0.098669	0.049238	0.104640	-0.106915	0.067564	-0.109175	0.087966	0.111473	0.070341
22	0.033286	-0.000654	0.173198	0.199046	-0.163440	0.176472	-0.183354	0.031309	-0.011467	-0.088938
23	-0.070479	0.121597	0.007689	0.084306	0.055208	0.111745	0.186667	0.108758	-0.048888	-0.010410
25	0.275476	-0.230280	0.246899	0.171006	-0.302693	0.157869	0.102837	-0.059534	0.177327	-0.182102
27	0.089195	0.102056	0.051737	0.110445	-0.104089	0.057624	-0.112806	0.089226	0.109461	0.069108
29	0.034879	0.123365	0.014978	0.149393	-0.070228	0.094729	-0.110485	0.000390	0.052495	0.185034
30	-0.082610	-0.355950	0.079619	-0.125272	-0.301729	1.010372	0.659684	-0.248795	0.117434	0.470840
33	0.089734	0.091777	0.044150	0.106371	-0.102425	0.067708	-0.116865	0.089550	0.111210	0.074599
35	0.091726	0.099656	0.056138	0.104689	-0.096596	0.068928	-0.113558	0.079682	0.113759	0.071124
36	0.183615	-0.129582	-0.009280	-0.128634	-0.183836	0.469065	0.462965	-0.005706	0.368925	0.287368
38	0.276116	-0.368336	0.157953	0.153992	-0.371499	0.224039	0.017487	-0.384900	0.016909	0.092929
39	0.090349	0.103528	0.048020	0.094973	-0.098528	0.072933	-0.121648	0.078060	0.115203	0.067754
40	-0.112042	-0.174139	0.243724	0.045856	-0.349329	0.625671	0.296915	0.207385	0.151393	-0.188710
43	0.087703	0.093278	0.039341	0.112302	-0.100293	0.066290	-0.117577	0.084413	0.116441	0.068222
45	0.095434	0.105367	0.045715	0.097392	-0.109944	0.067066	-0.114442	0.080122	0.110405	0.065712
48	0.091624	0.089534	0.051559	0.100573	-0.099633	0.070256	-0.115347	0.085518	0.110642	0.077814
49	0.090011	0.100196	0.042176	0.098653	-0.108228	0.069636	-0.116852	0.089595	0.106815	0.070286
50	0.087062	0.094266	0.045454	0.110902	-0.102529	0.062854	-0.118697	0.088485	0.110456	0.069471
52	0.083253	0.092732	0.048615	0.102687	-0.096652	0.072535	-0.121874	0.086208	0.118302	0.069451
53	-0.074586	0.118195	-0.001786	0.083203	0.048812	0.129365	0.185033	0.105815	-0.051727	-0.015784
...	...	...	...	...	...	...	...	...	...	...
447	-0.052825	0.104558	0.001668	0.081088	0.031917	0.112720	0.180607	0.101982	-0.052730	-0.035403
450	0.090784	-0.265026	0.170762	0.231928	-0.360185	0.238125	-0.249570	-0.143541	-0.181566	-0.092046
452	-0.007944	0.246385	-0.017636	0.178871	0.077266	0.429073	0.134355	0.171917	-0.080726	0.236309
454	0.148199	0.066344	-0.000891	0.087365	-0.006468	0.152854	-0.131126	0.022376	0.135121	0.128504
455	0.387163	-0.300269	-0.063589	-0.038878	-0.166205	0.346559	0.114734	-0.580486	-0.028107	0.664999
458	-0.022409	0.180514	0.036770	0.121493	-0.194443	-0.012321	-0.038436	-0.107678	0.011381	0.347355
459	0.086537	0.096211	0.044022	0.102844	-0.106206	0.067682	-0.118965	0.086229	0.110985	0.070918
460	0.086176	0.094948	0.052502	0.095250	-0.100135	0.073225	-0.119682	0.090938	0.111673	0.073279
461	0.093453	0.094726	0.046016	0.103331	-0.103537	0.068196	-0.113851	0.093328	0.106020	0.074212
462	-0.009144	0.054628	-0.045455	0.133371	-0.022795	0.503285	0.589281	-0.242404	0.184664	0.660514
464	-0.049258	-0.047589	0.152541	0.128306	-0.202275	0.219671	-0.173160	0.040970	0.062192	-0.049357
468	0.050379	-0.046618	0.200173	0.328947	-0.141044	0.210893	-0.257204	-0.228634	-0.214693	0.032068
469	0.093028	0.090585	0.047421	0.103511	-0.098598	0.068574	-0.119860	0.092650	0.108269	0.075087
473	0.092326	0.092410	0.042547	0.098111	-0.104181	0.074588	-0.117710	0.086491	0.110729	0.072799
474	0.092048	0.102665	0.041481	0.093755	-0.109630	0.072784	-0.118755	0.085089	0.106730	0.066362
478	0.161115	-0.007041	0.056669	0.055352	-0.216935	0.026725	-0.082211	0.089330	0.118223	0.031433
479	-0.101068	0.135038	0.003041	0.065909	-0.237783	0.054288	-0.015715	-0.110666	0.077308	0.382122
481	0.081967	0.095540	0.047519	0.100817	-0.109685	0.070518	-0.114137	0.082708	0.116141	0.068827
483	0.128267	-0.120323	0.038312	0.144115	-0.002947	0.430070	-0.078708	-0.206197	0.144764	0.090938
484	0.096415	0.098917	0.033990	0.104391	-0.103143	0.069810	-0.116475	0.081806	0.110683	0.065098
485	0.093108	0.096958	0.048292	0.102181	-0.100979	0.070902	-0.116491	0.086250	0.109722	0.069234
486	0.093019	0.098077	0.045700	0.096967	-0.100765	0.072004	-0.120247	0.089644	0.107845	0.071343
488	0.071297	0.108856	0.035581	0.119352	-0.068418	0.126829	-0.065622	0.108527	0.092797	0.111446
491	0.344091	-0.144204	0.101617	0.063674	-0.259098	0.036117	0.181888	0.007086	0.328418	-0.022169
493	0.087989	0.098752	0.046831	0.099545	-0.102417	0.072610	-0.118356	0.089564	0.109259	0.068012
494	-0.073800	0.116293	-0.006515	0.075952	0.045490	0.127459	0.186307	0.096179	-0.034882	-0.023701
496	0.118371	-0.236318	0.261182	0.095095	-0.258123	0.527516	0.411485	0.099284	0.267055	-0.181755
497	-0.066977	0.128963	0.178262	0.289765	-0.047914	0.398260	0.032021	0.012872	-0.121691	0.032443
498	-0.052260	0.141821	0.001091	0.056393	0.055154	0.160654	0.187161	0.107494	-0.068163	-0.036949
499	0.083557	0.090731	0.040724	0.099736	-0.106194	0.078692	-0.121653	0.079857	0.116108	0.065122

	record_id	time	user	item	rank	prediction	score
0	1	105	23	30	101	0.000151	0.0
2	2	110	23	337	101	0.000110	0.0
5	3	115	23	337	101	0.000111	0.0
9	4	120	23	120	101	-0.000059	0.0
14	5	125	34	30	101	0.000000	0.0
20	6	130	23	120	101	-0.000054	0.0
21	7	135	23	168	101	0.000328	0.0
34	8	140	23	204	101	0.000004	0.0
44	9	145	23	120	101	-0.000056	0.0
45	10	150	23	168	101	0.000331	0.0
65	11	155	23	120	101	-0.000042	0.0
66	12	160	23	168	101	0.000345	0.0
79	13	165	23	455	101	0.000254	0.0
101	14	170	74	166	11	0.000000	1.0
110	15	175	70	468	101	0.000000	0.0
133	16	180	34	372	101	-0.000149	0.0
142	17	185	34	225	9	-0.000005	1.0
156	18	190	4	256	101	0.000000	0.0
181	19	195	23	120	101	-0.000047	0.0
200	20	200	23	383	101	-0.000074	0.0
201	21	205	23	168	101	0.000370	0.0
226	22	210	23	256	101	0.000167	0.0
246	23	215	23	256	101	0.000182	0.0
268	24	220	23	30	101	0.000176	0.0
290	25	225	23	30	101	0.000174	0.0
329	26	230	23	94	101	-0.000269	0.0
335	27	235	32	468	101	0.000000	0.0
373	28	240	23	204	19	-0.000010	1.0
406	29	245	23	94	101	-0.000256	0.0
425	30	250	23	120	101	-0.000005	0.0
...	...	...	...	...	...	...	...
88809	970	4950	99	271	101	0.000448	0.0
88968	971	4955	32	140	101	0.000232	0.0
89069	972	4960	55	120	76	0.000149	1.0
89148	973	4965	84	351	101	0.000179	0.0
89216	974	4970	71	496	23	0.000364	1.0
89371	975	4975	91	90	101	0.000112	0.0
89481	976	4980	39	118	101	0.000160	0.0
89557	977	4985	82	30	64	0.000081	1.0
89686	978	4990	99	43	101	0.000177	0.0
89706	979	4995	99	337	101	0.000489	0.0
89865	980	5000	38	43	101	0.000096	0.0
89899	981	5005	84	94	101	0.000357	0.0
89996	982	5010	51	27	101	0.000239	0.0
90178	983	5015	28	263	101	0.000064	0.0
90223	984	5020	17	323	101	0.000089	0.0
90356	985	5025	83	488	101	0.000199	0.0
90402	986	5030	23	445	101	0.000542	0.0
90505	987	5035	80	94	12	0.000217	1.0
90624	988	5040	8	196	31	0.000000	1.0
90710	989	5045	70	299	17	0.000460	1.0
90806	990	5050	52	418	101	0.000190	0.0
90941	991	5055	55	236	101	0.000204	0.0
91076	992	5060	34	395	83	0.000139	1.0
91121	993	5065	53	353	101	0.000197	0.0
91286	994	5070	38	128	93	0.000076	1.0
91324	995	5075	86	168	31	0.000248	1.0
91411	996	5080	40	357	101	0.000105	0.0
91564	997	5085	83	29	71	0.000187	1.0
91661	998	5090	73	152	101	0.000164	0.0
91695	999	5095	23	344	101	0.000707	0.0

	user	item	rank
0	1	94	1
1	1	30	2
2	1	166	3
3	1	225	4
4	1	98	5
5	1	300	6
6	1	299	7
7	1	442	8
8	1	196	9
9	1	455	10
10	1	337	11
11	1	372	12
12	1	462	13
13	1	250	14
14	1	256	15
15	1	427	16
16	1	204	17
17	1	38	18
18	1	247	19
19	1	429	20
20	1	62	21
21	1	36	22
22	1	338	23
23	1	496	24
24	1	215	25
25	1	177	26
26	1	293	27
27	1	255	28
28	1	128	29
29	1	40	30
...	...	...	...
170	2	36	71
171	2	156	72
172	2	488	73
173	2	485	74
174	2	102	75
175	2	177	76
176	2	62	77
177	2	362	78
178	2	16	79
179	2	319	80
180	2	444	81
181	2	38	82
182	2	122	83
183	2	336	84
184	2	125	85
185	2	79	86
186	2	113	87
187	2	434	88
188	2	318	89
189	2	296	90
190	2	251	91
191	2	409	92
192	2	380	93
193	2	270	94
194	2	420	95
195	2	493	96
196	2	1	97
197	2	97	98
198	2	99	99
199	2	30	100

	user	item	rank
0	1	94	1
1	1	30	2
2	1	166	3
3	1	225	4
4	1	98	5
5	1	300	6
6	1	299	7
7	1	442	8
8	1	196	9
9	1	455	10
10	1	337	11
11	1	372	12
12	1	462	13
13	1	250	14
14	1	256	15
15	1	427	16
16	1	204	17
17	1	38	18
18	1	247	19
19	1	429	20
20	1	62	21
21	1	36	22
22	1	338	23
23	1	496	24
24	1	215	25
25	1	177	26
26	1	293	27
27	1	255	28
28	1	128	29
29	1	40	30
...	...	...	...
170	2	36	71
171	2	156	72
172	2	488	73
173	2	485	74
174	2	102	75
175	2	177	76
176	2	62	77
177	2	362	78
178	2	16	79
179	2	319	80
180	2	444	81
181	2	38	82
182	2	122	83
183	2	336	84
184	2	125	85
185	2	79	86
186	2	113	87
187	2	434	88
188	2	318	89
189	2	296	90
190	2	251	91
191	2	409	92
192	2	380	93
193	2	270	94
194	2	420	95
195	2	493	96
196	2	1	97
197	2	97	98
198	2	99	99
199	2	30	100