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CH,ENEM02010Q0001,71990,0.49326859197021716,1.35599,1.23358,0.23895,0,3373217
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CN,ENEM02010Q0086,70861,0.3434555203534193,1.92617,0.71518,0.13916,0,3373217
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CN,ENEM02010Q0088,57872,0.5708242309937368,1.88952,-0.19051,0.23446,0,3373217
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CN,ENEM02010Q0090,70272,0.32451158641735767,2.04719,0.92652,0.1801,0,3373217
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MT,ENEM02010Q0162,73313,0.23892969289316007,1.82066,2.38859,0.20188,0,3254991
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CH,ENEM02011Q0001,71745,0.5087527017146346,1.981,0.29799,0.14845,0,3982656
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CN,ENEM02011Q0071,73678,0.10898006480601509,,,,0,3982655
CN,ENEM02011Q0072,73305,0.5210652190561321,0.83048,-0.28157,0.00551,0,3982655
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CN,ENEM02011Q0089,71700,0.46608857659023945,2.32558,0.13736,0.22295,0,3982655
CN,ENEM02011Q0090,70807,0.35898690697537194,1.38451,0.82654,0.21537,0,3982655
MT,ENEM02011Q0136,72052,0.41014136573422794,2.23008,0.55451,0.27798,0,5366930
MT,ENEM02011Q0137,70183,0.46864230602516327,2.11832,0.73515,0.18808,0,3868891
MT,ENEM02011Q0138,71649,0.637024408286509,2.41492,0.28863,0.31102,0,3868891
MT,ENEM02011Q0139,72676,0.12432112346377205,2.96492,2.02812,0.0738,0,3868892
MT,ENEM02011Q0140,73124,0.4781034151647074,1.2623,0.61947,0.13382,0,3868891
MT,ENEM02011Q0141,72124,0.3254687195891536,2.21598,1.31369,0.1587,0,3868891
MT,ENEM02011Q0142,71642,0.3482465647132473,1.80059,1.4011,0.1643,0,3868891
MT,ENEM02011Q0143,70501,0.13254263301809227,2.68015,2.08631,0.07696,0,3868891
MT,ENEM02011Q0144,70698,0.19997177485744622,1.40069,2.32577,0.10823,0,3868891
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MT,ENEM02011Q0146,70407,0.4583452984330652,1.78608,0.80144,0.18101,0,3868891
MT,ENEM02011Q0147,72783,0.2891521110313007,1.99058,1.58634,0.17168,0,3868891
MT,ENEM02011Q0148,71238,0.2918976006302581,1.62432,1.82815,0.21852,0,3868891
MT,ENEM02011Q0149,75890,0.47735332941662095,2.1861,0.61239,0.13381,0,3868891
MT,ENEM02011Q0150,72056,0.41029121781926653,2.29585,0.9001,0.15579,0,3868891
MT,ENEM02011Q0151,70423,0.4958826702535688,1.42032,0.78256,0.16611,0,3868891
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MT,ENEM02011Q0154,72821,0.23286388786864246,1.56479,1.83743,0.12533,0,3868891
MT,ENEM02011Q0155,70540,0.1527194743920157,2.96462,3.137,0.14003,0,3868891
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MT,ENEM02011Q0158,70201,0.23075191314513643,1.63,2.90487,0.21354,0,3868891
MT,ENEM02011Q0159,71187,0.15349669970025,1.63381,2.36775,0.09676,0,3868891
MT,ENEM02011Q0160,70034,0.284375548445278,2.87279,1.68092,0.16149,0,3868891
MT,ENEM02011Q0161,70461,0.470604625459854,1.23541,0.4856,0.05802,0,3868891
MT,ENEM02011Q0162,72028,0.2648280863947834,2.27622,2.37639,0.23999,0,3868891
MT,ENEM02011Q0163,70198,0.3578596553896194,1.92502,1.29983,0.1805,0,3868891
MT,ENEM02011Q0164,70238,0.3218268490893127,2.74865,1.47857,0.21513,0,3868891
MT,ENEM02011Q0165,72687,0.2297306902675728,2.45814,2.11927,0.18911,0,3868891
MT,ENEM02011Q0166,70445,0.29136282205934466,2.83067,1.45097,0.18616,0,3868891
MT,ENEM02011Q0167,71127,0.320683368955083,2.06737,1.89008,0.24604,0,3868891
MT,ENEM02011Q0168,71130,0.20632294887604744,2.08886,2.57501,0.18102,0,3868891
MT,ENEM02011Q0169,72788,0.3079874827179158,1.64337,1.86905,0.20692,0,3868891
MT,ENEM02011Q0170,70405,0.36216218032505954,1.6024,1.2153,0.14692,0,3868891
MT,ENEM02011Q0171,70053,0.4374700657113369,1.81642,1.0908,0.24672,0,3868891
MT,ENEM02011Q0172,72644,0.4896573720996534,1.65137,0.45335,0.2265,0,3868891
MT,ENEM02011Q0173,72909,0.19548056536097813,2.87235,1.96767,0.15109,0,3868891
MT,ENEM02011Q0174,70406,0.15724919621669362,2.2181,2.68569,0.13379,0,3868891
MT,ENEM02011Q0175,70563,0.5738833169505163,1.60162,0.26671,0.2498,0,3868891
MT,ENEM02011Q0176,70734,0.5772499147688576,1.41344,0.04783,0.13377,0,3868891
MT,ENEM02011Q0177,70745,0.28203301669651587,2.74436,1.698,0.2011,0,3868891
MT,ENEM02011Q0178,71636,0.20223547264577885,3.03092,2.35596,0.17,0,3868891
MT,ENEM02011Q0179,73038,0.31274026587980897,2.53869,1.66527,0.23544,0,3868891
MT,ENEM02011Q0180,71864,0.35301847480324466,2.00926,1.20709,0.19016,0,3868891
CH,ENEM02012Q0001,9054,0.25159133004234857,2.169,1.621,0.188,0,4218798
CH,ENEM02012Q0002,8450,0.6145160303953875,0.929,0.518,0.171,0,4218798
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CH,ENEM02018Q0046,112081,0.3068929405827215,1.62334,1.80121,0.14924,0,4147127
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CH,ENEM02018Q0058,111920,0.2446028298626977,3.21792,1.6599,0.14834,0,4147127
CH,ENEM02018Q0059,112045,0.3688985169733167,3.46765,1.30736,0.15603,0,4147127
CH,ENEM02018Q0060,97982,0.2866697836839817,2.67992,1.47683,0.11389,0,4147127
CH,ENEM02018Q0061,97862,0.47250060101848823,3.15291,0.99606,0.06127,0,4147127
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CH,ENEM02018Q0064,97747,0.5594482638221593,4.81178,0.85966,0.15211,0,4147127
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CH,ENEM02018Q0068,111972,0.14099061832444484,3.66124,1.90182,0.09097,0,4147127
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CH,ENEM02018Q0071,89461,0.3308121501945805,2.26131,1.79644,0.22052,0,4147127
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CH,ENEM02018Q0075,89113,0.30277756142987666,2.42215,1.58606,0.13283,0,4147127
CH,ENEM02018Q0076,89639,0.5183795914617517,2.64093,0.97084,0.10439,0,4147127
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CH,ENEM02018Q0078,112013,0.37360370203275667,3.4662,1.3981,0.16469,0,4147127
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CH,ENEM02018Q0087,111943,0.24111294397302035,6.69428,1.39536,0.11947,0,4147127
CH,ENEM02018Q0088,44378,0.5027924150863959,4.22228,1.01525,0.18939,0,4147127
CH,ENEM02018Q0089,83733,0.3928010885608278,4.79502,1.23908,0.18538,0,4147127
CH,ENEM02018Q0090,96813,0.33029732349288965,5.31599,1.31276,0.17992,0,4146561
CN,ENEM02018Q0091,61264,0.2449831437727988,1.85116,1.38202,0.14086,0,3905085
CN,ENEM02018Q0092,11526,0.4641266451306438,1.62341,0.557,0.27893,0,3905085
CN,ENEM02018Q0093,111506,0.27347445702206225,1.87928,1.01563,0.07272,0,3905085
CN,ENEM02018Q0094,111408,0.2551642281794122,1.24846,1.46128,0.08248,0,3905085
CN,ENEM02018Q0095,66646,0.46753194872838877,2.38835,0.44914,0.13382,0,3905085
CN,ENEM02018Q0096,27417,0.2905825758175293,1.67211,1.96584,0.24188,0,3904539
CN,ENEM02018Q0097,40203,0.155346682594617,2.40632,1.8928,0.12094,0,3905085
CN,ENEM02018Q0098,17449,0.32322369423456854,8.00279,2.09568,0.33632,0,3905085
CN,ENEM02018Q0099,111456,0.23024203369463078,0.92339,1.76478,0.08431,0,3904539
CN,ENEM02018Q0100,87205,0.22352010263540997,2.18742,1.54804,0.13671,0,3905085
CN,ENEM02018Q0101,46621,0.25994645443056935,2.89268,0.97872,0.15829,0,3905085
CN,ENEM02018Q0102,85700,0.23172760644134507,1.8199,1.42382,0.13254,0,3905085
CN,ENEM02018Q0103,83440,0.10829418565793063,2.3472,1.81929,0.05895,0,3905085
CN,ENEM02018Q0104,62289,0.1631608530928264,1.51059,1.777,0.06851,0,3905085
CN,ENEM02018Q0105,87724,0.2588240716911412,1.71388,1.29656,0.10169,0,3905085
CN,ENEM02018Q0106,89518,0.20393051623716257,3.91646,1.31695,0.14694,0,3905085
CN,ENEM02018Q0107,111653,0.5078276145077508,1.11154,0.37898,0.09388,0,3905085
CN,ENEM02018Q0108,59858,0.19462444479441549,3.48757,1.61171,0.17521,0,3905085
CN,ENEM02018Q0109,86222,0.41388113190878045,3.34738,0.81755,0.28074,0,3905085
CN,ENEM02018Q0110,43554,0.2324167079589817,2.76937,1.50273,0.18595,0,3905085
CN,ENEM02018Q0111,76029,0.33968004929647266,1.57501,2.57259,0.3311,0,3904539
CN,ENEM02018Q0112,111533,0.1563707320071138,1.50152,1.93822,0.08461,0,3905085
CN,ENEM02018Q0113,76034,0.11690701514314494,3.2733,1.82425,0.09321,0,3904539
CN,ENEM02018Q0114,82909,0.40657220009295575,2.33756,1.05073,0.2738,0,3905085
CN,ENEM02018Q0115,12591,0.15969127432565489,4.53397,1.35731,0.08292,0,3905085
CN,ENEM02018Q0116,67958,0.1524115352162629,3.9208,1.41415,0.11735,0,3905085
CN,ENEM02018Q0117,82306,0.2304482488857477,4.08723,1.42925,0.18667,0,3905085
CN,ENEM02018Q0118,60359,0.13044146286188393,1.37076,2.55861,0.08688,0,3905085
CN,ENEM02018Q0119,111394,0.25749093809737816,2.58095,2.06279,0.24535,0,3905085
CN,ENEM02018Q0120,62499,0.24059972252806286,3.20508,1.25772,0.17322,0,3904539
CN,ENEM02018Q0121,88496,0.2408769591442952,1.17373,2.03465,0.14095,0,3905085
CN,ENEM02018Q0122,12199,0.29060895537219633,1.89006,1.7307,0.23529,0,3904539
CN,ENEM02018Q0123,111720,0.29552929065564515,2.03842,1.28024,0.1488,0,3905085
CN,ENEM02018Q0124,111395,0.16915329627908227,1.74195,2.00253,0.12818,0,3905085
CN,ENEM02018Q0125,39342,0.3603150763683761,2.07227,1.12818,0.2408,0,3905085
CN,ENEM02018Q0126,111635,0.2660193055977015,1.8637,1.5452,0.17661,0,3905085
CN,ENEM02018Q0127,111630,0.23347007162689373,1.44722,1.54204,0.09725,0,3904539
CN,ENEM02018Q0128,111463,0.28092458546322624,1.7303,2.07699,0.24838,0,3904539
CN,ENEM02018Q0129,111411,0.40927392452732575,1.25858,0.79603,0.19389,0,3904539
CN,ENEM02018Q0130,97446,0.3121625882082366,1.869,0.91258,0.12516,0,3904539
CN,ENEM02018Q0131,111564,0.24069207200355433,1.5965,1.16868,0.11844,0,3905085
CN,ENEM02018Q0132,111414,0.30300057489145565,2.04034,1.92499,0.21775,0,3905085
CN,ENEM02018Q0133,111761,0.25705740079921435,2.03133,1.98006,0.20358,0,3905085
CN,ENEM02018Q0134,111637,0.1551489916352653,2.65752,1.59508,0.15058,0,3905085
CN,ENEM02018Q0135,111612,0.22033502471777183,4.95113,0.77702,0.26199,0,3905085
MT,ENEM02018Q0136,111524,0.3116697843964984,3.86138,1.36143,0.14915,0,3905085
MT,ENEM02018Q0137,97059,0.3128956219903024,3.05583,1.34943,0.10809,0,3905085
MT,ENEM02018Q0138,48921,0.25396963190301874,2.1454,2.05264,0.19268,0,3905085
MT,ENEM02018Q0139,111594,0.21642115344480337,2.66415,1.64983,0.09545,0,3905085
MT,ENEM02018Q0140,28815,0.1638765865531736,2.15655,3.72556,0.14695,0,3905085
MT,ENEM02018Q0141,29500,0.169876712030596,2.44385,1.98197,0.08633,0,3905085
MT,ENEM02018Q0142,82367,0.2136363228969408,1.46307,2.90397,0.16934,0,3905085
MT,ENEM02018Q0143,111512,0.3877713289211374,2.94976,1.27294,0.19298,0,3905085
MT,ENEM02018Q0144,111557,0.2519304957510528,1.67338,3.02425,0.21851,0,3905085
MT,ENEM02018Q0145,84768,0.1946247008707877,2.42824,2.08891,0.13961,0,3905085
MT,ENEM02018Q0146,111609,0.7437979233912121,1.91344,0.1217,0.34263,0,3904539
MT,ENEM02018Q0147,111487,0.31252072618137633,3.53913,1.54995,0.19735,0,3905085
MT,ENEM02018Q0148,86027,0.11591885964069924,1.4909,3.45729,0.09597,0,3905085
MT,ENEM02018Q0149,62736,0.26181837271147745,2.43503,1.6332,0.12168,0,3905085
MT,ENEM02018Q0150,30294,,,,,2,3905085
MT,ENEM02018Q0151,66805,0.24017838280088655,4.68954,1.506,0.12012,0,3905085
MT,ENEM02018Q0152,15292,0.24848601246835855,3.33463,1.86899,0.21641,0,3905085
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MT,ENEM02018Q0167,32953,0.34260867561141434,3.16831,1.44943,0.19086,0,3905085
MT,ENEM02018Q0168,5990,0.17986804384539645,1.929,3.008,0.185,0,3905085
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MT,ENEM02018Q0170,85892,0.22071709066511996,0.32259,4.77908,0.02107,0,3905085
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MT,ENEM02018Q0172,18456,0.3090078705073001,2.60715,3.02371,0.30706,0,3905085
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MT,ENEM02018Q0176,111718,0.34693173232201724,0.24681,3.47775,0.02647,0,3903033
MT,ENEM02018Q0177,111554,0.29163257649961527,2.77807,3.31893,0.28666,0,3905085
MT,ENEM02018Q0178,96201,0.1963109637818383,3.58407,2.94242,0.1875,0,3905085
MT,ENEM02018Q0179,15884,0.2499963509116959,1.0681,2.89341,0.16386,0,3905085
MT,ENEM02018Q0180,111434,0.1777131611731883,1.1816,3.44272,0.1365,0,3905085
CH,ENEM02019Q0046,86751,0.1286477395370842,3.45284,1.65017,0.09484,0,3923046
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CH,ENEM02019Q0048,77940,0.6313170939112108,1.69265,-0.16281,0.19993,0,3923046
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CH,ENEM02019Q0058,33960,0.26052103391089476,3.73216,1.13742,0.16303,0,3923046
CH,ENEM02019Q0059,78903,0.6263265839860149,1.5682,-0.25115,0.00814,0,3923046
CH,ENEM02019Q0060,84888,0.2523605382144385,2.3107,1.45829,0.17477,0,3923046
CH,ENEM02019Q0061,88233,0.3050777380637392,1.39689,1.31818,0.11442,0,3923046
CH,ENEM02019Q0062,86934,0.3731110978561047,0.61655,1.00168,0.04145,0,3923046
CH,ENEM02019Q0063,118158,0.4714232257281714,2.22546,0.46653,0.15939,0,3923046
CH,ENEM02019Q0064,88306,0.33867815977686727,1.78628,1.34891,0.18079,0,3923046
CH,ENEM02019Q0065,88194,0.22649874612737145,1.35668,1.69195,0.10234,0,3923046
CH,ENEM02019Q0066,118125,0.42815811999145564,1.14176,0.61713,0.00161,0,3923046
CH,ENEM02019Q0067,87057,0.5607487141369232,1.22262,0.2453,0.17471,0,3923046
CH,ENEM02019Q0068,85139,0.46576843605708423,2.89534,0.60872,0.22906,0,3923046
CH,ENEM02019Q0069,118029,0.3251996025537299,2.63717,1.07054,0.22582,0,3923046
CH,ENEM02019Q0070,118024,0.15590589557196116,2.44871,1.81367,0.12029,0,3923046
CH,ENEM02019Q0071,97013,0.3489393190903191,0.73411,1.20733,0.01008,0,3923046
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CH,ENEM02019Q0073,89147,0.29410157311436064,3.04427,1.36609,0.23176,0,3923046
CH,ENEM02019Q0074,96596,0.23539540448926677,1.69675,2.03032,0.19839,0,3923046
CH,ENEM02019Q0075,84147,0.49681803374214834,2.067,0.32546,0.20034,0,3923046
CH,ENEM02019Q0076,95648,0.3207316457670902,2.21046,1.12907,0.18621,0,3923046
CH,ENEM02019Q0077,118038,0.2918681045289808,3.49023,1.02573,0.18725,0,3923046
CH,ENEM02019Q0078,76955,0.5932612602877145,,,,2,3923612
CH,ENEM02019Q0079,111894,0.28663696525607907,2.21881,1.10809,0.13721,0,3923046
CH,ENEM02019Q0080,118054,0.19358019253406664,2.07127,1.3472,0.07736,0,3923046
CH,ENEM02019Q0081,118067,0.17704814065397143,,,,0,3923046
CH,ENEM02019Q0082,66498,0.48290715938584455,1.31749,0.44063,0.18259,0,3923046
CH,ENEM02019Q0083,88308,0.30161053426342693,1.40104,1.15692,0.09673,0,3923046
CH,ENEM02019Q0084,87709,0.2594504372367798,2.06585,1.48052,0.17341,0,3923046
CH,ENEM02019Q0085,112135,0.529689685004968,2.05925,0.34452,0.21848,0,3923046
CH,ENEM02019Q0086,81689,0.19343107371160062,2.34674,1.87883,0.11625,0,3923046
CH,ENEM02019Q0087,97975,0.3419944604269234,1.45676,1.3618,0.21595,0,3923046
CH,ENEM02019Q0088,118108,0.5230818093899485,2.80224,0.44492,0.21353,0,3923046
CH,ENEM02019Q0089,66219,0.3903176256408923,1.471,1.73902,0.23765,0,3923046
CH,ENEM02019Q0090,111877,0.4234747693501427,2.54319,0.68737,0.16087,0,3923046
CN,ENEM02019Q0091,111601,0.22934775431328977,7.09582,2.42384,0.23686,0,3710335
CN,ENEM02019Q0092,111545,0.2569773349306734,0.54124,3.79854,0.17374,0,3710335
CN,ENEM02019Q0093,19407,0.18356940815317216,2.32962,1.67314,0.14993,0,3710335
CN,ENEM02019Q0094,82364,0.29576224249292854,2.43167,1.13773,0.22233,0,3710335
CN,ENEM02019Q0095,117836,0.17294476105257342,1.87838,1.45214,0.07496,0,3710335
CN,ENEM02019Q0096,29776,0.49973627718251856,2.80865,0.58402,0.21207,0,3710335
CN,ENEM02019Q0097,117979,0.3023497878223934,1.19339,2.04675,0.26041,0,3710335
CN,ENEM02019Q0098,83609,0.3027745473117657,,,,0,3710335
CN,ENEM02019Q0099,60311,0.47488003104840937,2.79351,0.15609,0.14102,0,3710335
CN,ENEM02019Q0100,117890,0.2623935574550546,1.85268,0.99689,0.12401,0,3710335
CN,ENEM02019Q0101,54381,0.12161258063037929,3.20714,2.14905,0.08067,0,3709830
CN,ENEM02019Q0102,31718,0.41223582237183437,1.90483,0.48421,0.21286,0,3710335
CN,ENEM02019Q0103,89150,0.4086409987238349,3.482,0.29664,0.15617,0,3710335
CN,ENEM02019Q0104,117988,0.22153013137627733,3.11909,0.80483,0.08561,0,3710335
CN,ENEM02019Q0105,7529,0.4264585273297425,2.26543,0.62256,0.1662,0,3710335
CN,ENEM02019Q0106,14273,0.09608458535415265,3.90297,2.19782,0.05798,0,3710335
CN,ENEM02019Q0107,97169,0.1317393173392699,5.93339,1.98881,0.13188,0,3710335
CN,ENEM02019Q0108,49600,0.18077343420472813,1.14904,2.36402,0.1148,0,3710335
CN,ENEM02019Q0109,96556,0.2458888483115406,1.66258,1.3767,0.17468,0,3710335
CN,ENEM02019Q0110,39290,0.3320624687528215,2.00073,0.8684,0.16452,0,3710335
CN,ENEM02019Q0111,89298,0.41931361992919775,1.02022,0.29761,0.0167,0,3710335
CN,ENEM02019Q0112,55328,0.37722941482493805,0.54974,0.97948,0.05877,0,3709830
CN,ENEM02019Q0113,89515,0.32509679045153606,3.14647,1.67856,0.32644,0,3710335
CN,ENEM02019Q0114,117868,0.4929452461839699,0.05808,8.96186,0.22071,0,3710335
CN,ENEM02019Q0115,111624,0.4936521904356345,1.03081,-0.00312,0.01163,0,3710335
CN,ENEM02019Q0116,82236,0.18660309648589682,2.77263,1.28601,0.13618,0,3710335
CN,ENEM02019Q0117,83441,0.1319209720955116,4.60881,1.94111,0.12462,0,3710335
CN,ENEM02019Q0118,19223,0.49941393432129444,2.06769,-0.11974,0.19742,0,3710335
CN,ENEM02019Q0119,78504,0.18884602064234093,3.20779,1.67408,0.16383,0,3710335
CN,ENEM02019Q0120,7629,0.2750145202522144,0.75316,1.83643,0.10672,0,3710335
CN,ENEM02019Q0121,88853,0.34246206878893687,2.39212,0.70901,0.165,0,3710335
CN,ENEM02019Q0122,117883,0.25312377453787865,0.38139,3.36471,0.06869,0,3710335
CN,ENEM02019Q0123,82490,0.3344838134561974,1.75058,1.02204,0.23374,0,3710335
CN,ENEM02019Q0124,117999,0.3286978130007129,3.76618,0.41535,0.10709,0,3710335
CN,ENEM02019Q0125,117746,0.16311087812825525,1.2139,2.21432,0.09924,0,3710335
CN,ENEM02019Q0126,86521,0.4573441481699092,1.71979,0.25498,0.14498,0,3710335
CN,ENEM02019Q0127,81478,0.24164745231899545,2.37739,1.59193,0.20921,0,3710335
CN,ENEM02019Q0128,83526,0.21549132355973247,1.62997,1.70518,0.1628,0,3710335
CN,ENEM02019Q0129,117875,0.18943249059721023,1.78539,1.58564,0.13602,0,3710335
CN,ENEM02019Q0130,117642,0.3469247386017705,1.92967,1.07472,0.22014,0,3710335
CN,ENEM02019Q0131,85548,0.30174984199539934,2.96175,0.58267,0.11175,0,3710335
CN,ENEM02019Q0132,84568,0.28687948662317553,2.42831,1.66707,0.29842,0,3710335
CN,ENEM02019Q0133,89772,0.2409367348231359,0.67587,1.59601,0.02169,0,3710335
CN,ENEM02019Q0134,24104,0.238272554904072,3.41094,1.15715,0.16829,0,3710335
CN,ENEM02019Q0135,111705,0.22634047868992963,1.78669,1.78124,0.20473,0,3710335
MT,ENEM02019Q0136,117783,0.23484375400064952,1.96646,2.01615,0.1547,0,3710335
MT,ENEM02019Q0137,83792,0.19996981404644054,1.25552,1.93449,0.06675,0,3710335
MT,ENEM02019Q0138,8442,0.18479732962117976,1.51,3.003,0.147,0,3710335
MT,ENEM02019Q0139,117635,0.5486175916416656,1.84162,0.88644,0.35048,0,3709830
MT,ENEM02019Q0140,9779,0.1523848924692784,2.287,2.217,0.085,0,3710335
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MT,ENEM02019Q0143,83994,0.30024081383486934,1.78432,1.1667,0.0455,0,3710335
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MT,ENEM02019Q0148,86432,0.617383336006048,1.12267,0.37213,0.26876,0,3710335
MT,ENEM02019Q0149,23065,0.1554711367032896,,,,0,3710335
MT,ENEM02019Q0150,117798,0.16016235730735903,2.72919,1.92868,0.23186,0,3710335
MT,ENEM02019Q0151,30740,0.3170010255138687,1.64938,1.94549,0.20724,0,3710335
MT,ENEM02019Q0152,30436,0.26941313924483906,1.64692,0.47926,0.18811,0,3710335
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MT,ENEM02019Q0154,84409,0.21740489740144758,1.77601,1.53793,0.14814,0,3710335
MT,ENEM02019Q0155,28434,0.30973267912466124,2.45024,3.01267,0.14322,0,3710335
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MT,ENEM02019Q0160,39708,0.19989192350555948,2.45518,2.61221,0.15911,0,3710335
MT,ENEM02019Q0161,117950,0.17721499541146554,3.76775,2.42062,0.20831,0,3710335
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MT,ENEM02019Q0168,18002,0.18644408119482472,3.41848,1.95818,0.16938,0,3710335
MT,ENEM02019Q0169,42803,0.23399800826609995,2.30372,1.40103,0.13792,0,3710335
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MT,ENEM02019Q0171,10360,0.24433427170322897,1.52296,2.72201,0.36496,0,3710335
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MT,ENEM02019Q0174,117818,0.2363533211960645,1.62365,1.98136,0.18076,0,3710335
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MT,ENEM02019Q0179,117767,0.23894904368473466,0.1828,12.4166,0.11617,0,3710335
MT,ENEM02019Q0180,83955,0.2047914810926776,2.5833,1.63345,0.16356,0,3710335
CH,ENEM02021Q0046,33940,0.15095465523453389,2.20524,2.56419,0.13887,0,2300883
CH,ENEM02021Q0047,125656,0.5328562990817004,2.13734,0.664,0.25431,0,2300883
CH,ENEM02021Q0048,111846,0.3624725811786171,3.22135,1.15587,0.18835,0,2300883
CH,ENEM02021Q0049,88365,0.2827644865036597,1.29208,1.81119,0.20192,0,2300883
CH,ENEM02021Q0050,112129,0.5862988252770784,1.22,0.04233,0.19344,0,2300883
CH,ENEM02021Q0051,96415,0.27519130698953403,2.49173,1.57817,0.16145,0,2300883
CH,ENEM02021Q0052,96369,0.32928184527418386,2.56529,1.19286,0.16157,0,2300883
CH,ENEM02021Q0053,88561,0.23435002996675625,1.78332,2.10818,0.177,0,2300883
CH,ENEM02021Q0054,95563,0.4488768007760499,1.50299,0.57725,0.18681,0,2300883
CH,ENEM02021Q0055,86998,0.5025818348868674,1.8994,0.84844,0.2609,0,2300883
CH,ENEM02021Q0056,118059,0.4554147255640552,3.04986,0.83936,0.23466,0,2300883
CH,ENEM02021Q0057,98208,0.537862203336719,2.65063,0.47062,0.1495,0,2300883
CH,ENEM02021Q0058,125722,0.28330254080715966,2.36414,1.17248,0.09949,0,2300883
CH,ENEM02021Q0059,111925,0.48472434278492216,2.88658,0.55124,0.13869,0,2300883
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CN,ENEM12020Q0105,95441,0.42353629131750026,3.0376,0.62392,0.2084,0,2508713
CN,ENEM12020Q0106,62745,0.22151028981965457,2.74421,1.55431,0.18593,0,2508353
CN,ENEM12020Q0107,60072,0.19338506183140888,4.35472,1.09202,0.14701,0,2508353
CN,ENEM12020Q0108,83584,0.5428797302453043,3.26129,0.47507,0.24933,0,2508353
CN,ENEM12020Q0109,89494,0.22213182615946903,2.28319,2.62945,0.24444,0,2508713
CN,ENEM12020Q0110,49826,0.4157578009122606,1.79438,0.79213,0.20738,0,2508713
CN,ENEM12020Q0111,111501,0.6741835355419292,1.46882,-0.23526,0.19946,0,2508713
CN,ENEM12020Q0112,84063,0.2557777633392102,2.95498,1.28416,0.16449,0,2508713
CN,ENEM12020Q0113,87675,0.2681378858402695,2.46972,0.95056,0.13112,0,2508713
CN,ENEM12020Q0114,32352,,,,,1,2508713
CN,ENEM12020Q0115,83684,0.3230935543443989,1.7008,1.15068,0.14749,0,2508713
CN,ENEM12020Q0116,111537,0.1678705376023483,2.82041,2.00971,0.12385,0,2508713
CN,ENEM12020Q0117,38012,0.39865003051803316,1.88342,1.48748,0.27044,0,2508353
CN,ENEM12020Q0118,55948,0.5598488149102747,2.9073,0.12756,0.21003,0,2508713
CN,ENEM12020Q0119,82243,0.20115652926420838,1.92924,2.72331,0.17678,0,2508713
CN,ENEM12020Q0120,87690,0.43085040018527426,2.54499,0.32436,0.18403,0,2508713
CN,ENEM12020Q0121,98228,0.28643336882807163,1.57481,1.65344,0.22044,0,2508353
CN,ENEM12020Q0122,49867,0.17276986247530107,3.21388,1.90806,0.17205,0,2508713
CN,ENEM12020Q0123,76371,0.25198219166560704,2.39814,1.69946,0.19017,0,2508713
CN,ENEM12020Q0124,10035,0.4126167481094888,2.40418,0.72028,0.21294,0,2508713
CN,ENEM12020Q0125,84184,0.48694729130035996,1.85393,0.63073,0.178,0,2508713
CN,ENEM12020Q0126,79124,0.5725118815902815,1.71246,-0.07547,0.17405,0,2508713
CN,ENEM12020Q0127,31244,0.23154741096331066,2.91476,1.12362,0.11243,0,2508713
CN,ENEM12020Q0128,17936,0.21932361334277775,3.01205,1.50249,0.18745,0,2508713
CN,ENEM12020Q0129,14966,0.3803460180578647,1.99076,0.67442,0.18662,0,2508713
CN,ENEM12020Q0130,59913,0.22367365258600724,2.29781,1.5026,0.13077,0,2508713
CN,ENEM12020Q0131,90016,0.21295048982340206,1.0062,2.26887,0.12084,0,2508353
CN,ENEM12020Q0132,87642,0.2730766731786378,3.99324,0.94637,0.16656,0,2508713
CN,ENEM12020Q0133,53548,0.2982042983792885,0.29544,3.80025,0.02691,0,2508713
CN,ENEM12020Q0134,84444,0.24298015470709267,3.17216,1.35723,0.2178,0,2508353
CN,ENEM12020Q0135,78655,0.39484269424202767,0.50541,0.95758,0.04207,0,2508713
MT,ENEM12020Q0136,49630,0.3324508153358,2.75865,1.15201,0.15163,0,2508353
MT,ENEM12020Q0137,30344,0.4547454046539702,1.2009,0.84968,0.16359,0,2508353
MT,ENEM12020Q0138,7633,0.18940229512104415,2.73149,2.49854,0.13472,0,2508713
MT,ENEM12020Q0139,15248,0.25911014930763304,3.29625,2.86173,0.25529,0,2508713
MT,ENEM12020Q0140,86282,0.26962430537092125,2.26511,2.77688,0.23479,0,2508713
MT,ENEM12020Q0141,44322,,,,,1,2508713
MT,ENEM12020Q0142,48864,0.19480631314651486,1.78193,3.29884,0.18914,0,2508353
MT,ENEM12020Q0143,60666,0.30631164266299094,0.96583,2.50648,0.2049,0,2508713
MT,ENEM12020Q0144,10660,0.21318979094061377,1.75839,2.40752,0.16633,0,2508713
MT,ENEM12020Q0145,85793,0.17246133774568873,2.94722,2.06568,0.12112,0,2508713
MT,ENEM12020Q0146,49782,0.38063899696776793,1.08432,0.84339,0.01494,0,2508713
MT,ENEM12020Q0147,63324,0.192169052418511,2.18282,2.31084,0.14885,0,2508713
MT,ENEM12020Q0148,85224,0.1939739658543608,1.50087,2.33109,0.12155,0,2507553
MT,ENEM12020Q0149,82724,0.6455477370269138,1.5867,-0.08552,0.20086,0,2508713
MT,ENEM12020Q0150,32975,0.15925855209424114,3.35137,3.34168,0.16605,0,2508713
MT,ENEM12020Q0151,59546,0.49289177359068176,2.75624,0.62447,0.09203,0,2508713
MT,ENEM12020Q0152,111432,0.31958936713765185,1.18053,2.23254,0.20706,0,2508713
MT,ENEM12020Q0153,29531,0.21866072364594913,1.73821,2.6803,0.13986,0,2508713
MT,ENEM12020Q0154,85945,0.32734427730068294,1.53928,1.60311,0.19657,0,2508353
MT,ENEM12020Q0155,77477,0.44379129856623695,2.96092,0.8958,0.18532,0,2508713
MT,ENEM12020Q0156,84253,0.1634435664820966,2.88702,2.82281,0.13958,0,2508713
MT,ENEM12020Q0157,111714,0.2564478280297507,2.13749,1.78181,0.156,0,2508713
MT,ENEM12020Q0158,111461,0.23792278767387967,2.99017,1.89021,0.15884,0,2507553
MT,ENEM12020Q0159,111491,0.6233249466881257,1.25662,-0.40358,0.20406,0,2508353
MT,ENEM12020Q0160,85053,0.37263728453593536,0.93375,1.1092,0.07392,0,2508713
MT,ENEM12020Q0161,43031,0.14375418790431588,2.99305,1.84654,0.06394,0,2508713
MT,ENEM12020Q0162,62498,0.33885422525414427,3.02476,1.33416,0.11462,0,2508713
MT,ENEM12020Q0163,8028,0.37500423523934384,2.09221,0.99371,0.1329,0,2508713
MT,ENEM12020Q0164,37990,0.3382551132791993,1.5164,1.9226,0.2304,0,2508713
MT,ENEM12020Q0165,37286,0.19798239176820945,3.25442,1.5772,0.1355,0,2508713
MT,ENEM12020Q0166,55387,0.28830918482903384,2.59667,1.34028,0.15449,0,2508713
MT,ENEM12020Q0167,86803,0.39379673960313516,2.04724,1.08171,0.1794,0,2508713
MT,ENEM12020Q0168,13085,0.24439941914439794,4.06335,2.18033,0.2161,0,2508713
MT,ENEM12020Q0169,111525,0.3202041339476541,2.14544,1.73587,0.22609,0,2508353
MT,ENEM12020Q0170,30462,0.20896587230658734,1.57033,2.17738,0.11432,0,2507553
MT,ENEM12020Q0171,88028,0.5271772919655138,2.31216,0.44069,0.18915,0,2507553
MT,ENEM12020Q0172,83753,0.14139839830223705,2.5968,2.82961,0.09691,0,2508713
MT,ENEM12020Q0173,111530,0.25614098175177097,2.59012,2.03367,0.18389,0,2508353
MT,ENEM12020Q0174,83638,0.3542597339751498,2.00634,1.15438,0.14059,0,2508713
MT,ENEM12020Q0175,41676,0.26517341760496316,3.19891,1.60834,0.17181,0,2508713
MT,ENEM12020Q0176,15897,0.304679730204292,1.89497,1.65188,0.27993,0,2508713
MT,ENEM12020Q0177,111719,0.21274653577352212,1.22121,2.10949,0.09007,0,2508713
MT,ENEM12020Q0178,24338,0.29634784258834385,1.33979,1.93473,0.18699,0,2508353
MT,ENEM12020Q0179,111566,0.24821771163142217,2.73154,1.76976,0.1412,0,2508713
MT,ENEM12020Q0180,63901,0.18742797601798214,2.42448,3.0197,0.15362,0,2508713
enem_aggregate/README.md 0 → 100644
View file @ b6dd9af8
# ENEM aggregation code
This code does most of the grunt work of aggregating the massive interaction files from INEP for ENEM, as well as aggregating accross metafiles also provided by INEP for each year (which contain their IRT values for the questions) and also combining these with the question and answer text provided by Marinho.
It does *not* necessarily bring the results into the concrete format used by the models. That is done in python to allow for more model specific adaptions in in the model repository itself.
Originally this code was intended to be run in a streaming fashion on just any PC.
However that did not work out, so a part is executed on the Laptop now.
Because there was already a lot of code, leading to at the very least a lot of manual labor I cant spare to port to Python, we do retain a split of the code.
Because we're working with a lot of data, that may or may not be what we expected, this code is written in a very script like fashion.
## Installation
I suggest installing rust and its connected tools via `rustup`.
Install `rustup` either via your package manager (`homebrew`, `winget`, `apt` etc probably have it), or from https://rustup.rs/.
Rustup should install all the tools required to run the local code on your computer.
If you want to run the code on the cluster ensure that you can compile code that the cluster can run by executing `rustup toolchain install stable-x86_64-unknown-linux-gnu` (this is fine anywhere, and required on Apple Silicon or Windows).
The results of both the local and the cluster code are mostly already present in `./GENERATED/`, so that you dont have to install the tools, and dont have to execute the lengthy cluster code.
Update: I wont release the ENEM texts, as they were provided by Marinho and they didnt open them up themselfes. Theres a [file without that text](./GENERATED/localGen_result_no_secrets.csv) but the Identifiers used by Marinho when he send the data over to us, if you get the same format we got it should be easy to unify.
## A few words about Rust
Rust doesnt have exceptions, instead relying on a `Result` type as return value, and `panic`s.
`panic`s are not intended to be catched and handled during program execution, instead they will terminate the program.
`Result` is an Enum, but Rust enums differ from eg. Java Enums, in that they may contain values per instance (this is know by terms such as tagged union, discriminated union, product type).
In handling, because it is a value that contains the "good path" value, it behaves similar to a checked exception in Java, though it is closer to the `Either` class in Haskell - with some additional meaning, the variants are called `Ok` and `Err`.
A `Result` may be promoted to a panic with eg. `unwrap()`, which, because of the script-like nature of this code is done frequently.
Rust also has the `Option` type, which is similar, but replaces the concept of (type-checked) nulls in Rust - it is equivalent to `Maybe` in Haskell. That type also has an `unwrap()` which behaves similar.
One last thing I've frequently done in this code that may be unknown to people is using `let-else` bindings.
I will assume familiarity with pattern matching, there is a lot of resources on it, and I've also done it in Python.
Rust has pattern matching, but in many spots, eg a `let` pattern-binding it requires that the bound pattern does always apply, so matching an vector that is known by the programmer but not the compiler to contain 2 elements is still impossible.
`let-else` bindings were adopted in Rust, from Swift, to allow to use these `fallible` bindings.
It takes the form
```rs
let [element1, element2] = arraylike_object else {
// some diverging code!
}
```
The `diverging` path must exit the scope that the pattern is expanded into.
That may be done via `return`, or also a `panic`, as that exits the program and Rust is aware of that.
Because of that I've mostly placed `unreachable!()` in there, which causes a `panic`.
`unreachable!()` and also `dbg!()` are Macros.
Rust Macros are relatively easier to understand and debug in comparison to e.g. C macros, and they allow some nice things. For example `dbg!()` does println debugging on stereoides, as it doesnt just print the inserted value, but also the code-snippet it contained, and its concrete source location.
Rust allows to assign a lot of things to other thing, eg if-else, they are all `expressions`.
In particular code blocks delimited with `{}` can be placed anywhere (similar to C), but they can also be assigned as expressions.
In this case, and in general in functions, an element thats not followed with an `;` will be returned.
In functions this is something that is not required.
But `return` always returns from the currenct function, so in `{}`-delimited blocks return will not work, so this "implicit return" has to be used.
I use these blocks in some assignments, where I want to ensure that the stuff I wite in this block isnt by accident picked up elsewhere, but in particular I use these blocks extensively without assigning them.
If I do the latter chances are that these are tests performed for debugging.
This code is not strictly required to understand the data flow, though perhaps youll find them useful (I sure did) to know how the data actually looks like at this point.
For an full intro into Rust I recommend [the excellent and free Rust book](https://doc.rust-lang.org/stable/book/) and/or [rustlings excercises](https://github.com/rust-lang/rustlings) for practice.
## Polars
The only (direkt and with a broad API) dependency of this project is `polars`, a `pandas` alternative written in Rust, with a python API (used in the related projects).
It has a few features that `pandas` is lacking, such as support for streaming.
Sadly these specific features are not very mature yet.
Generally the API of `polars` seems not very stable at this point, and also very much oriented around the python API, which means it mostly doesn't utilize the niceties that Rusts strong type system can provide, while still requiring strong typing in other situations, which can make some things awkward.
I'm almost certain that I've used a number of APIs in ways that are not expected by the authors, but dont immediately fail, which makes it very hard to debug.
The result can be seen with some awkward code, and some very annoyed comments, sorry about that.
## Running the local code
The local code is defined in `./src/local.rs` and also utilizes stuff in `./src/lib.rs`
Ensure that all paths are correct (you'll get a runtime error otherwise) and execute
```sh
cargo run --release --bin local
```
`cargo` is Rusts official build system (aka CMake) and package manager (aka npm).
`--release` enables code optimizations.
`--bin local` refers to this section in the [`Cargo.toml`](Cargo.toml)
```toml
[[bin]]
name="local"
path="src/local.rs"
```
On first run this will download the dependencies and compile them. Because of the enabled performance optimizations (mostly for the cluster aggregation) this will take a long time, subsequent runs should have these cached.
## Running the Cluster code
Please consult the section for [the local code](#running-the-local-code) first for more information on whats happening.
The bwUniCluster cluster uses x86_64 CPUs and a Linux OS, thus we have to compile for that target:
```sh
cargo build --target x86_64-unknown-linux-gnu --release --bin cluster
```
Rust doesnt usually dynamically link against many libraries, but it does by default link against gnu C libraries (`libc`, `libgcc_s`, `libm`), as do most binaries.
Avoiding this with `musl` was investigated but caused a lot of other issues.
The bwUniCluster only has ancient versions of these around (from 4 years ago for `libc`), that are no longer supported by Rust, and I did not find an easy way to add/use a newer versions of these particular libraries on the cluster.
Because of that I ran the binary in a container that provides modern libraries.
The only container runtime provided on bwUniCluster is `enroot`, so this is what I've used.
Alternatively one could simply build the code on the cluster, but it doesn't have Rust tooling - so it would need a container anyways -, and compiler feedback would be annoying this way, youd have to build on both devices anyways do debug.
All the steps to execute the bwUniCluster Code are in [`execute_cluster.sh`](execute_cluster.sh) (refer to the [script in the model code](../../reimplement/cluster_push_and_queue.sh) for some comments).
(TODO: this is untested as written down, test before final submit)
\ No newline at end of file
enem_aggregate/execute_cluster.sh 0 → 100755
View file @ b6dd9af8
#!/bin/sh
LEGAL_EXEC_DIR='~/process_enem'
AUTHENTICATED_SSH_HOST="uniClusterIp"
cargo build --target x86_64-unknown-linux-gnu --release --bin cluster
ssh ${AUTHENTICATED_SSH_HOST} "mkdir -p ${LEGAL_EXEC_DIR}"
scp target/x86_64-unknown-linux-gnu/release/cluster slurm.sh ${AUTHENTICATED_SSH_HOST}:${LEGAL_EXEC_DIR}/
# TODO: revisit once the global project structure is established.
# compress and send over data.
# I strongly suggest compression to move the data,
# as that slimmed down the filesize from 40 GB to 8 GB for me (text files auch as csv are easy to compress).
# comment this out if the data is already there.
tar cafv official_data.tar.zst ../raw_data/enem/official_data/**/*.csv
scp official_data.tar.zst ${AUTHENTICATED_SSH_HOST}:${LEGAL_EXEC_DIR}/
ssh ${AUTHENTICATED_SSH_HOST} <<EOF_SSH
set -o errexit
cd ${LEGAL_EXEC_DIR}
# you may also comment this and the following line out if its not required.
enroot import docker://ubuntu
enroot create ubuntu
tar xvaf official_data.tar.zst
sbatch slurm.sh
exit
EOF_SSH
\ No newline at end of file
enem_aggregate/ingest_enem_data.py 0 → 100644
View file @ b6dd9af8
"""
DEPRECATED!
This module only remains for reference,
perhaps someone can/wants to do something with the thoughts
"""
import polars as pl
from dataclasses import dataclass
from common_py.utils import UNREACHABLE, deprecated
@dataclass
class IRT():
a: float
b: float
c: float
@deprecated()
def ingestData(path: str):
csv = pl.scan_csv(path)
csv: pl.LazyFrame = csv.rename({
# "column_0": "id",
"text": "question_and_answer_text",
"ano": "year",
"a": "irt_a",
"b": "irt_b",
"c": "irt_c",
"anulada": "annulled",
# see https://www.brazileducation.info/Tests/Higher-Education-Tests/enem-in-brazil.html
"area": "topic",
})
# Cleanup: remove leading newline, and leading space for following lines
csv = csv.with_columns(
pl.col("question_and_answer_text").str.replace_all("^\\n ", "")
).with_columns(
pl.col("question_and_answer_text").str.replace_all("\n ", "\n")
)
return csv
@deprecated("this was never done, aggregation was choosen instead, naively one would think less can go wrong there")
def recoverPValues(questionIrtParameters: list[IRT], targetMedianPValue: float):
"""
There is a non-linear mapping between IRT difficulty and the "p-value"
(correctness/ proportion of students that answer a question correct).
That may lead to issues when using IRT parameters as pretraining target
and finetuning on "p-value", if the model can not easily account for that non-linearity.
Thus this method can be used to calibrate IRT questions to a median "p-value",
effectively we optimize the students ability with the target being that the median of
all questions equals the target.
This assumes that the questions given are answered by one group,
which is unlikely to be correct. But it may be the lesser evil.
Not sure whether median or mean would be best here.
"""
UNREACHABLE("""
TODO if we reach that point in experimentation where we think that this might be helpful,
actually implement this, using eg https://docs.scipy.org/doc/scipy/tutorial/optimize.html#id50
IRT is differentiable, so newton approach is an option, we may also initialize student_ability with eg the mean or median of irt_difficulty
And provide the range of irt_difficulty as range if that helps.
Until that point we will assume that irt_difficulty as target is alright for pretraining
""")
if __name__ == "__main__":
data = ingestData("../raw_data/brasilian/with_corresponding/enem2.csv")
print(
# data.collect().describe(),
# data.head().collect(),
data
# .with_columns(
# pl.col("question_and_answer_text").str.lengths().alias("textlenght")
# )
.select(pl.col("^irt_.$"))
# .head()
.collect()
.describe(),
# data.select("topic").unique().collect(),
# .filter(pl.col("area") == pl.col("area").min())
)
\ No newline at end of file
enem_aggregate/slurm.sh 0 → 100644
View file @ b6dd9af8
#!/bin/sh
#SBATCH --partition=fat
#SBATCH --mail-type=ALL
#SBATCH --time=2:00:00
#SBATCH --mail-user=02masa1bif@hft-stuttgart.de
#SBATCH --output=%x_%A.log
#SBATCH --cpus-per-task=8
#SBATCH --mem=350gb
# cluster doesnt provide modern glibc variants, so I get a linker error, and I found no easy way to add that other than containers
enroot start -m "${PWD}:/workspace" ubuntu sh <<EOF
cd /workspace
./cluster
EOF
\ No newline at end of file
enem_aggregate/src/.gitignore 0 → 100644
View file @ b6dd9af8
melt_groupby_fail.rs
\ No newline at end of file
enem_aggregate/src/cluster.rs 0 → 100644
View file @ b6dd9af8
use jemallocator::Jemalloc;
// just followiing polars suggestions for speed, this is an untested presumed speedup.
#[global_allocator]
static GLOBAL: Jemalloc = Jemalloc;
use polars::{lazy::{dsl, frame::OptState}, prelude::{self as pl, LazyFileListReader as _, SerWriter as _, *}, functions::diag_concat_df};
use enem_aggregate::ENEM_QUESTION_ANSWER_OPT;
fn main() {
// DEV:
let root_dir = "./official_data";
// let root_dir = "../../../raw_data/brasilian/official_data";
let question_ident = [
"#question",
"topic",
"ordering_id",
"year",
].map(|elem| dsl::col(elem));
let mut interactions_eval = Vec::<pl::DataFrame>::new();
// DEV:
for year in 2009..=2021 {
dbg!(year);
let interactions = pl::LazyCsvReader
::new(format!("{root_dir}/{year}/DADOS/MICRODADOS_ENEM_*.csv"))
.with_delimiter(b';')
.with_encoding(pl::CsvEncoding::LossyUtf8)
.finish()
.unwrap();
let topics = [
"CN",
"CH",
"LC",
"MT",
];
let interactions = interactions.clone()
.with_optimizations(OptState{
common_subplan_elimination: false,
// doesnt actually work right now
streaming: true,
predicate_pushdown: true,
projection_pushdown: true,
slice_pushdown: true,
type_coercion: true,
// making this false causes a panic!
file_caching: true,
simplify_expr: true,
})
.select(
topics.into_iter()
.map(|topic| {
dsl::as_struct(&[
dsl::col(&format!("CO_PROVA_{topic}"))
.alias(&format!("ordering_id")),
dsl::col(&format!("TX_RESPOSTAS_{topic}"))
.alias(&format!("answers")),
]).alias(topic)
})
.chain([dsl::col("NU_INSCRICAO").alias("#student")])
.collect::<Vec<_>>()
)
.melt(pl::MeltArgs {
id_vars: vec!["#student".into()],
value_vars: topics.map(Into::into).to_vec(),
streamable: true,
variable_name: Some("topic".into()),
value_name: Some("interactions".into()),
})
.filter(dsl::col("interactions").struct_().field_by_name("ordering_id").is_null().not())
.unnest(["interactions"])
.explode(&[
dsl::col("answers"),
])
.groupby(&[
dsl::col("#student"),
dsl::col("topic")
])
.agg(&[
dsl::col("ordering_id").unique().first(),
dsl::col("answers"),
dsl::lit(year).alias("year"),
dsl::col("answers").cumcount(false).cast(pl::DataType::UInt8).alias("#question"),
])
.explode([
"answers",
"#question",
])
;
// dbg!(&question_ident);
let interactions = interactions
.sort_by_exprs(&question_ident, [false, false, false], true)
;
// dbg!(interactions.clone().fetch(5).unwrap());
let interactions_coll = interactions.clone()
.groupby(&question_ident)
.agg([
dsl::col("answers").value_counts(false, false),
])
// .sort_by_exprs(&join_cols, [false, false, false], true)
;
// dbg!(interactions_coll.clone()
// .fetch(5).unwrap())
// ;
interactions_eval.push(
interactions_coll.clone()
.with_columns(
ENEM_QUESTION_ANSWER_OPT.map(|row_title| {
dsl::col("answers").map(move |col| {
let accum_counts = col.list().unwrap().into_iter().map(|elements| {
let elements = elements.unwrap();
let elements = elements.struct_().unwrap();
let [choice, count] = elements.fields() else {unreachable!()};
let accum_choice = choice.utf8().unwrap().equal(row_title);
let count: i64 = count.u32().unwrap().filter(&accum_choice).unwrap().cast(&pl::DataType::Int64).unwrap().sum().unwrap();
count
});
Ok(Some(Series::from_iter(accum_counts)))
}, Default::default()).alias(&format!("#{row_title}_answers"))
})
)
.with_column(
dsl::col("answers").map(move |col| {
let accum_counts = col.list().unwrap().into_iter().map(|elements| {
let elements = elements.unwrap();
let elements = elements.struct_().unwrap();
let [_, count] = elements.fields() else {unreachable!()};
let count: i64 = count.u32().unwrap().cast(&pl::DataType::Int64).unwrap().sum().unwrap();
count
});
Ok(Some(Series::from_iter(accum_counts)))
}, Default::default()).alias("#total_answers")
)
.select(&[
"#question",
"topic",
"ordering_id",
"year",
// "correct_perc",
"#total_answers",
"^#._answers$",
].map(dsl::col))
.sort_by_exprs(&question_ident, [false, false, false], true)
// fetch here seems somewhat problematic, causes nulls which dissappear with a longer fetch
// DEV:
// .fetch(500).unwrap()
// also despite trying to only trigger collect() where it should not take up much memory,
// this does indeed end up taking faaar too much RAM (debugging with explain or to_dot above!)
.collect().unwrap()
);
// std::io::BufWriter::new(std::fs::File::create("polars.unopt.dot").unwrap()).write_all(
// interactions_eval.last().unwrap().clone()
// .sort_by_exprs(&join_cols, [false, false, false], true)
// .to_dot(false).unwrap().as_bytes()
// ).unwrap();
// println!("{}",
// interactions_eval.last().unwrap().clone()
// .with_streaming(true)
// .explain(true).unwrap()
// );
dbg!(interactions_eval.last().unwrap().clone());
}
let mut interactions_eval = diag_concat_df(&interactions_eval).unwrap();
dbg!(interactions_eval.clone());
pl::CsvWriter::new(std::fs::File::create("./clusterGen_interactions_agg.csv").unwrap()).finish(&mut interactions_eval).unwrap();
}
enem_aggregate/src/lib.rs 0 → 100644
View file @ b6dd9af8
pub const ENEM_QUESTION_ANSWER_OPT: [&str; 9] = [
// options
"A", "B", "C", "D", "E",
// duplicate selection
"*",
// no selection
".",
// item not presented
"9",
// i dont know what this does, isnt explained.
//MOST of the questions that have that as correct answer dont have IRT scores attached.
"X",
];
// pub trait ExprExt: Sized {
// /// On a groupby-context collect all entries for this col into a list of unique entries,
// /// then assert that that list only contains one element.
// ///
// /// If that passes, remove the wrapping list
// #[track_caller]
// fn assert_single_unique(self) -> Self;
// }
// impl ExprExt for dsl::Expr {
// #[track_caller]
// fn assert_single_unique(self) -> Self {
// let loc = std::panic::Location::caller();
// self.unique().apply(move |ser| {
// if ser.len() != 1 {
// panic!("
// Not a single unique element:
// {ser}
// original location:
// {loc}
// " );
// }
// Ok(Some(ser))
// }, Default::default()).first()
// }
// }
// pub trait ListNameSpaceExtension {
// #[track_caller]
// fn assert_one_element_list(self) -> Self;
// }
// impl ListNameSpaceExtension for dsl::ListNameSpace {
// #[track_caller]
// fn assert_one_element_list(self) -> Self {
// let loc = std::panic::Location::caller();
// self.lengths().list().min().eq(self.lengths().list().max()).all().first();
// (move |ser| {
// if ser.len() != 1 {
// panic!("\nNot a single element list:\n{ser}\noriginal location:\n{loc}\n\n");
// }
// Ok(Some(ser))
// }, Default::default()).list()
// }
// }
\ No newline at end of file
enem_aggregate/src/local.rs 0 → 100644
View file @ b6dd9af8
//! This file defines what is run locally.
use std::ops::Deref;
use polars::{lazy::dsl, prelude::{self as pl, LazyFileListReader as _, IntoLazy as _, SerWriter as _, NamedFromOwned, Literal}, series::IntoSeries};
use enem_aggregate::ENEM_QUESTION_ANSWER_OPT;
fn main() {
let mut _test_csv = pl::CsvWriter::new(std::fs::File::create("./GENERATED/localGen_test.csv").unwrap());
let question_meta = parse_official_question_meta("../raw_data/enem/official_data");
// {
// let mut laaa = question_meta.clone().lazy().filter(dsl::col("year").eq(dsl::lit(2017))).collect().unwrap();
// print!("{laaa}");
// _test_csv.finish(&mut laaa).unwrap();
// }
let marinho_question_and_answer_text = parse_marinho_enem_data("../raw_data/enem/plaintext_questions_and_irt/enem.csv");
let interactions_agg = pl::LazyCsvReader::new("./GENERATED/clusterGen_interactions_agg.csv").finish().unwrap();
let mut result_csv = pl::CsvWriter::new(std::fs::File::create("GENERATED/localGen_result.csv").unwrap());
let mut result_csv_no_secrets = pl::CsvWriter::new(std::fs::File::create("GENERATED/localGen_result_no_secrets.csv").unwrap());
// These elements together uniquely identify a question (at least in the data we had).
let question_meta_and_interact = combine_official_question_meta_and_aggregated_interaction_data(question_meta, interactions_agg);
dbg!(question_meta_and_interact.clone().collect().unwrap());
let question_meta_and_interact = add_idomatic_ordering(question_meta_and_interact);
let question_and_interact = combine_official_and_aggregated_with_marinho(question_meta_and_interact, marinho_question_and_answer_text);
dbg!(question_and_interact.clone().collect().unwrap());
let question_and_interact = question_and_interact.clone()
.with_column((dsl::col("#correct_answers").cast(pl::DataType::Float64) / dsl::col("#total_answers")).alias("correctness"))
.select(&[
"topic",
"qname",
"question_and_answer_text",
"question_id",
"correctness",
"^.*irt.*$",
"anulled",
"#total_answers",
].map(dsl::col))
;
dbg!(question_and_interact.clone().collect().unwrap());
result_csv.finish(&mut question_and_interact.clone().collect().unwrap()).unwrap();
result_csv_no_secrets.finish(&mut question_and_interact.clone().select(&[dsl::all().exclude(["^irt_._marinho$", "question_and_answer_text"])]).clone().collect().unwrap()).unwrap();
// TODO: ENEM and Marinho IRT parameters dont match. At all.
// which is kind of expected, Marinho describes explicitly that he recovers these parameters
// And Marinho parameters kindof make sense: they are of a similar order of magnitude as the capability of students
// (Its hard to identify, but they most likely used either NU_NOTA_REDACAO - writing ability in the writing test before - , OR the resulting grade NU_NOTA_{TOPIC})
// None of these capabilities explicitly mention IRT in their description (at least the ones I looked at, 2009 + 2016)
// These parameters should be able to differ without outright conflicting, assuming a different(ly scaled) student ability:
// Since the difference between ability and difficulty (irt_b) is scaled with the differentiation (irt_a),
// if differentiation is scaled down with a factor, and ability and difficulty are scaled up with the same factor, the result is the same
// But they dont seem to scale like that!
// the influence of c is also there, and combined with the numerical optimization this might explain some discrepancy
// Add to this, that the IRT params of the question items are probably just the ones determined on the pretesting, while Marinho regressed on the actual application...
// FIXME: The thing is, this means I cant really use the difficulty that marinho calculated in my work, can I? I have not created them myself, and I dont have the formula available.
// best thing I can do is hope that they didnt mess up in terms of correctness, but also, at least the connected bits to this are also just not reproducible, should I use marinho difficulty
// FIXME: Newer developement: Marinho contains multiple questions, that were "abandoned" (IN_ITEM_ABAN, see excel) from ENEM(INEP) as they didnt allow IRT to converge on their side.
// these dont have any INEP IRT score attached (but marinho)
// FIXME: irt_c should always be the same with ENEM, as we've always got 5 choices, so random chance is the same. But NEITHER INEP nor Marinho have them converge even to a roughtly similar size for all questions
// according to wikipedia that ought to be p(-inf) = c for that same formula, so 1/5 = 0.2
// Analyzing data:
/*
│ describe ┆ irt_a ┆ irt_b ┆ irt_c ┆ irt_a_marinho ┆ irt_b_marinho ┆ irt_c_marinho │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════════╪══════════╪══════════╪══════════╪═══════════════╪═══════════════╪═══════════════╡
│ count ┆ 1754.0 ┆ 1754.0 ┆ 1754.0 ┆ 1754.0 ┆ 1754.0 ┆ 1754.0 │
│ null_count ┆ 28.0 ┆ 28.0 ┆ 28.0 ┆ 1.0 ┆ 1.0 ┆ 1.0 │
│ mean ┆ 2.129609 ┆ 1.57606 ┆ 0.163521 ┆ 0.021735 ┆ 653.794005 ┆ 0.141196 │
│ std ┆ 0.941571 ┆ 1.852909 ┆ 0.068605 ┆ 0.012599 ┆ 117.705627 ┆ 0.080214 │
│ min ┆ 0.01929 ┆ -2.10568 ┆ 0.00083 ┆ 0.000605 ┆ 325.0 ┆ 0.01 │
│ max ┆ 8.00279 ┆ 61.05459 ┆ 0.5 ┆ 0.1 ┆ 1010.0 ┆ 0.34 │
│ median ┆ 2.01504 ┆ 1.46911 ┆ 0.16333 ┆ 0.0192985 ┆ 652.673 ┆ 0.148987 │
│ 25% ┆ 1.47905 ┆ 0.8782 ┆ 0.12074 ┆ 0.0139272 ┆ 569.096 ┆ 0.0935926 │
│ 75% ┆ 2.69234 ┆ 2.04605 ┆ 0.20359 ┆ 0.0259177 ┆ 729.578 ┆ 0.195858 │
*/
// neither Marino nor INEP irt_c look great. Marinho is more contained without quite so large outliers, but mean and median as well as standard deviation are worse
// FIXME: irt_b Marinho vs INEP from my work doesnt look like his paper AT ALL.
// INEP irt_b also has LARGE outliers, which I checked back in the original data and were also present in regularly used questionaires (I mean in commonly used CO_PROVA)
// these questions were also not to my knowledge canceled, and were asked in years (2013 or 2016 i beliefe) where no questions were canceled according to Marinho
// TODO: Visual inspection of INEP IRT_b vs Marinho IN HIS PAPER has high symmetry of window - and shown data seems symmatric in mean - , suggesting that INEP scores were linearly transformed to same scope
// Wikipedia suggests looking at ability (and thus irt_b!) as a normal distribution, and plots show 'mean' ability being 0.
// It might make sense to center like that for numerical stability with floats during regression
// this would make it good practice, Marinho might not have done that. But with the numerical ranges I've seen Marinho should still be just fine
// Update: Mean of difficulty for INEP is not 0, its 1.8. Doesnt fit whith them having transformed it, unless that tranformation was calibrated on much less able people(?) beforehand
// pl::CsvWriter::new(std::fs::File::create("test.csv").unwrap()).finish(&mut res.clone()
// .select(&["question_id",/* "question_ident",*/ "idomatic_ordering_ident"].map(dsl::col))
// .explode(&["idomatic_ordering_ident"].map(dsl::col))
// .unnest(["idomatic_ordering_ident"])
// .sort_by_exprs(&[dsl::col("year"), dsl::col("idomatic_placement_all_topics")], &[false, false], false)
// .collect().unwrap()).unwrap();
// let enem = pl::LazyCsvReader
// ::new(format!("../../../raw_data/brasilian/with_corresponding/enem2.csv")).finish().unwrap()
// .select([dsl::col("qname")])
// .filter(dsl::col("qname").str().starts_with(dsl::lit("ENEM02")).not())
// .sort("qname", Default::default())
// ;
// dbg!(enem.collect().unwrap());
}
fn combine_official_and_aggregated_with_marinho(question_meta_and_interact: pl::LazyFrame, marinho_data: pl::LazyFrame) -> pl::LazyFrame {
let question_meta_and_interact = question_meta_and_interact.with_column((
// determine the identifier as provided by Marinho
dsl::lit("ENEM")
+ dsl::when(dsl::col("year").eq(dsl::lit(2020))).then(dsl::lit("1")).otherwise(dsl::lit("0"))
+ dsl::col("year").cast(pl::DataType::Utf8)
+ dsl::col("idomatic_placement_all_topics")
.map(|series| {
let iter = series.i64().unwrap().into_iter().map(|el| {
let el: i64 = el.unwrap();
format!("Q{el:04}")
});
Ok(Some(pl::Series::from_iter(iter)))
}, dsl::GetOutput::from_type(pl::DataType::Utf8))
).alias("qname"))
.sort("qname", Default::default());
dbg!(question_meta_and_interact.clone().collect().unwrap());
let qname_join = [dsl::col("qname")];
return question_meta_and_interact
.join(marinho_data, &qname_join, &qname_join, pl::JoinType::Left);
}
fn add_idomatic_ordering(question_meta_and_interact: pl::LazyFrame) -> pl::LazyFrame {
let idomatic_order_id = {
// The "idomatic" order is the yellow regular ordering.
// AFAICT there is no programmatic, somewhat easy to follow and reliable way to find that in the CSVs.
let mut idomatic_order_id = std::collections::HashMap::new();
for ((year, topic), co_prova) in [
((2009, "CN"), 50),
((2009, "CH"), 54),
((2009, "LC"), 57),
((2009, "MT"), 61),
((2010, "CN"), 90),
((2010, "CH"), 86),
((2010, "LC"), 93),
((2010, "MT"), 97),
((2011, "CN"), 122),
((2011, "CH"), 118),
((2011, "LC"), 125),
((2011, "MT"), 129),
((2012, "CN"), 142),
((2012, "CH"), 138),
((2012, "LC"), 145),
((2012, "MT"), 149),
((2013, "CN"), 172),
((2013, "CH"), 168),
((2013, "LC"), 175),
((2013, "MT"), 179),
((2014, "CN"), 200),
((2014, "CH"), 196),
((2014, "LC"), 203),
((2014, "MT"), 207),
((2015, "CN"), 236),
((2015, "CH"), 232),
((2015, "LC"), 239),
((2015, "MT"), 243),
((2016, "CN"), 292),
((2016, "CH"), 296),
((2016, "LC"), 300),
((2016, "MT"), 304),
((2017, "CN"), 392),
((2017, "CH"), 396),
((2017, "LC"), 400),
((2017, "MT"), 404),
((2018, "CN"), 448),
((2018, "CH"), 452),
((2018, "LC"), 456),
((2018, "MT"), 460),
((2019, "CN"), 504),
((2019, "CH"), 508),
((2019, "LC"), 512),
((2019, "MT"), 516),
((2020, "CN"), 598),
((2020, "CH"), 568),
((2020, "LC"), 578),
((2020, "MT"), 588),
((2021, "CN"), 910),
((2021, "CH"), 880),
((2021, "LC"), 890),
((2021, "MT"), 900),
] {
idomatic_order_id.insert((year, topic), co_prova);
}
idomatic_order_id
};
let question_meta_and_interact = question_meta_and_interact
// .filter(dsl::col("total_answers").is_not_null())
.with_column(
// all this gigant mess does is add a struct row,
// that contains the year, and what would be considered the "idomatic" placement of this question
// (so the placement in the regular yellow test)
dsl::col("question_ident").map(move |arg| {
let arg = arg.list().unwrap().into_iter().map(|list| {
let list = list.unwrap();
let list = list.struct_().unwrap();
let (year, id_pl_all_top): (Vec<_>, Vec<_>) = list.into_iter()
.filter_map(|el| {
let [
ordering_id,
topic,
year,
idomatic_placement_all_topics,
idomatic_placement
] = el else {unreachable!()};
// I would be able to combine this pattern with the above, but then I get less fine grained "errors" (unreachable triggering)
let pl::AnyValue::Int64(ordering_id) = ordering_id else {unreachable!()};
let pl::AnyValue::Int64(year) = year else {unreachable!()};
let topic = topic.get_str().unwrap();
// I sadly didnt properly document this beforehand,
// But I was either very annoyed or sad. (probably both).
// I THINK this clause was required due to polars behavior
// if one does use fetch with some specific operations, presumable because theye manipulate row numbers (=> the thing fetch "subsamples").
// in that case some things end up with nulls.
// I did not find this behavior documented anywhere in the official polars documentation.
//
// Because I and will want to continue to be able to test this code on stuff generaded by th cluster code with fetch "subsampling"
// I had to add this line.
//
// Take all of this with a pinch of salt!
let pl::AnyValue::Int64(mut idomatic_placement_all_topics) = idomatic_placement_all_topics else {
// dbg!(el);
return None;
};
let pl::AnyValue::Int64(idomatic_placement) = idomatic_placement else {unreachable!()};
// DATA_ERROR: in 2017 they decided NOT to provide the total order in
// CO_POSTACIO, instead its the per-topic placement
if *year == 2017 {
assert_ne!(topic, "LC", "If we handle LC we need some more special casing for that!");
let ( _, topic_start_placement) = [("LC", 1), ("CH", 46), ("CN", 91), ("MT", 136)]
.into_iter()
.find(|(pot_topic, _)| *pot_topic == topic).unwrap()
;
idomatic_placement_all_topics = topic_start_placement + idomatic_placement;
}
// dbg!(idomatic_order_co_prova.get(&dbg!((*year, topic))).unwrap());
// dbg!(ordering_id);
if idomatic_order_id.get(&(*year, topic)).unwrap() != ordering_id {return None};
Some((*year, idomatic_placement_all_topics))
})
.unzip()
;
// what do i even name these things for if polars is just going to throw that away like a wet paper towel, and give me ununderstandable errors.
pl::StructChunked::new("year_and_idomatic_placement", &[
pl::Series::from_vec("year", year),
pl::Series::from_vec("idomatic_placement_all_topics", id_pl_all_top),
]).unwrap().into_series()
});
let arg = pl::ListChunked::from_iter(arg).into_series();
Ok(Some(arg))
}, Default::default()).alias("year_and_idomatic_placement")
);
dbg!(question_meta_and_interact.clone().collect().unwrap());
let question_meta_and_interact = question_meta_and_interact.clone()
.filter(dsl::col("year_and_idomatic_placement").list().lengths().neq(dsl::lit(0)))
.with_column(dsl::col("year_and_idomatic_placement").list().first())
.drop_columns(&["year"])
.unnest(["year_and_idomatic_placement"])
// without this the folowing code wont work.... thanks polars! You only wasted about another 1.5h of my time.
.collect().unwrap().lazy()
;
question_meta_and_interact
}
fn parse_marinho_enem_data(enem_question_and_answer_text_path: &str) -> pl::LazyFrame {
let question_bodies = pl::LazyCsvReader
::new(enem_question_and_answer_text_path).finish().unwrap()
.select(&
[
dsl::col("qname"),
dsl::col("anulada").alias("anulled"),
dsl::col("text").alias("question_and_answer_text"),
].into_iter().chain(
["a", "b", "c"].map(|irt_param| dsl::col(irt_param).alias(&format!("irt_{irt_param}_marinho")))
).collect::<Vec<_>>()
)
;
dbg!(question_bodies.clone().collect().unwrap());
question_bodies
}
fn combine_official_question_meta_and_aggregated_interaction_data(
question_meta: pl::DataFrame,
interactions_agg: pl::LazyFrame,
) -> pl::LazyFrame {
let question_ident = [
"#question",
"topic",
"ordering_id",
"year",
].map(|elem| dsl::col(elem));
dbg!(question_meta.clone());
let question_meta = question_meta.lazy()
.with_column(dsl::col("year").cast(pl::DataType::Int64))
.groupby(&question_ident)
.agg([
// these should all be unique! I've tested that with past values.
dsl::col("correct_answer").unique().first(),
dsl::col("question_id").unique().first(),
dsl::col("#question_all_topics").unique().first(),
// TODO: this doesnt work correctly, unlike previous things. I have NO idea why not, dont ask...
// I have validated that theres only a single unique parameter with manual checks
// dsl::col("^irt_.$").assert_single_unique(),
dsl::col("^irt_.$").unique().first(),
// dsl::col("^irt_.$").unique().list().lengths().map_alias(|old| Ok(format!("{old}_len"))),
])
// .with_column(dsl::col("^irt_.$").list().lengths())
// .sort("irt_c", Default::default())
.sort_by_exprs(
&question_ident,
[false, false, false, false],
true,
)
.collect().unwrap()
.lazy()
;
dbg!(question_meta.clone().collect().unwrap());
// test_csv.finish(&mut question_meta.clone().collect().unwrap()).unwrap();
// TODO: try reversing
let question_meta_and_interact = interactions_agg.clone()
.join(
question_meta.clone(),
&question_ident,
&question_ident,
pl::JoinType::Left,
)
.groupby(&[
"question_id",
"topic",
].map(dsl::col))
.agg(&[
dsl::col("year").unique(),
dsl::as_struct(&[
dsl::col("ordering_id"),
dsl::col("topic"),
dsl::col("year"),
dsl::col("#question_all_topics"),
dsl::col("#question"),
]).alias("question_ident"),
dsl::col("correct_answer").unique().first(),
dsl::col("^irt_.$").unique().first(),
dsl::col("^#._answers$").sum(),
dsl::col("#total_answers").sum(),
])
.with_column(
// add a column for the "correct" answers
match_colname(
dsl::col("correct_answer"),
&ENEM_QUESTION_ANSWER_OPT
.map(|option|
(option, dsl::col(&format!("#{option}_answers"))
)),
pl::Null{}.lit(),
).alias("#correct_answers"),
)
;
question_meta_and_interact
}
/// This represents an equivalent of the match expression (aka switch-case) for polars dsl.
///
/// Without this one would have it being even more verbose and difficulty to read.
fn match_colname(match_on: dsl::Expr, cases: &[(&str, dsl::Expr)], otherwise: dsl::Expr) -> dsl::Expr {
let mut expr = otherwise;
for (case, then_expr) in cases.into_iter() {
expr = dsl::when(match_on.clone().eq(dsl::lit(case.deref()))).then(then_expr.clone()).otherwise(expr);
};
expr
}
/// This parses the official metadata about the questions (`/DADOS/ITENS_PROVA_*.csv`).
/// It mostly does validation, and then transformations, first on the year specific data:
/// * renames of rows into clearer names
/// * removes problematic datapoints
/// * adds some datapoints
/// * such as question year, determined from file location
/// * as well as #question (quesgion number in its respective topic)
///
/// Then it aggregates the data of all years together.
fn parse_official_question_meta(root_dir: &str) -> pl::DataFrame {
// in 2013 they added a special "ledor" edition,
// that seems to have had different questions in the same positions (CO_POSICAO),
// while keeping the same ordering_id (CO_PROVA).
let forbidden_editions = [
188,
187,
189,
190,
];
let mut question_meta = Vec::new();
for year in 2009..=2021 {
// dbg!(year);
let provas = pl::LazyCsvReader
::new(format!("{root_dir}/{year}/DADOS/ITENS_PROVA_*.csv"))
// dont ask me why...
.with_delimiter(if year != 2021 {b';'} else {b','})
.with_encoding(pl::CsvEncoding::LossyUtf8)
.finish()
.unwrap()
.filter(dsl::col("CO_PROVA").is_in(dsl::lit(pl::Series::from_iter(forbidden_editions))).not())
;
// dbg!(provas.clone().collect().unwrap());
let questions = provas.clone()
.select(&[
dsl::col("CO_ITEM").alias("question_id"),
dsl::col("TX_GABARITO").alias("correct_answer"),
dsl::col("SG_AREA").alias("topic"),
dsl::col("NU_PARAM_A").alias("irt_a"), // aka differential (not named this way for consistency with data from other sources)
dsl::col("NU_PARAM_B").alias("irt_b"), // aka difficulty
dsl::col("NU_PARAM_C").alias("irt_c"), // aka guess
dsl::col("TX_COR").alias("color"),
dsl::col("CO_POSICAO").alias("#question_all_topics"),
dsl::col("CO_PROVA").alias("ordering_id"),
])
.with_columns([
dsl::col("color").str().to_lowercase(),
dsl::lit(year).alias("year"),
])
// filter out weirdness of language sectionin said topic:
// It may have questions with the same index but different languages,
// also the different language may be confusing to non-multilanguage LMs
.filter(dsl::col("topic").neq(dsl::lit("LC")))
;
{
let unique_per_question = ["correct_answer", "topic", "irt_a", "irt_b", "irt_c"];
let questions = questions.clone()
.groupby([dsl::col("question_id")])
.agg(unique_per_question.map(|col| dsl::col(col).unique()))
.collect().unwrap()
;
for unique_field in unique_per_question {
for (idx, entry) in questions.clone().column(unique_field).unwrap().iter().enumerate() {
let pl::AnyValue::List(entry) = entry else {unreachable!()};
if entry.len() != 1 {
dbg!(questions.clone().get(idx));
// DATA_ERROR: Spotted an error?
// CO_ITEM = 58542 has different answer SPECIFICALLY for TX_COR = ROSA
assert_eq!(entry.len(), 1);
}
}
}
}
// dbg!(questions.clone().collect().unwrap());
let questions = questions
.groupby(&[
dsl::as_struct(&[
dsl::col("year"),
dsl::col("topic"), // duplicated with ordering_id, but this is helpful for processing the interactions
dsl::col("#question_all_topics"),
dsl::col("ordering_id"),
]).alias("occurence_data"),
])
;
{
let unique_per_occurence = ["color", "question_id"];
let questions_1 = questions.clone()
.agg(unique_per_occurence.map(|col| dsl::col(col).unique()))
;
let questions = questions_1.clone()
.collect().unwrap()
;
// dbg!(questions_1.clone().filter(dsl::col("question_id").list().lengths().neq(dsl::lit(1))).collect().unwrap());
// DATA_ISSUE: In 2013 they break another rule.
// They did a special "Ledor" edition of a random color per topic,
// where they have different questions in the same spots for the same ordering_id.
// This is now fixed with filtering out these questions.
for unique_field in unique_per_occurence {
for (idx, entry) in questions.clone().column(unique_field).unwrap().iter().enumerate() {
let pl::AnyValue::List(entry) = entry else {unreachable!()};
if entry.len() != 1 {
dbg!("Repetition of {unique_field}!");
dbg!(questions.clone().get(idx));
assert_eq!(entry.len(), 1);
}
}
}
}
let questions = questions.clone()
.agg(&[
dsl::as_struct(&[
dsl::col("question_id"),
dsl::col("correct_answer"),
dsl::col("^irt_.*$"),
]).alias("question_meta"),
])
;
{
let questions = questions.clone().collect().unwrap();
for (idx, entry) in questions.clone().column("question_meta").unwrap().iter().enumerate() {
let pl::AnyValue::List(entry) = entry else {unreachable!()};
if entry.len() != 1 {
dbg!("Repetition of combined question identifier!");
dbg!(questions.clone().get(idx));
assert_eq!(entry.len(), 1);
}
}
}
let questions = questions.with_column(dsl::col("question_meta").list().first());
// dbg!(questions.clone()
// .unnest([
// "occurence_data",
// "question_meta",
// ])
// .collect().unwrap()
// );
let questions_by_ordering = questions.clone()
.unnest([
"occurence_data",
// "question_meta",
])
.sort_by_exprs(
[dsl::col("#question_all_topics")],
[false], false
)
.groupby([
dsl::col("ordering_id")
])
.agg([
dsl::as_struct(&[
dsl::col("ordering_id"),
(
dsl::col("#question_all_topics") - dsl::col("#question_all_topics").min()
).alias("#question"),
dsl::col("topic"),
dsl::col("year"),
dsl::col("#question_all_topics"),
]).alias("occurence_data"),
dsl::col("question_meta"),
])
// already present in occurence_data
.drop_columns(["ordering_id"])
;
question_meta.push(questions_by_ordering.clone());
// dbg!(questions_by_ordering.clone().collect().unwrap());
}
let question_meta = pl
::diag_concat_lf(
question_meta,
true,
false,
)
.unwrap()
.explode([
dsl::col("occurence_data"),
dsl::col("question_meta"),
])
// FIXME: for some reason this + select panics...
// .unnest([
// "occurence_data",
// "question_meta",
// ])
// .select(&[
// dsl::col("topic"),
// dsl::col("#question"),
// dsl::col("ordering_id"),
// dsl::col("year"),
// dsl::col("correct_answer"),
// dsl::col("question_id"),
// dsl::col("#question_all_topics"),
// dsl::col("^irt_.$"),
// ])
.select(&[
dsl::col("occurence_data").struct_().field_by_name("topic"),
dsl::col("occurence_data").struct_().field_by_name("#question"),
dsl::col("occurence_data").struct_().field_by_name("ordering_id"),
dsl::col("occurence_data").struct_().field_by_name("year"),
dsl::col("occurence_data").struct_().field_by_name("#question_all_topics"),
// FIXME: Hotfix for a really weird error when accessing fields 1. by name in any way 2. by any number other than 0
dsl::col("question_meta").struct_().rename_fields(vec!["question_id".into(), "correct_answer".into(), "irt_a".into(), "irt_b".into(), "irt_c".into()]).struct_().field_by_name("correct_answer"),
dsl::col("question_meta").struct_().rename_fields(vec!["question_id".into(), "correct_answer".into(), "irt_a".into(), "irt_b".into(), "irt_c".into()]).struct_().field_by_name("question_id"),
dsl::col("question_meta").struct_().rename_fields(vec!["question_id".into(), "correct_answer".into(), "irt_a".into(), "irt_b".into(), "irt_c".into()]).struct_().field_by_name("irt_a"),
dsl::col("question_meta").struct_().rename_fields(vec!["question_id".into(), "correct_answer".into(), "irt_a".into(), "irt_b".into(), "irt_c".into()]).struct_().field_by_name("irt_b"),
dsl::col("question_meta").struct_().rename_fields(vec!["question_id".into(), "correct_answer".into(), "irt_a".into(), "irt_b".into(), "irt_c".into()]).struct_().field_by_name("irt_c"),
])
.collect().unwrap()
;
dbg!(question_meta.clone());
{
// more assertions to catch gotchas
// DATA_ERROR: Glad I did that. It seems question_id also isnt really unique. Theres 1 case
// question_id/CO_ITEM = 60227
// appears in 2009 in MT and 2017 in CN
let questions_by_q = question_meta.clone().lazy()
.groupby([dsl::col("question_id")])
.agg([
dsl::col("topic").unique(),
dsl::col("#question"),
dsl::col("year").unique(),
dsl::col("correct_answer").unique(),
dsl::col("ordering_id"),
])
.collect().unwrap()
;
let unique = [
"topic",
// "year",
"correct_answer",
];
for unique_field in unique {
for (idx, entry) in questions_by_q.clone().column(unique_field).unwrap().iter().enumerate() {
let pl::AnyValue::List(entry) = entry else {unreachable!()};
if entry.len() != 1 {
// Do consider that every repetition will result in 2 errors ;-)
dbg!("Repetition of CO_ITEM!");
dbg!(idx);
dbg!(questions_by_q.clone().get(idx));
}
}
}
}
question_meta
}
generate_repos_snapshot.sh 0 → 100644
View file @ b6dd9af8
#!/bin/sh
set -o errexit
set -o xtrace
# Hardcoded directory path.
# ensure this doesnt get run in the wrong directory, otherwise it could fuck up my repos
target_directory="/home/smaier/workspace/hft/BA/meta/monorepo"
if [ "$(pwd)" != "$target_directory" ]; then
echo "Execution directory is not equal to the target directory."
exit 1
fi
# "remove" old repo
rm -rf .git
cd ../..
# my filesystem is btrfs. Its a copy on write filesystem.
# Because of that it copies the 50GB of enem raw data just like that, glorious thing.
cp -r common_py data_explore enem_aggregate moodle_extract qde_model_code raw_data README.md meta/monorepo
cd meta/monorepo || exit
rm -rf ./**/.git
git init --initial-branch=main
git add .
git commit -m "Update snapshot"
git remote add origin ssh://git@gitlab.rz.hft-stuttgart.de:54321/02masa1bif/pretrained_qde_closed_q.git
set +o xtrace
printf "\n\nNow push up with:\n"
echo 'git push --force --set-upstream origin main'
\ No newline at end of file
moodle_extract/.gitignore 0 → 100644
View file @ b6dd9af8
__pycache__
experiment.py
.*_cache
\ No newline at end of file
moodle_extract/README.md 0 → 100644
View file @ b6dd9af8
# Parsing HFT Questions and mapping them to a more generic format
This contains code that parses the generated moodle Question XMLs, then maps them to Python dataclasses ([dataclasses with parts of the xml parsing](./moodle_questions_dataclasses.py), [the code that uses these dataclasses and parses a file](./extract_moodle_xml.py)).
Then other code in here takes these Dataclasses and maps them to a more generic format.
Dataloss is inevitable in that step, thus its separated to be able to iterate.
[This is that code](./moodle_to_generic_questions.py)
Then, because I dont want to execute that stuff on the cluster (that would be a nightmare), these questions end up in a csv file together with the experimental information.
The mapping of questions to strings (csv doesnt support nested data, questions have answers -> nesting) is done in [this file](./map_generic_question_to_str.py).
Note that during the simplification when mapping to the generic format, answers for some questions are already mapped into strings eariler as part of the simplification (eg "select the correct word here" type of quesrions).
The reading of the experimental data and the writing of the csv [happens here](./combine_hft_data.py).
\ No newline at end of file
moodle_extract/combine_hft_data.py 0 → 100644
View file @ b6dd9af8
"""
This should probably be your main entrypoint, as this, when choosen as entry point,
will go through through the raw data provided and generate `./GENERATED/result.csv`,
which contains the questions alongside all their raw data from moodle and the csv file.
"""
from moodle_to_generic_questions import GenericQuestion
from map_generic_question_to_str import *
import polars as pl
import fuzzywuzzy.process as fuzzyProc
def combineQuizXmlAndExperimentalData(
questionsDataList: list[GenericQuestion],
experimentalData: pl.LazyFrame,
):
questionsData: pl.LazyFrame = pl.DataFrame(questionsDataList).lazy()
questionsData2: pl.LazyFrame = questionsData.with_columns([
# Could be made more efficient with .select call before collect
pl.col("name").apply(lambda item: fuzzyProc.extractOne(item, experimentalData.collect()["title"])[0], return_dtype=pl.datatypes.Utf8).alias("title"),
pl.Series([len(genericQuestion.answerOptions) for genericQuestion in questionsDataList]).alias("answer_count"),
pl.Series([mapGenericQuestionToStr(genericQuestion) for genericQuestion in questionsDataList]).alias("question_and_answer_text"),
pl.Series([mapGenericQuestionToQuestionStr(genericQuestion) for genericQuestion in questionsDataList]).alias("question_text"),
pl.Series([mapGenericQuestionToAnswerStr(genericQuestion) for genericQuestion in questionsDataList]).alias("answer_text"),
])
questionsData3: pl.LazyFrame = questionsData2.join(experimentalData, on="title")
# questionsData4: pl.LazyFrame = questionsData3.with_columns([
# pl.struct(["name", "title"]).apply(lambda row: fuzz.ratio(row["name"], row["title"])).alias("similarity"),
# ])
# questionsData5: pl.LazyFrame = questionsData4.sort(pl.col("similarity"), descending=False)
# questionsData6: pl.LazyFrame = questionsData3.select(["name", "questionText", "answerOptions", "mean_share_total_points"])
return (questionsData3
.select([
"lecture",
"retries_allowed",
"#quiz_participants",
"mean_share_total_points",
"questionType",
"answer_count",
"^.*_text$",
])
)
if __name__ == "__main__":
from extract_moodle_xml import extractQuestionsFromMoodleQuizXml
from moodle_to_generic_questions import mapToGenericQuestionFormat
df = combineQuizXmlAndExperimentalData(
mapToGenericQuestionFormat(extractQuestionsFromMoodleQuizXml(
map(lambda subject: (subject, f"../raw_data/hft/{subject}/quiz.moodle.xml"), ["ASV", "KI", "MLDM", "PGM_1_2"])
)),
pl.read_csv("../raw_data/hft/experimental_data_extract.csv").lazy(),
).collect()
print(df)
df.write_csv("./GENERATED/result.csv")
# df.write_csv("../qde_model_code/sources/hft_prepared.csv")
# print(df.filter((pl.col("name").str.contains(r"Regression.*"))).head().collect())
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