From fbe3a656ee83b8966a808f6f020900cfa611e6f7 Mon Sep 17 00:00:00 2001 From: R-Palazzo <116157184+R-Palazzo@users.noreply.github.com> Date: Thu, 16 Nov 2023 10:51:32 -0600 Subject: [PATCH] Update the synthetic data that's available for the single-table demo (#520) --- sdmetrics/demos/single_table/synthetic.csv | 432 +++++++++--------- .../single_table/_properties/test_boundary.py | 11 +- .../_properties/test_column_pair_trends.py | 16 +- .../_properties/test_column_shapes.py | 10 +- .../single_table/_properties/test_coverage.py | 10 +- .../_properties/test_data_validity.py | 11 +- .../single_table/test_diagnostic_report.py | 25 +- .../single_table/test_quality_report.py | 36 +- 8 files changed, 271 insertions(+), 280 deletions(-) diff --git a/sdmetrics/demos/single_table/synthetic.csv b/sdmetrics/demos/single_table/synthetic.csv index a3231929..0a20f8fc 100644 --- a/sdmetrics/demos/single_table/synthetic.csv +++ b/sdmetrics/demos/single_table/synthetic.csv @@ -1,216 +1,216 @@ 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-77,F,79.87556058397956,62.53443870308387,Commerce,79.70322578910474,Comm&Mgmt,False,1,72.90974803438294,Mkt&HR,55.620489740363766,24776.0,True,2020-03-14,2021-01-04,12.0 -78,M,69.85863110299482,96.91276990887434,Science,62.44015136633962,Comm&Mgmt,True,0,71.45553179044673,Mkt&Fin,71.67908951707415,42343.0,True,2020-01-27,2021-02-04,12.0 -79,M,79.76448763697219,70.45003373602843,Arts,82.86736437040133,Comm&Mgmt,False,0,60.36958998406182,Mkt&HR,60.20222342643217,23102.0,True,2020-07-21,2021-03-06, -80,M,87.89250123440986,47.31837087155028,Science,79.66420528398358,Others,False,0,77.32989221841434,Mkt&Fin,63.89381754646252,,False,,, -81,M,77.05023401866376,77.32812069192194,Commerce,82.88916221152039,Comm&Mgmt,False,0,75.21055205821375,Mkt&Fin,51.54071871475651,30668.0,True,,2020-08-04,12.0 -82,M,71.72590756573044,95.31188295557284,Science,78.71251271577358,Sci&Tech,False,0,64.51918705050713,Mkt&HR,58.300695996199025,,False,,, -83,M,64.8133109470379,55.43251963651731,Science,69.83269113080587,Sci&Tech,False,0,68.89130861608642,Mkt&HR,82.44123385973982,,False,,, -84,F,70.42293273882265,69.94943224474352,Commerce,71.07250898503887,Comm&Mgmt,False,0,41.95256373073564,Mkt&Fin,71.7331070913308,27721.0,True,2020-01-12,2020-06-26,3.0 -85,F,51.84730620331001,77.70302164426343,Commerce,80.20165566046533,Comm&Mgmt,True,0,45.45055309958289,Mkt&Fin,71.29421290207956,22357.0,True,2020-02-08,2020-06-23,3.0 -86,M,43.72150095914541,88.78134085228403,Commerce,60.292891980639766,Comm&Mgmt,False,2,99.73863263170861,Mkt&Fin,66.80601136125763,31417.0,True,2020-02-13,2020-03-29,12.0 -87,M,59.607454917206226,74.51528068334491,Science,59.35544522826958,Sci&Tech,True,0,73.54276318786881,Mkt&Fin,61.914774004835586,26509.0,True,2020-01-07,2020-11-28,3.0 -88,M,37.91663762604209,67.81329543308996,Science,61.68345043671977,Comm&Mgmt,True,0,55.52023221877088,Mkt&Fin,68.13999114854722,22556.0,True,2020-01-25,2020-08-28,6.0 -89,F,42.24315335161267,81.38906955065443,Commerce,77.6333025106548,Sci&Tech,False,0,41.00889814712363,Mkt&Fin,49.07608111836168,,False,,, -90,F,55.87111154990734,101.32175112438586,Science,64.2780446688074,Comm&Mgmt,False,0,72.20336143546933,Mkt&Fin,67.63542454131485,32058.0,True,,,3.0 -91,M,44.409397848256894,82.83736469499706,Commerce,69.00564342338168,Comm&Mgmt,False,0,57.081894603763644,Mkt&HR,56.041909439848084,26726.0,False,2020-03-10,,3.0 -92,M,39.76924772110114,92.32769527090608,Science,62.142813958630065,Comm&Mgmt,False,0,87.58102927012517,Mkt&Fin,62.87216216740642,29906.0,True,2020-02-02,2020-09-02,6.0 -93,M,74.62648965644928,87.25024777468482,Commerce,87.384880833599,Comm&Mgmt,False,0,78.63888061186054,Mkt&HR,81.90673733955727,,False,,, -94,M,67.40728540799238,82.8976659452566,Commerce,84.91477064829152,Comm&Mgmt,True,1,63.70888191104017,Mkt&Fin,74.01990596319344,23834.0,True,2020-02-06,2020-04-06,6.0 -95,M,55.40160567868055,104.85639163342788,Commerce,85.67667496787205,Comm&Mgmt,False,0,81.7724696958017,Mkt&HR,66.34224488761907,,False,,, -96,M,47.9096347079047,102.09185423636289,Science,81.24814895623935,Comm&Mgmt,False,1,64.64478181905095,Mkt&HR,56.27252447826821,22837.0,True,2019-12-24,2020-04-23,12.0 -97,M,74.62806739134805,57.91825171482823,Science,70.97322403571498,Comm&Mgmt,False,0,67.33853491534387,Mkt&HR,55.691234515986274,26241.0,False,2020-01-29,2020-09-02,6.0 -98,M,50.2151550089016,100.79601599146645,Commerce,69.30807470153795,Others,False,0,52.27966258861038,Mkt&HR,71.4437377482899,,False,,, -99,M,51.12865836666642,80.15210850791001,Commerce,72.66683658773721,Comm&Mgmt,False,0,70.03642330017524,Mkt&Fin,63.24511058878459,,False,,, -100,F,78.30500837805829,80.15730438238901,Science,72.1577652346942,Comm&Mgmt,False,0,48.854727652340806,Mkt&HR,53.88673838319084,,False,,, -101,M,60.21335992054437,72.65190923582088,Arts,51.651952987994854,Comm&Mgmt,True,0,51.800494098635426,Mkt&HR,60.087759218875085,28732.0,True,2020-02-07,2020-06-02,6.0 -102,F,60.196557001393515,82.40325044930272,Commerce,81.86950315821628,Comm&Mgmt,False,0,86.53814914609497,Mkt&HR,55.21744243136246,,True,,, -103,M,58.61630871650663,96.75156265968248,Commerce,69.84988600559662,Comm&Mgmt,False,0,73.17921273013529,Mkt&HR,64.95445116571491,,False,,, -104,M,47.11701443007432,70.75840989785425,Science,56.555129615151216,Comm&Mgmt,True,0,95.03984203605661,Mkt&Fin,65.31672405706131,21263.0,True,2020-02-16,2021-03-05,3.0 -105,M,53.32779346724624,72.11477556106314,Commerce,82.50363275118784,Sci&Tech,True,1,51.20180976715983,Mkt&Fin,66.80363037833145,27566.0,True,2020-01-19,2020-12-27,3.0 -106,M,43.62840367272835,72.23379123461447,Commerce,67.23932751825497,Comm&Mgmt,True,0,42.16713960893108,Mkt&Fin,58.99467073520916,30714.0,True,2019-12-24,2020-11-17,3.0 -107,M,51.24654004669398,78.65287376733544,Science,70.91240831986399,Others,False,0,58.99321545641066,Mkt&HR,65.72434715327739,,False,2020-02-18,2020-07-06,12.0 -108,M,43.25349578787207,76.17619859481653,Science,49.07142535295526,Sci&Tech,True,0,74.01478457459001,Mkt&Fin,50.525541996939864,29308.0,True,2020-02-02,2020-09-29,6.0 -109,M,53.30364847623592,67.7036925812489,Arts,77.53170025338795,Sci&Tech,False,0,62.53436669765429,Mkt&Fin,58.342026472939466,,False,,, -110,F,54.31819967330902,72.78149102839835,Science,62.186762323157744,Comm&Mgmt,True,0,58.98222308708009,Mkt&HR,76.10276966757313,28866.0,True,2020-01-30,2021-02-03,12.0 -111,M,48.268028664903,65.71166708135694,Science,66.27987820848624,Comm&Mgmt,False,0,64.06918530092308,Mkt&Fin,59.36802639510086,29284.0,True,2020-01-08,2020-06-22,3.0 -112,M,57.79090627411189,106.63215632991489,Commerce,76.82337621339653,Comm&Mgmt,False,0,70.35304335158068,Mkt&HR,67.5928444493027,28055.0,True,2020-02-10,2020-12-14,3.0 -113,M,68.15192209346867,74.14146555033632,Arts,88.77824100572825,Comm&Mgmt,True,0,53.31832263176982,Mkt&HR,70.54800559909788,,False,2020-01-08,,6.0 -114,M,36.57495456664636,86.24088743212535,Commerce,78.29587968045432,Comm&Mgmt,True,0,44.06677245284883,Mkt&Fin,58.287585844403324,29979.0,True,2020-01-08,2020-06-03,6.0 -115,M,62.41473433657513,75.15280665775282,Science,58.20040244582717,Comm&Mgmt,True,0,69.01130585346445,Mkt&HR,55.39454414128404,22768.0,True,2020-01-06,2020-09-02,6.0 -116,M,72.89854320431252,89.07878685391753,Commerce,80.89629494604628,Sci&Tech,False,0,66.10746742318231,Mkt&HR,50.6144113301142,30304.0,True,,,6.0 -117,M,67.38815728077131,76.78799566355313,Science,79.70493302402451,Sci&Tech,False,0,63.509723369234145,Mkt&HR,77.51334784944065,,True,2020-02-15,2020-09-03, -118,M,83.14852274957693,64.32876215183572,Arts,59.38537209775024,Sci&Tech,False,0,62.370931027438694,Mkt&HR,60.836354760838816,,False,,, -119,M,63.58246704697633,89.66931135330185,Commerce,64.2814263938173,Others,False,1,46.58981593878096,Mkt&Fin,74.55272725517082,,True,,2020-08-17,12.0 -120,M,58.8938844231883,43.22388735873488,Science,60.149855710883486,Comm&Mgmt,False,0,69.6422839492482,Mkt&HR,60.33462380850782,30965.0,True,2020-01-21,,6.0 -121,F,63.891064729557215,62.49535583384627,Commerce,61.97801766311359,Comm&Mgmt,True,0,52.78965680902587,Mkt&HR,60.42613556940799,25499.0,True,2020-01-05,2020-08-12,6.0 -122,F,50.46696332333795,81.06326363237211,Science,76.26874265987038,Comm&Mgmt,False,0,88.57856520234093,Mkt&HR,56.88818579544358,,False,,, -123,M,41.26727429648551,84.86959455542643,Commerce,79.40315571599932,Comm&Mgmt,True,0,58.92154112470752,Mkt&Fin,57.2854261133821,22726.0,True,2020-01-22,2020-05-10,6.0 -124,M,59.6837551750306,75.41246692284014,Commerce,60.46193961365872,Comm&Mgmt,False,0,73.59600095865935,Mkt&Fin,44.26811359984342,,False,,,3.0 -125,M,63.86973541978515,81.93277090387149,Science,70.9045730441496,Sci&Tech,True,1,53.80000615438599,Mkt&Fin,59.01962165260227,27377.0,True,2020-01-10,2020-12-29,3.0 -126,F,74.30907961673977,92.81851649328428,Commerce,71.96253235206417,Comm&Mgmt,True,2,60.19149064890496,Mkt&Fin,53.523465868011705,51006.0,True,2020-01-28,2020-06-09,3.0 -127,M,52.83653223312068,93.88797742160801,Commerce,84.097574000634,Sci&Tech,False,0,63.82964707171799,Mkt&Fin,54.852791359492294,30489.0,True,,2020-08-16,6.0 -128,M,41.89068450415549,69.43297282105496,Commerce,88.80005094569482,Comm&Mgmt,False,0,53.41675318480129,Mkt&Fin,58.83995894953317,,False,,,12.0 -129,M,46.15132045331971,67.55320954407551,Commerce,78.50437873093674,Comm&Mgmt,False,0,70.10010111619017,Mkt&Fin,54.12158143334393,,False,,, -130,M,57.770298151897364,72.31372652874833,Arts,67.8672949859608,Comm&Mgmt,False,0,77.83811955552025,Mkt&HR,77.87058718496812,,False,2020-03-29,, -131,M,72.76672147438566,89.14062677532381,Science,73.42049027892845,Comm&Mgmt,True,1,66.49403776354615,Mkt&HR,74.77303181656525,30285.0,True,2020-01-14,2020-09-29,12.0 -132,M,67.34174151747112,86.54774977499466,Commerce,71.5861614447421,Comm&Mgmt,True,0,76.49872569522822,Mkt&Fin,63.78156457467996,29781.0,True,2020-01-16,2020-10-18,12.0 -133,M,51.414877344438054,74.61304330293879,Commerce,90.97632153669417,Comm&Mgmt,True,0,83.01088201832013,Mkt&Fin,82.22775306952383,25248.0,True,2020-02-20,2021-01-05,12.0 -134,F,69.38264498306876,39.693554010312084,Commerce,77.76353151444066,Comm&Mgmt,False,0,58.60429539496239,Mkt&HR,68.45806238948776,28174.0,True,2019-12-30,2020-10-02,6.0 -135,F,69.15038329284793,71.89674525680581,Arts,61.907772674388696,Sci&Tech,False,0,86.65258507304986,Mkt&HR,55.36253965784777,,True,2020-03-12,2020-08-13, -136,M,51.710503658985395,107.54969869675926,Science,75.85543302941123,Sci&Tech,True,0,56.21757736848865,Mkt&Fin,83.75491587756768,27048.0,True,2020-08-13,2021-01-28,6.0 -137,M,86.21700481436409,80.33707829238244,Commerce,71.11773844649835,Comm&Mgmt,False,0,74.02751647313555,Mkt&Fin,58.65254853275083,,False,,,6.0 -138,F,39.65990305312977,109.91103311021865,Commerce,85.84240564795769,Comm&Mgmt,False,0,77.66579434933632,Mkt&HR,66.26497522890267,,False,,, -139,F,66.94062716972086,96.55479961540169,Commerce,74.1035252989897,Sci&Tech,False,0,78.88923031434474,Mkt&HR,64.42877781779775,,False,,, -140,M,58.177253920908356,74.04257763411897,Science,95.7780141541463,Comm&Mgmt,False,0,61.95689131041939,Mkt&Fin,72.92644045814704,,False,,, -141,M,41.458899973913034,88.59993652591778,Commerce,69.2190030567862,Comm&Mgmt,False,0,61.433033810913365,Mkt&Fin,55.012188002213136,26715.0,True,2020-02-01,2020-10-08, -142,F,65.20323295450142,82.02161240179399,Commerce,95.52818602992664,Comm&Mgmt,False,0,65.54147516266576,Mkt&HR,70.47290051283377,,False,,, -143,F,50.23809801617039,66.39838472342295,Science,72.29876668005411,Sci&Tech,True,0,87.91605429759228,Mkt&HR,65.90820711458153,27882.0,True,2020-01-10,2020-07-04, -144,M,54.738113544229066,107.70209345645833,Science,70.92827576869655,Comm&Mgmt,True,0,100.40030196254386,Mkt&Fin,76.71705176356802,29550.0,True,2020-01-20,2020-12-12,3.0 -145,M,44.97452567343419,95.59469325126459,Commerce,78.05488555609966,Comm&Mgmt,False,0,66.54448135790837,Mkt&HR,59.71000463132267,,False,,,6.0 -146,M,48.28744090008715,91.60543202578043,Commerce,66.69454516688373,Comm&Mgmt,True,1,75.25865820016953,Mkt&Fin,60.91981350709154,28214.0,True,2020-02-14,2021-01-02,3.0 -147,M,57.39150367337949,91.93145360353654,Commerce,66.88810863200982,Comm&Mgmt,False,0,71.69462864556479,Mkt&HR,71.8297972044766,,False,,, -148,M,37.87459165014509,81.3894042373949,Science,64.45613606959668,Sci&Tech,True,0,49.59512494451232,Mkt&HR,56.42179137489089,,False,2019-12-28,2020-08-14,12.0 -149,M,39.59263372923444,108.3041561268148,Commerce,76.50909747614743,Sci&Tech,True,0,59.89905504224804,Mkt&Fin,57.64152735921523,22462.0,True,2020-08-27,2020-12-03,3.0 -150,F,52.5427847715677,63.571569815100005,Commerce,59.948560067657226,Comm&Mgmt,True,0,55.111378452483095,Mkt&Fin,77.06262206177989,31748.0,True,2020-02-05,2020-10-18,3.0 -151,F,65.78797204250851,67.01380528055594,Commerce,66.20166758721363,Comm&Mgmt,False,0,75.74110098836047,Mkt&HR,59.69325521997545,,False,,, -152,M,80.18650186551194,95.70584216910576,Science,75.42675726787563,Sci&Tech,False,0,70.46640090278589,Mkt&HR,71.30693574739847,,False,,, -153,F,51.19686988123621,104.77787291084037,Arts,77.98474700609508,Comm&Mgmt,False,0,71.50446180928057,Mkt&HR,49.41550128054096,,False,,, -154,M,78.67034329674004,80.76532457145477,Commerce,81.27455082268793,Comm&Mgmt,False,0,69.26299616149578,Mkt&HR,56.21712515189408,,False,,, -155,M,54.32118140092705,91.20799949938689,Commerce,76.88638582812088,Sci&Tech,True,1,75.88068641774063,Mkt&Fin,57.20953017516935,26461.0,True,2020-01-13,2020-12-09,12.0 -156,F,91.99437226625123,85.2713371763002,Commerce,68.7743691783782,Sci&Tech,False,0,103.4591495305631,Mkt&HR,57.55296505193053,42087.0,True,2019-12-16,2020-08-18, -157,M,66.35498765946119,79.09687276356857,Commerce,77.2764902042783,Comm&Mgmt,False,0,58.01561183603927,Mkt&Fin,59.03489217287453,25560.0,True,2020-07-17,2020-11-29,3.0 -158,F,53.33935340901259,77.80867721619384,Commerce,61.81093030048872,Sci&Tech,False,0,76.78900591891161,Mkt&Fin,75.71817928811905,,False,,,6.0 -159,M,56.14208981236698,71.83329000338004,Science,83.5195702979838,Comm&Mgmt,False,0,77.84907839108229,Mkt&HR,75.76840266054484,,False,,, -160,M,54.93185847709436,95.37844172358209,Commerce,66.48471286690372,Others,False,0,57.45768966322291,Mkt&Fin,58.8635514985935,,False,,, -161,M,73.05567386992963,66.37184552799057,Commerce,60.949472557436906,Comm&Mgmt,False,0,66.10407864957166,Mkt&Fin,62.60779624756706,19715.0,True,2020-01-02,2020-03-30, -162,M,48.22956847409175,78.03565929457572,Arts,75.11240926403389,Comm&Mgmt,True,0,68.72189229139936,Mkt&Fin,59.96297893396941,,False,,, -163,M,72.10228007316647,59.78303530618349,Commerce,72.3215825047536,Sci&Tech,True,1,68.41731252361153,Mkt&Fin,68.05346466027348,33156.0,True,2020-02-12,2021-02-22,3.0 -164,M,55.2861746174668,90.37120437284186,Science,69.48255080784212,Comm&Mgmt,False,0,55.83171919768507,Mkt&HR,65.645651607342,,False,,, -165,F,40.75838588791687,68.6384896860059,Science,73.75938992008344,Comm&Mgmt,True,0,62.77339904186398,Mkt&Fin,65.94962057972191,23415.0,True,2019-12-18,2020-06-22,3.0 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b/tests/integration/reports/single_table/_properties/test_boundary.py index 7a6f3c3f..ef22e1c6 100644 --- a/tests/integration/reports/single_table/_properties/test_boundary.py +++ b/tests/integration/reports/single_table/_properties/test_boundary.py @@ -17,19 +17,14 @@ def test_get_score(self): score = boundary_property.get_score(real_data, synthetic_data, metadata) # Assert - assert score == 0.9172655676537751 - + assert score == 1.0 expected_details = pd.DataFrame({ 'Column': [ 'start_date', 'end_date', 'salary', 'duration', 'high_perc', 'second_perc', 'degree_perc', 'experience_years', 'employability_perc', 'mba_perc' ], 'Metric': ['BoundaryAdherence'] * 10, - 'Score': [ - 0.8503937007874016, 0.8615384615384616, 0.9444444444444444, 1.0, - 0.8651162790697674, 0.9255813953488372, 0.9441860465116279, 1.0, - 0.8883720930232558, 0.8930232558139535 - ] + 'Score': [1.0] * 10 }) pd.testing.assert_frame_equal(boundary_property.details, expected_details) @@ -64,4 +59,4 @@ def test_get_score_error(self): assert error_messages[0] == expected_message_1 assert error_messages[1] == expected_message_2 assert error_messages[2] == expected_message_3 - assert score == 0.9270636340403783 + assert score == 1.0 diff --git a/tests/integration/reports/single_table/_properties/test_column_pair_trends.py b/tests/integration/reports/single_table/_properties/test_column_pair_trends.py index 7068c362..93e45178 100644 --- a/tests/integration/reports/single_table/_properties/test_column_pair_trends.py +++ b/tests/integration/reports/single_table/_properties/test_column_pair_trends.py @@ -42,19 +42,19 @@ def test_get_score(self): 'ContingencySimilarity', 'ContingencySimilarity', 'ContingencySimilarity' ], 'Score': [ - 0.9854510263003199, 0.586046511627907, 0.6232558139534884, 0.7348837209302326, - 0.6976744186046512, 0.8976744186046511 + 0.9187918131436303, 0.6744186046511629, 0.7162790697674419, 0.813953488372093, + 0.772093023255814, 0.9348837209302325 ], 'Real Correlation': [ 0.04735340044317632, np.nan, np.nan, np.nan, np.nan, np.nan ], 'Synthetic Correlation': [ - 0.07645134784253645, np.nan, np.nan, np.nan, np.nan, np.nan + -0.11506297326956302, np.nan, np.nan, np.nan, np.nan, np.nan ] } expected_details = pd.DataFrame(expected_details_dict) pd.testing.assert_frame_equal(column_shape_property.details, expected_details) - assert score == 0.754164318336875 + assert score == 0.8050699533533958 def test_get_score_warnings(self, recwarn): """Test the ``get_score`` method when the metrics are raising erros for some columns.""" @@ -90,7 +90,7 @@ def test_get_score_warnings(self, recwarn): # Assert details = column_shape_property.details pd.testing.assert_series_equal(details['Error'], exp_error_serie, check_names=False) - assert score == 0.7023255813953488 + assert score == 0.7751937984496124 def test_only_categorical_columns(self): """Test the ``get_score`` method when there are only categorical columns.""" @@ -119,12 +119,12 @@ def test_only_categorical_columns(self): ], 'Metric': ['ContingencySimilarity'] * 6, 'Score': [ - 0.8883720930232558, 0.9023255813953488, 0.7767441860465116, 0.9348837209302325, - 0.8883720930232558, 0.8976744186046511 + 0.9209302325581395, 0.9627906976744186, 0.6837209302325581, 0.9302325581395349, + 0.9255813953488372, 0.9348837209302325 ], 'Real Correlation': [np.nan] * 6, 'Synthetic Correlation': [np.nan] * 6 } expected_details = pd.DataFrame(expected_details_dict) pd.testing.assert_frame_equal(column_shape_property.details, expected_details) - assert score == 0.8813953488372093 + assert score == 0.8930232558139535 diff --git a/tests/integration/reports/single_table/_properties/test_column_shapes.py b/tests/integration/reports/single_table/_properties/test_column_shapes.py index cc986b1a..a1a08952 100644 --- a/tests/integration/reports/single_table/_properties/test_column_shapes.py +++ b/tests/integration/reports/single_table/_properties/test_column_shapes.py @@ -28,13 +28,15 @@ def test_get_score(self): 'TVComplement' ], 'Score': [ - 0.701107, 0.768919, 0.869155, 0.826051, 0.553488, 0.902326, 0.995349, 0.627907, - 0.939535, 0.627907, 0.916279, 0.800000, 0.781395, 0.841860, 0.972093, 0.925581 + 0.6621621621621622, 0.849290780141844, 0.8531399046104928, 0.43918918918918914, + 0.8976744186046511, 0.9860465116279069, 0.986046511627907, 0.8976744186046511, + 1.0, 0.9162790697674419, 0.9906976744186047, 0.3441860465116279, + 0.9348837209302325, 0.9255813953488372, 0.9953488372093023, 0.9395348837209302 ] } expected_details = pd.DataFrame(expected_details_dict) pd.testing.assert_frame_equal(column_shape_property.details, expected_details) - assert score == 0.8155594899871002 + assert score == 0.8511084702797364 def test_get_score_errors(self): """Test the ``get_score`` method when the metrics are raising errors for some columns.""" @@ -65,4 +67,4 @@ def test_get_score_errors(self): assert column_names_nan == ['start_date', 'employability_perc'] assert error_messages[0] == expected_message_1 assert error_messages[1] == expected_message_2 - assert score == 0.8261749908947813 + assert score == 0.858620688670242 diff --git a/tests/integration/reports/single_table/_properties/test_coverage.py b/tests/integration/reports/single_table/_properties/test_coverage.py index 536497bd..fd83ef17 100644 --- a/tests/integration/reports/single_table/_properties/test_coverage.py +++ b/tests/integration/reports/single_table/_properties/test_coverage.py @@ -16,7 +16,7 @@ def test_get_score(self): score = coverage_property.get_score(real_data, synthetic_data, metadata) # Assert - assert score == 0.9419212095491987 + assert score == 0.896792056025647 expected_details = pd.DataFrame({ 'Column': [ @@ -31,8 +31,10 @@ def test_get_score(self): 'RangeCoverage', 'RangeCoverage', 'CategoryCoverage', 'CategoryCoverage' ], 'Score': [ - 1.0, 1.0, 0.42333783783783785, 1.0, 0.9807348482826732, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 0.6666666666666667, 1.0, 1.0, 1.0, 1.0 + 0.9952153110047847, 0.9554140127388535, 0.45462162162162156, + 0.7777777777777778, 0.928171334431631, 1.0, 1.0, 0.9659863945578232, + 1.0, 1.0, 1.0, 0.33333333333333337, 0.9943749999999998, 0.943778110944528, + 1.0, 1.0 ] }) @@ -64,4 +66,4 @@ def test_get_score_error(self): assert column_names_nan == ['start_date', 'employability_perc'] assert error_messages[0] == expected_message_1 assert error_messages[1] == expected_message_2 - assert score == 0.9336242394847984 + assert score == 0.8827916132432548 diff --git a/tests/integration/reports/single_table/_properties/test_data_validity.py b/tests/integration/reports/single_table/_properties/test_data_validity.py index b92f69b7..0d8ccbbc 100644 --- a/tests/integration/reports/single_table/_properties/test_data_validity.py +++ b/tests/integration/reports/single_table/_properties/test_data_validity.py @@ -30,16 +30,11 @@ def test_get_score(self): 'BoundaryAdherence', 'BoundaryAdherence', 'BoundaryAdherence', 'CategoryAdherence', 'CategoryAdherence' ], - 'Score': [ - 0.8503937007874016, 0.8615384615384616, 0.9444444444444444, - 1.0, 1.0, 0.8651162790697674, 1.0, 1.0, 0.9255813953488372, - 1.0, 0.9441860465116279, 1.0, 1.0, 0.8883720930232558, - 0.8930232558139535, 1.0, 1.0 - ] + 'Score': [1.0] * 17 } expected_details = pd.DataFrame(expected_details_dict) pd.testing.assert_frame_equal(data_validity_property.details, expected_details) - assert score == 0.9513326868551618 + assert score == 1.0 def test_get_score_errors(self): """Test the ``get_score`` method when the metrics are raising errors for some columns.""" @@ -70,4 +65,4 @@ def test_get_score_errors(self): assert column_names_nan == ['start_date', 'employability_perc'] assert error_messages[0] == expected_message_1 assert error_messages[1] == expected_message_2 - assert score == 0.9622593255151395 + assert score == 1.0 diff --git a/tests/integration/reports/single_table/test_diagnostic_report.py b/tests/integration/reports/single_table/test_diagnostic_report.py index 3e33e99f..0fb7b470 100644 --- a/tests/integration/reports/single_table/test_diagnostic_report.py +++ b/tests/integration/reports/single_table/test_diagnostic_report.py @@ -21,7 +21,7 @@ def test_get_properties(self): expected_frame = pd.DataFrame( { 'Property': ['Data Validity', 'Data Structure'], - 'Score': [0.951333, 1.0] + 'Score': [1.0, 1.0] } ) pd.testing.assert_frame_equal(properties_frame, expected_frame) @@ -38,7 +38,7 @@ def test_get_score(self): # Assert - assert result == 0.975666343427581 + assert result == 1.0 def test_get_score_with_no_verbose(self): """Test the ``get_score`` method works when verbose=False.""" @@ -51,7 +51,7 @@ def test_get_score_with_no_verbose(self): result_dict = report.get_score() # Assert - assert result_dict == 0.975666343427581 + assert result_dict == 1.0 def test_end_to_end(self): """Test the end-to-end functionality of the diagnostic report.""" @@ -78,9 +78,8 @@ def test_end_to_end(self): 'CategoryAdherence' ], 'Score': [ - 0.8503937007874016, 0.8615384615384616, 0.9444444444444444, 1.0, 1.0, - 0.8651162790697674, 1.0, 1.0, 0.9255813953488372, 1.0, 0.9441860465116279, 1.0, - 1.0, 0.8883720930232558, 0.8930232558139535, 1.0, 1.0 + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0 ] }) @@ -129,9 +128,8 @@ def test_generate_with_object_datetimes(self): 'CategoryAdherence' ], 'Score': [ - 0.8503937007874016, 0.8615384615384616, 0.9444444444444444, 1.0, 1.0, - 0.8651162790697674, 1.0, 1.0, 0.9255813953488372, 1.0, 0.9441860465116279, - 1.0, 1.0, 0.8883720930232558, 0.8930232558139535, 1.0, 1.0 + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0 ] }) @@ -160,9 +158,9 @@ def test_generate_multiple_times(self): report = DiagnosticReport() report.generate(real_data, synthetic_data, metadata, verbose=False) - assert report.get_score() == 0.975666343427581 + assert report.get_score() == 1.0 report.generate(real_data, synthetic_data, metadata) - assert report.get_score() == 0.975666343427581 + assert report.get_score() == 1.0 def test_get_details_with_errors(self): """Test the ``get_details`` function of the diagnostic report when there are errors.""" @@ -190,9 +188,8 @@ def test_get_details_with_errors(self): 'CategoryAdherence' ], 'Score': [ - 0.8503937007874016, 0.8615384615384616, 0.9444444444444444, 1.0, 1.0, - 0.8651162790697674, 1.0, 1.0, np.nan, 1.0, 0.9441860465116279, 1.0, 1.0, - 0.8883720930232558, 0.8930232558139535, 1.0, 1.0 + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, np.nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0 ], 'Error': [ None, None, None, None, None, None, None, None, diff --git a/tests/integration/reports/single_table/test_quality_report.py b/tests/integration/reports/single_table/test_quality_report.py index b21e8df5..b882129f 100644 --- a/tests/integration/reports/single_table/test_quality_report.py +++ b/tests/integration/reports/single_table/test_quality_report.py @@ -87,7 +87,7 @@ def test_report_end_to_end(self): 'Column': ['start_date', 'second_perc', 'work_experience', 'degree_type'], 'Metric': ['KSComplement', 'KSComplement', 'TVComplement', 'TVComplement'], 'Score': [ - 0.7011066184294531, 0.627906976744186, 0.9720930232558139, 0.9255813953488372 + 0.6621621621621622, 0.8976744186046511, 0.9953488372093023, 0.9395348837209302 ], } @@ -105,14 +105,14 @@ def test_report_end_to_end(self): 'ContingencySimilarity', 'ContingencySimilarity', 'ContingencySimilarity' ], 'Score': [ - 0.9854510263003199, 0.586046511627907, 0.6232558139534884, 0.7348837209302326, - 0.6976744186046512, 0.8976744186046511 + 0.9187918131436303, 0.6744186046511629, 0.7162790697674419, 0.813953488372093, + 0.772093023255814, 0.9348837209302325 ], 'Real Correlation': [ 0.04735340044317632, np.nan, np.nan, np.nan, np.nan, np.nan ], 'Synthetic Correlation': [ - 0.07645134784253645, np.nan, np.nan, np.nan, np.nan, np.nan + -0.11506297326956302, np.nan, np.nan, np.nan, np.nan, np.nan ] } expected_details_column_shapes = pd.DataFrame(expected_details_column_shapes_dict) @@ -124,7 +124,7 @@ def test_report_end_to_end(self): pd.testing.assert_frame_equal( report.get_details('Column Pair Trends'), expected_details_cpt ) - assert report.get_score() == 0.7804181608907237 + assert report.get_score() == 0.8393750143888287 report_info = report.get_info() assert report_info == report.report_info @@ -167,7 +167,7 @@ def test_quality_report_with_object_datetimes(self): 'Column': ['start_date', 'second_perc', 'work_experience', 'degree_type'], 'Metric': ['KSComplement', 'KSComplement', 'TVComplement', 'TVComplement'], 'Score': [ - 0.7011066184294531, 0.627906976744186, 0.9720930232558139, 0.9255813953488372 + 0.6621621621621622, 0.8976744186046511, 0.9953488372093023, 0.9395348837209302 ], } @@ -185,14 +185,14 @@ def test_quality_report_with_object_datetimes(self): 'ContingencySimilarity', 'ContingencySimilarity', 'ContingencySimilarity' ], 'Score': [ - 0.9854510263003199, 0.586046511627907, 0.6232558139534884, 0.7348837209302326, - 0.6976744186046512, 0.8976744186046511 + 0.9187918131436303, 0.6744186046511629, 0.7162790697674419, 0.813953488372093, + 0.772093023255814, 0.9348837209302325 ], 'Real Correlation': [ 0.04735340044317632, np.nan, np.nan, np.nan, np.nan, np.nan ], 'Synthetic Correlation': [ - 0.07645134784253645, np.nan, np.nan, np.nan, np.nan, np.nan + -0.11506297326956302, np.nan, np.nan, np.nan, np.nan, np.nan ] } expected_details_column_shapes = pd.DataFrame(expected_details_column_shapes_dict) @@ -204,7 +204,7 @@ def test_quality_report_with_object_datetimes(self): pd.testing.assert_frame_equal( report.get_details('Column Pair Trends'), expected_details_cpt ) - assert report.get_score() == 0.7804181608907237 + assert report.get_score() == 0.8393750143888287 def test_report_end_to_end_with_errors(self): """Test the quality report end to end with errors in the properties computation.""" @@ -229,7 +229,7 @@ def test_report_end_to_end_with_errors(self): expected_details_column_shapes_dict = { 'Column': ['start_date', 'second_perc', 'work_experience', 'degree_type'], 'Metric': ['KSComplement', 'KSComplement', 'TVComplement', 'TVComplement'], - 'Score': [0.7011066184294531, np.nan, 0.9720930232558139, 0.9255813953488372], + 'Score': [0.6621621621621622, np.nan, 0.9953488372093023, 0.9395348837209302], 'Error': [ None, "TypeError: '<' not supported between instances of 'str' and 'float'", @@ -252,7 +252,7 @@ def test_report_end_to_end_with_errors(self): 'ContingencySimilarity', 'ContingencySimilarity', 'ContingencySimilarity' ], 'Score': [ - np.nan, 0.586046511627907, 0.6232558139534884, np.nan, np.nan, 0.8976744186046511 + np.nan, 0.6744186046511629, 0.7162790697674419, np.nan, np.nan, 0.9348837209302325 ], 'Real Correlation': [np.nan] * 6, 'Synthetic Correlation': [np.nan] * 6, @@ -274,7 +274,7 @@ def test_report_end_to_end_with_errors(self): pd.testing.assert_frame_equal( report.get_details('Column Pair Trends'), expected_details_cpt ) - assert report.get_score() == 0.7842929635366918 + assert report.get_score() == 0.8204378797402054 def test_report_with_column_nan(self): """Test the report with column full of NaNs.""" @@ -307,7 +307,7 @@ def test_report_with_column_nan(self): 'KSComplement', 'KSComplement', 'TVComplement', 'TVComplement', 'KSComplement' ], 'Score': [ - 0.7011066184294531, 0.627906976744186, 0.9720930232558139, 0.9255813953488372, + 0.6621621621621622, 0.8976744186046511, 0.9953488372093023, 0.9395348837209302, np.nan ], 'Error': [ @@ -334,16 +334,16 @@ def test_report_with_column_nan(self): 'ContingencySimilarity' ], 'Score': [ - 0.9854510263003199, 0.586046511627907, 0.6232558139534884, np.nan, - 0.7348837209302326, 0.6976744186046512, np.nan, 0.8976744186046511, - 0.9720930232558139, 0.9255813953488372 + 0.9187918131436303, 0.6744186046511629, 0.7162790697674419, np.nan, + 0.813953488372093, 0.772093023255814, np.nan, 0.9348837209302325, + 0.9953488372093023, 0.9395348837209302 ], 'Real Correlation': [ 0.04735340044317632, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan ], 'Synthetic Correlation': [ - 0.07645134784253645, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, + -0.11506297326956302, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan ], 'Error': [