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privacykit
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added pitman risk, and refactored some code

Browse Source
pull/2/head
Steve L. Nyemba 6 years ago
parent 0b16ce94cc
commit 0239b0ab42
5 changed files with 174 additions and 2503 deletions
Show Stats Download Patch File Download Diff File

501
src/out/risk_xoi.csv
Unescape View File

@ -1,501 +0,0 @@
,group_count,row_count,marketer,prosecutor,field_count
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0,70637864,79080802,0.893236565810246,1,39
0,71991707,79080802,0.910356308728381,1,47
0,31350943,79080802,0.396441894962067,1,41
0,2149480,79080802,0.027180806790503,1,12
0,218978,79080802,0.002769041214327,1,8
0,1313636,79080802,0.016611313577725,1,21
0,10096939,79080802,0.127678763298329,1,25
0,122997,79080802,0.001555333240045,1,7
0,289845,79080802,0.003665175272249,1,26
0,12310155,79080802,0.155665530554432,1,19
0,17545402,79080802,0.22186676862483,1,20
0,76030815,79080802,0.961432017343476,1,56
0,39676331,79080802,0.501718874828811,1,56
0,76005422,79080802,0.961110915390059,1,62
0,76993790,79080802,0.973609119442162,1,63
0,60872345,79080802,0.769748705886923,1,59
0,62215589,79080802,0.786734421332753,1,47
0,67640611,79080802,0.855335420093489,1,63
0,75804432,79080802,0.958569337726241,1,46
0,67967366,79080802,0.859467333171457,1,57
0,147521,79080802,0.001865446432878,1,12
0,298395,79080802,0.003773292536917,1,7
0,76469335,79080802,0.966977231718009,1,51
0,434605,79080802,0.00549570804808,1,25
0,56993011,79080802,0.720693386493475,1,60
0,72182662,79080802,0.912770990865773,1,55
0,60125410,79080802,0.760303493128459,1,43
0,1595092,79080802,0.020170407477658,1,21
0,77184520,79080802,0.976020956388379,1,53
0,70788233,79080802,0.895138026040758,1,32
0,298323,79080802,0.003772382075741,1,24
0,69024258,79080802,0.872832043357375,1,34
0,1651129,79080802,0.020879011823881,1,36
0,12864,79080802,0.000162669063473,1,3
0,64052458,79080802,0.809962170085225,1,57
0,1215803,79080802,0.015374186518746,1,26
0,22183359,79080802,0.280515098974338,1,34
0,57634,79080802,0.000728798880922,1,11
0,677621,79080802,0.0085687168423,1,10
0,221096,79080802,0.002795823947258,1,16
0,72404061,79080802,0.915570646337148,1,40
0,1437770,79080802,0.018181024517177,1,23
0,68514773,79080802,0.866389455686097,1,43
0,731677,79080802,0.009252270860885,1,9
0,72058346,79080802,0.911198978482793,1,28
0,8327783,79080802,0.105307265346146,1,20
0,622396,79080802,0.007870380474897,1,10
0,9821,79080802,0.000124189433486,1,3
0,2036269,79080802,0.02574922039865,1,52
0,56459842,79080802,0.713951307676419,1,34
0,1629061,79080802,0.02059995547339,1,34
0,14223124,79080802,0.179855586188921,1,33
0,13027236,79080802,0.16473323070244,1,24
0,156044,79080802,0.001973222274605,1,13
0,28214564,79080802,0.356781460056513,1,47
0,74415933,79080802,0.941011359495317,1,48
0,19492216,79080802,0.246484804238581,1,56
0,48111291,79080802,0.60838142486213,1,56
0,6381107,79080802,0.080690974782982,1,21
0,53293529,79080802,0.67391234853688,1,29
0,228179,79080802,0.002885390565462,1,16
0,74978648,79080802,0.948127056172243,1,56
0,222764,79080802,0.002816916297839,1,6
0,55798763,79080802,0.70559176928934,1,40
0,62863364,79080802,0.79492572672695,1,53
0,5449080,79080802,0.068905219246512,1,17
0,15545809,79080802,0.196581327033077,1,55
0,22032270,79080802,0.278604534132064,1,43
0,31103913,79080802,0.393318127957276,1,39
0,22552954,79080802,0.285188736452116,1,21
0,204259,79080802,0.002582915130274,1,17
0,1649901,79080802,0.02086348340271,1,35
0,143307,79080802,0.001812159163484,1,6
0,722687,79080802,0.009138589666807,1,13
0,64922991,79080802,0.820970315905496,1,41
0,1100228,79080802,0.013912706651609,1,13
0,2281146,79080802,0.028845762085215,1,21
0,64623104,79080802,0.817178156589762,1,54
0,1795735,79080802,0.022707597224419,1,60
0,57283796,79080802,0.724370448342191,1,39
0,223114,79080802,0.002821342150779,1,12
0,77339,79080802,0.000977974401423,1,7
0,7218328,79080802,0.091277880565754,1,40
0,54107668,79080802,0.68420737564093,1,39
0,10488062,79080802,0.132624628667777,1,22
0,1938450,79080802,0.024512270373788,1,37
0,53971169,79080802,0.682481305639768,1,34
0,605227,79080802,0.007653273420267,1,11
0,529757,79080802,0.006698933073542,1,21
0,1854246,79080802,0.023447486028278,1,13
0,77957980,79080802,0.985801585573196,1,47
0,75646957,79080802,0.95657802003576,1,59
0,716254,79080802,0.009057242489776,1,7
0,74394184,79080802,0.940736336993649,1,40
0,75764792,79080802,0.958068078267593,1,44
0,231507,79080802,0.002927474104271,1,7
0,35860,79080802,0.0004534602469,1,11
0,34177184,79080802,0.432180543641932,1,17
0,59537102,79080802,0.752864165439293,1,35
0,78207144,79080802,0.988952337635625,1,48
0,1596255,79080802,0.020185113954712,1,38
0,13433,79080802,0.000169864235823,1,5
0,78342165,79080802,0.990659717892087,1,38
0,523358,79080802,0.006618015836511,1,30
0,241491,79080802,0.003053724720698,1,17
0,48374597,79080802,0.611711006673908,1,35
0,112751,79080802,0.001425769556561,1,5
0,24430791,79080802,0.308934537613819,1,29
0,33321,79080802,0.000421353845147,1,13
0,9074064,79080802,0.11474420808226,1,24
0,50996824,79080802,0.644869838320557,1,57
0,234776,79080802,0.002968811570727,1,23
0,12037445,79080802,0.152217032396814,1,19
0,3175690,79080802,0.040157534062439,1,26
0,12094,79080802,0.000152932187005,1,3
0,47104103,79080802,0.595645236374816,1,27
0,61106284,79080802,0.772706933346478,1,38
0,7034939,79080802,0.088958872723623,1,21
0,1963373,79080802,0.024827429038972,1,30
0,1942169,79080802,0.024559298222595,1,57
0,76101545,79080802,0.962326418996105,1,56
0,29176563,79080802,0.368946220348145,1,26
0,1279533,79080802,0.016180071112582,1,31
0,1566280,79080802,0.019806071263668,1,34
0,77275610,79080802,0.977172816229152,1,53
0,6307525,79080802,0.079760508751543,1,15
0,56836315,79080802,0.718711919487109,1,44
0,79080802,79080802,1,1,62
0,76062053,79080802,0.961827031040985,1,62
0,55118947,79080802,0.696995296026462,1,41
0,57563108,79080802,0.727902430731545,1,54
0,22667481,79080802,0.286636964051022,1,17
0,59199379,79080802,0.748593558775491,1,52
0,976803,79080802,0.012351961225684,1,6
0,16019853,79080802,0.202575752835688,1,18
0,53310872,79080802,0.674131655872686,1,44
0,140893,79080802,0.001781633423495,1,15
0,11103848,79080802,0.140411423748586,1,29
0,13469,79080802,0.000170319466411,1,2
0,67860921,79080802,0.858121304839574,1,45
0,13344770,79080802,0.16874854152339,1,11
0,1974169,79080802,0.024963947634219,1,23
0,100608,79080802,0.001272217750144,1,4
0,79080802,79080802,1,1,63
0,204582,79080802,0.002586999560273,1,5
0,39412276,79080802,0.498379821691743,1,41
0,38405878,79080802,0.485653622986778,1,36
0,75960474,79080802,0.960542534710258,1,55
0,648528,79080802,0.008200827300664,1,3
0,10227400,79080802,0.129328481013635,1,39
0,2030895,79080802,0.025681264588086,1,44
0,76715501,79080802,0.970090073188686,1,63
0,28302,79080802,0.000357887113993,1,9
0,37060925,79080802,0.468646296733308,1,33
0,78518333,79080802,0.992887414065426,1,55
0,27837349,79080802,0.352011465437591,1,37
0,49068,79080802,0.000620479291548,1,6
0,12809,79080802,0.000161973572296,1,4
0,268627,79080802,0.003396867421754,1,18
0,116992,79080802,0.001479398248895,1,8
0,4578046,79080802,0.05789073813389,1,29
0,2014367,79080802,0.025472263166982,1,21
0,145000,79080802,0.001833567646418,1,16
0,22475063,79080802,0.284203781848343,1,37
0,65721531,79080802,0.831068089066674,1,58
0,266307,79080802,0.003367530339412,1,14
0,1602020,79080802,0.020258014075275,1,26
0,78503594,79080802,0.992701035075492,1,43
0,9497180,79080802,0.120094634346273,1,23
0,60040704,79080802,0.759232360845303,1,53
0,59751450,79080802,0.755574658941876,1,52
0,9838675,79080802,0.12441293905947,1,20
0,47290444,79080802,0.598001573125169,1,45
0,72709589,79080802,0.919434137756974,1,60
1 group_count row_count marketer prosecutor field_count
2 0 432512 79080802 0.005469241447501 1 10
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4 0 43538084 79080802 0.550551877306454 1 38
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11 0 68089326 79080802 0.861009553241506 1 40
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13 0 6849 79080802 8.6607619381503E-05 1 5
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23 0 59947 79080802 0.000758047446206 1 5
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25 0 9253 79080802 0.00011700690643 1 5
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28 0 76748775 79080802 0.970510832705009 1 62
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49 0 69540609 79080802 0.879361453618035 1 61
50 0 2652519 79080802 0.033541882895927 1 17
51 0 5128 79080802 6.48450682126365E-05 1 2
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53 0 13532 79080802 0.00017111611994 1 2
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56 0 4949 79080802 6.25815605663686E-05 1 4
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60 0 43802720 79080802 0.553898277359403 1 49
61 0 4728512 79080802 0.059793424957931 1 31
62 0 68774853 79080802 0.869678243779065 1 54
63 0 79080801 79080802 0.999999987354706 1 59
64 0 56819554 79080802 0.718499971712477 1 27
65 0 78878427 79080802 0.997440908603835 1 61
66 0 436798 79080802 0.00552343917807 1 7
67 0 77952406 79080802 0.985731100703809 1 60
68 0 49878165 79080802 0.630724066253147 1 19
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99 0 5954 79080802 7.52900811501634E-05 1 2
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111 0 4938 79080802 6.24424623311231E-05 1 2
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123 0 56585686 79080802 0.715542642068805 1 51
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127 0 6383033 79080802 0.080715329619444 1 49
128 0 76711922 79080802 0.970044815681055 1 53
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134 0 79080743 79080802 0.999999253927647 1 60
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153 0 215211 79080802 0.002721406391402 1 7
154 0 4937 79080802 6.24298170370098E-05 1 2
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681
src/out/risk_xoi2.csv
Unescape View File

@ -1,681 +0,0 @@
,group_count,patient_count,rec_count,marketer,prosecutor,field_count
0,273774,4936,79080802,0.003461952750555059,1.0,9
0,7489177,4936,79080802,0.09470284583102735,1.0,30
0,64952802,4936,79080802,0.8213472847683058,1.0,56
0,55095676,4936,79080802,0.6967010273871527,1.0,31
0,73434842,4936,79080802,0.9286051752484756,1.0,47
0,50154906,4936,79080802,0.634223537591336,1.0,26
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0,1594832,4936,79080802,0.020167119701188667,1.0,23
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0,59550486,4936,79080802,0.7530334100557048,1.0,45
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0,78403586,4936,79080802,0.9914364045018158,1.0,54
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0,1145613,4936,79080802,0.014486613324938207,1.0,9
0,35491,4936,79080802,0.0004487941333725978,1.0,6
0,185955,4936,79080802,0.0023514556668254327,1.0,9
0,4941,4936,79080802,6.248039821346273e-05,1.0,4
0,69117717,4936,79080802,0.8740138598999034,1.0,61
0,28838354,4936,79080802,0.3646694680714037,1.0,38
0,31062023,4936,79080802,0.39278841658687275,1.0,25
0,58346375,4936,79080802,0.737807072315731,1.0,55
0,79080802,4936,79080802,1.0,1.0,62
0,1091575,4936,79080802,0.013803286921647557,1.0,24
0,45109405,4936,79080802,0.5704216934977467,1.0,32
0,457095,4936,79080802,0.0057801007126862475,1.0,23
0,47467250,4936,79080802,0.6002373369961523,1.0,36
0,5699,4936,79080802,7.206553115129005e-05,1.0,4
0,78193840,4936,79080802,0.9887841046427425,1.0,57
0,76098393,4936,79080802,0.9622865610290599,1.0,60
0,77184520,4936,79080802,0.9760209563883786,1.0,60
0,1801424,4936,79080802,0.022779536302628797,1.0,31
0,69852105,4936,79080802,0.8833004121531292,1.0,54
0,78884725,4936,79080802,0.9975205486661605,1.0,54
0,14538,4936,79080802,0.00018383728581811804,1.0,2
0,44132066,4936,79080802,0.558062954394418,1.0,63
0,43734898,4936,79080802,0.5530406482220552,1.0,42
0,58854577,4936,79080802,0.744233436074662,1.0,46
0,52275966,4936,79080802,0.6610449651231407,1.0,21
0,60670185,4936,79080802,0.7671923332289928,1.0,35
0,63002798,4936,79080802,0.7966889106663334,1.0,45
0,68621267,4936,79080802,0.8677361036373911,1.0,43
0,1311998,4936,79080802,0.016590600585967754,1.0,21
0,16164245,4936,79080802,0.20440163214328555,1.0,23
0,1535446,4936,79080802,0.01941616626498047,1.0,32
0,48263368,4936,79080802,0.6103044832549878,1.0,32
0,97362,4936,79080802,0.0012311711254521672,1.0,14
0,77159270,4936,79080802,0.9757016627120195,1.0,47
0,74690647,4936,79080802,0.9444851988223387,1.0,53
0,31246454,4936,79080802,0.3951206008254696,1.0,38
0,13107870,4936,79080802,0.16575287134796635,1.0,45
0,58553924,4936,79080802,0.7404315904636374,1.0,38
0,35703276,4936,79080802,0.4514784258257775,1.0,48
0,5870999,4936,79080802,0.07424050909347125,1.0,26
0,61835524,4936,79080802,0.7819283876256086,1.0,52
0,70089261,4936,79080802,0.886299319523846,1.0,35
0,956801,4936,79080802,0.01209903005283128,1.0,10
0,78805409,4936,79080802,0.9965175745182756,1.0,43
0,27669943,4936,79080802,0.3498945673312721,1.0,25
0,6609585,4936,79080802,0.08358014629138436,1.0,25
0,77849984,4936,79080802,0.9844359443901441,1.0,60
0,77654526,4936,79080802,0.9819643204933607,1.0,62
0,1411781,4936,79080802,0.017852385968468047,1.0,18
0,47673428,4936,79080802,0.6028445184458296,1.0,25
0,62749553,4936,79080802,0.7934865531586288,1.0,49
0,5764210,4936,79080802,0.07289013078041369,1.0,28
0,24213046,4936,79080802,0.3061810880471344,1.0,33
0,60558420,4936,79080802,0.7657790319324278,1.0,46
0,9070259,4936,79080802,0.11469609273815913,1.0,21
0,75869109,4936,79080802,0.9593871974136023,1.0,56
0,65355063,4936,79080802,0.8264339934235871,1.0,49
0,3456806,4936,79080802,0.043712328562373455,1.0,19
0,185096,4936,79080802,0.002340593359182169,1.0,15
0,1392726,4936,79080802,0.017611429889140478,1.0,20
0,75583195,4936,79080802,0.9557717307925127,1.0,59
0,66376643,4936,79080802,0.8393521729837793,1.0,47
0,45486586,4936,79080802,0.5751912581766685,1.0,31
0,73133813,4936,79080802,0.9247985750068645,1.0,57
0,54189462,4936,79080802,0.6852416848276273,1.0,31
0,57643152,4936,79080802,0.7289146106535439,1.0,55
0,66430048,4936,79080802,0.8400274949158962,1.0,60
0,180224,4936,79080802,0.0022789854862625193,1.0,19
0,428863,4936,79080802,0.005423098769281576,1.0,18
0,60151696,4936,79080802,0.760635887329519,1.0,32
0,4720325,4936,79080802,0.059689897935026,1.0,35
0,1195159,4936,79080802,0.015113137067072234,1.0,13
0,33050014,4936,79080802,0.41792714747632426,1.0,30
0,1417785,4936,79080802,0.017928308314323873,1.0,44
0,75576077,4936,79080802,0.9556817215890148,1.0,46
0,21075259,4936,79080802,0.26650284856746903,1.0,25
0,63367342,4936,79080802,0.8012986767635463,1.0,53
0,74404404,4936,79080802,0.9408655718994858,1.0,27
0,141201,4936,79080802,0.0017855281740819977,1.0,3
0,548397,4936,79080802,0.006934641355812249,1.0,13
0,65047449,4936,79080802,0.8225441239202405,1.0,47
0,36526699,4936,79080802,0.4618908518403746,1.0,39
0,986228,4936,79080802,0.01247114312270126,1.0,14
0,65912913,4936,79080802,0.8334881707446518,1.0,42
0,50252,4936,79080802,0.0006354513197779658,1.0,10
0,76048765,4936,79080802,0.9616590003728086,1.0,62
0,267419,4936,79080802,0.003381591906465491,1.0,16
0,45252142,4936,79080802,0.5722266448435867,1.0,59
0,58440460,4936,79080802,0.7389968048123741,1.0,48
0,995152,4936,79080802,0.012583989727367711,1.0,9
0,49329677,4936,79080802,0.6237882741755705,1.0,43
0,663431,4936,79080802,0.008389280118833393,1.0,20
0,1006217,4936,79080802,0.012723909906730587,1.0,11
0,1500847,4936,79080802,0.01897865173395687,1.0,43
0,6943,4936,79080802,8.779627702814647e-05,1.0,5
0,35113589,4936,79080802,0.4440216602760301,1.0,38
0,27174516,4936,79080802,0.3436297472046376,1.0,41
0,46866291,4936,79080802,0.5926380336911606,1.0,44
0,3359070,4936,79080802,0.042476428096922945,1.0,21
0,960855,4936,79080802,0.01215029407516631,1.0,31
0,65657934,4936,79080802,0.8302638862969548,1.0,48
1 group_count patient_count rec_count marketer prosecutor field_count
2 0 273774 4936 79080802 0.003461952750555059 1.0 9
3 0 7489177 4936 79080802 0.09470284583102735 1.0 30
4 0 64952802 4936 79080802 0.8213472847683058 1.0 56
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8 0 68218429 4936 79080802 0.8626420986474063 1.0 49
9 0 1594832 4936 79080802 0.020167119701188667 1.0 23
10 0 77863013 4936 79080802 0.9846006999271454 1.0 48
11 0 59550486 4936 79080802 0.7530334100557048 1.0 45
12 0 25604937 4936 79080802 0.3237819591156903 1.0 14
13 0 61363078 4936 79080802 0.7759541690029901 1.0 29
14 0 74688866 4936 79080802 0.9444626775535231 1.0 56
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17 0 71192306 4936 79080802 0.9002476479689723 1.0 54
18 0 74247127 4936 79080802 0.9388767579772396 1.0 60
19 0 47795062 4936 79080802 0.6043826161499981 1.0 20
20 0 51970132 4936 79080802 0.6571776042433156 1.0 28
21 0 44964529 4936 79080802 0.5685896938677987 1.0 16
22 0 618899 4936 79080802 0.007826159881383094 1.0 25
23 0 37539224 4936 79080802 0.47469452826237146 1.0 44
24 0 75718774 4936 79080802 0.9574861671230901 1.0 58
25 0 589272 4936 79080802 0.007451517752690469 1.0 11
26 0 77184520 4936 79080802 0.9760209563883786 1.0 52
27 0 52093569 4936 79080802 0.6587385014127702 1.0 45
28 0 1604016 4936 79080802 0.020283254082324557 1.0 38
29 0 3531081 4936 79080802 0.044651557782633515 1.0 23
30 0 68411037 4936 79080802 0.865077683455967 1.0 52
31 0 2276935 4936 79080802 0.028792512751704264 1.0 15
32 0 185483 4936 79080802 0.0023454870880039885 1.0 18
33 0 78906741 4936 79080802 0.9977989474613573 1.0 50
34 0 1379948 4936 79080802 0.01744984832096164 1.0 37
35 0 6904475 4936 79080802 0.08730911707243434 1.0 16
36 0 18037 4936 79080802 0.00022808316992030506 1.0 5
37 0 76145311 4936 79080802 0.9628798529382643 1.0 46
38 0 65473121 4936 79080802 0.8279268715560067 1.0 42
39 0 65061413 4936 79080802 0.8227207028072376 1.0 52
40 0 68300221 4936 79080802 0.8636763825435154 1.0 49
41 0 72339508 4936 79080802 0.9147543546662564 1.0 43
42 0 46982606 4936 79080802 0.5941088710759408 1.0 24
43 0 69078744 4936 79080802 0.8735210348524285 1.0 63
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45 0 56840103 4936 79080802 0.7187598198612098 1.0 41
46 0 48819199 4936 79080802 0.6173331297272378 1.0 33
47 0 32215999 4936 79080802 0.4073807825064799 1.0 38
48 0 50031183 4936 79080802 0.6326590238677651 1.0 51
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51 0 12393721 4936 79080802 0.15672224720229821 1.0 29
52 0 14028385 4936 79080802 0.17739305425860502 1.0 23
53 0 72778283 4936 79080802 0.9203027935907884 1.0 60
54 0 28811099 4936 79080802 0.3643248205803477 1.0 30
55 0 60540996 4936 79080802 0.7655587003277989 1.0 48
56 0 4621304 4936 79080802 0.058437748266639986 1.0 12
57 0 30396235 4936 79080802 0.3843693315098145 1.0 31
58 0 5663760 4936 79080802 0.07161991098673987 1.0 18
59 0 69366259 4936 79080802 0.8771567465893935 1.0 28
60 0 59565305 4936 79080802 0.7532208006691687 1.0 31
61 0 32568 4936 79080802 0.0004118319386796305 1.0 6
62 0 67106700 4936 79080802 0.8485839584682007 1.0 58
63 0 2044935 4936 79080802 0.02585880451743522 1.0 46
64 0 71669554 4936 79080802 0.9062825892939225 1.0 44
65 0 12600114 4936 79080802 0.15933214739020982 1.0 15
66 0 78196361 4936 79080802 0.988815983429202 1.0 58
67 0 31042242 4936 79080802 0.39253828002401897 1.0 29
68 0 18221 4936 79080802 0.00023040990403713912 1.0 10
69 0 61786032 4936 79080802 0.7813025467293566 1.0 59
70 0 76209633 4936 79080802 0.9636932235462154 1.0 61
71 0 1391473 4936 79080802 0.0175955853356166 1.0 28
72 0 65253592 4936 79080802 0.8251508627846238 1.0 49
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75 0 56956443 4936 79080802 0.7202309733783429 1.0 43
76 0 32534 4936 79080802 0.00041140199867978073 1.0 2
77 0 1090054 4936 79080802 0.013784053429301337 1.0 18
78 0 9892 4936 79080802 0.00012508724936805774 1.0 2
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80 0 79080802 4936 79080802 1.0 1.0 56
81 0 74977179 4936 79080802 0.9481084802351903 1.0 60
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108 0 38939 4936 79080802 0.0004923951074750102 1.0 9
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110 0 25189308 4936 79080802 0.3185262081687032 1.0 40
111 0 363424 4936 79080802 0.004595603367806007 1.0 30
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119 0 498436 4936 79080802 0.0063028698166212324 1.0 21
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137 0 1967117 4936 79080802 0.024874773020131993 1.0 7
138 0 60553568 4936 79080802 0.7657176769653904 1.0 59
139 0 223388 4936 79080802 0.0028248069613659205 1.0 15
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141 0 60917877 4936 79080802 0.7703244714184866 1.0 33
142 0 430081 4936 79080802 0.005438500737511488 1.0 25
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152 0 597734 4936 79080802 0.00755852223147661 1.0 12
153 0 74527749 4936 79080802 0.942425305701882 1.0 40
154 0 48300368 4936 79080802 0.6107723591371772 1.0 57
155 0 18914348 4936 79080802 0.23917749341995798 1.0 15
156 0 209153 4936 79080802 0.0026448011996641108 1.0 5
157 0 13631334 4936 79080802 0.17237222758565346 1.0 21
158 0 74993826 4936 79080802 0.9483189864462932 1.0 50
159 0 46335 4936 79080802 0.0005859197027364493 1.0 12
160 0 367649 4936 79080802 0.004649029735434398 1.0 17
161 0 251264 4936 79080802 0.003177307180066282 1.0 9
162 0 480513 4936 79080802 0.006076228210229836 1.0 27
163 0 14161422 4936 79080802 0.1790753462515466 1.0 29
164 0 59358977 4936 79080802 0.7506117224253744 1.0 51
165 0 54674582 4936 79080802 0.6913761699078368 1.0 32
166 0 3760679 4936 79080802 0.047554892020442587 1.0 13
167 0 11192285 4936 79080802 0.14152973562407725 1.0 16
168 0 52940260 4936 79080802 0.6694451581307939 1.0 42
169 0 41931453 4936 79080802 0.5302355557800236 1.0 54
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171 0 13542961 4936 79080802 0.17125472500898511 1.0 26
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181 0 202709 4936 79080802 0.0025633149243984655 1.0 13
182 0 4940 4936 79080802 6.24677529193495e-05 1.0 2
183 0 35942642 4936 79080802 0.4545052792964846 1.0 34
184 0 53125855 4936 79080802 0.6717920614917385 1.0 60
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110 0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0
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114 0 0.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0
115 0 1.0 1.0 0.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0
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118 0 1.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0
119 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
120 0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0
121 0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0
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1 condition_occurrence_id person_id condition_concept_id condition_start_date condition_start_datetime condition_end_date condition_end_datetime condition_type_concept_id stop_reason provider_id visit_occurrence_id condition_source_value condition_source_concept_id drug_exposure_id3 drug_concept_id3 drug_exposure_start_date3 drug_exposure_start_datetime3 drug_exposure_end_date3 drug_exposure_end_datetime3 drug_type_concept_id3 stop_reason3 refills3 quantity3 days_supply3 sig3 route_concept_id3 effective_drug_dose3 dose_unit_concept_id3 lot_number3 provider_id3 visit_occurrence_id3 drug_source_value3 drug_source_concept_id3 route_source_value3 dose_unit_source_value3 measurement_id4 measurement_concept_id4 measurement_date4 measurement_datetime4 measurement_type_concept_id4 operator_concept_id4 value_as_number4 value_as_concept_id4 unit_concept_id4 range_low4 range_high4 provider_id4 visit_occurrence_id4 measurement_source_value4 measurement_source_concept_id4 unit_source_value4 value_source_value4 observation_id5 observation_concept_id5 observation_date5 observation_datetime5 observation_type_concept_id5 value_as_number5 value_as_string5 value_as_concept_id5 qualifier_concept_id5 unit_concept_id5 provider_id5 visit_occurrence_id5 observation_source_value5 observation_source_concept_id5 unit_source_value5 qualifier_source_value5 value_source_concept_id5 value_source_value5 questionnaire_response_id5 gender_concept_id6 year_of_birth6 month_of_birth6 day_of_birth6 birth_datetime6 race_concept_id6 ethnicity_concept_id6 location_id6 provider_id6 care_site_id6 person_source_value6 gender_source_value6 gender_source_concept_id6 race_source_value6 race_source_concept_id6 ethnicity_source_value6 ethnicity_source_concept_id6 procedure_occurrence_id7 procedure_concept_id7 procedure_date7 procedure_datetime7 procedure_type_concept_id7 modifier_concept_id7 quantity7 provider_id7 visit_occurrence_id7 procedure_source_value7 procedure_source_concept_id7 qualifier_source_value7 visit_occurrence_id8 visit_concept_id8 visit_start_date8 visit_start_datetime8 visit_end_date8 visit_end_datetime8 visit_type_concept_id8 provider_id8 care_site_id8 visit_source_value8 visit_source_concept_id8
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311
src/pandas_risk.py
Unescape View File

@ -1,13 +1,27 @@
"""
Health Information Privacy Lab
Steve L. Nyemba & Brad. Malin
Brad. Malin, Weiyi Xia, Steve L. Nyemba
This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas)
The framework will compute the following risk measures:
- marketer
- prosecutor
- pitman
This is an extension to the pandas data-frame that will perform a risk assessment on a variety of attributes
This implementation puts the responsibility on the user of the framework to join datasets and load the final results into a pandas data-frame.
References :
https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf
The code will randomly select fields and compute the risk (marketer and prosecutor) and perform a given number of runs.
This framework integrates pandas (for now) as an extension and can be used in two modes :
Experimental mode
Here the assumption is that we are not sure of the attributes to be disclosed, the framework will explore a variety of combinations and associate risk measures every random combinations
Evaluation mode
The evaluation mode assumes the set of attributes given are known and thus will evaluate risk for a subset of attributes.
features :
- determine viable fields (quantifiable in terms of uniqueness). This is a way to identify fields that can act as identifiers.
- explore and evaluate risk of a sample dataset against a known population dataset
- explore and evaluate risk on a sample dataset
Usage:
from pandas_risk import *
@ -22,14 +36,21 @@
"""
import pandas as pd
import numpy as np
import time
import logging
import json
from datetime import datetime
import sys
sys.setrecursionlimit(3000)
@pd.api.extensions.register_dataframe_accessor("deid")
class deid :
"""
This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe
This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe
"""
def __init__(self,df):
self._df = df.fillna(' ')
def explore(self,**args):
"""
This function will perform experimentation by performing a random policies (combinations of attributes)
@ -38,15 +59,15 @@ class deid :
@param pop|sample data-frame with popublation reference
@param id key field that uniquely identifies patient/customer ...
"""
# id = args['id']
id = args['id']
pop= args['pop'] if 'pop' in args else None
# if 'columns' in args :
# cols = args['columns']
# params = {"sample":args['data'],"cols":cols}
# if pop is not None :
# params['pop'] = pop
# return self.evaluate(**params)
# else :
if 'pop_size' in args :
pop_size = np.float64(args['pop_size'])
else:
pop_size = -1
#
# Policies will be generated with a number of runs
#
@ -57,7 +78,7 @@ class deid :
k = sample.columns.size -1 if 'field_count' not in args else int(args['field_count'])
columns = list(set(sample.columns.tolist()) - set([id]))
o = pd.DataFrame()
# pop = args['pop'] if 'pop' in args else None
for i in np.arange(RUNS):
n = np.random.randint(2,k)
@ -65,6 +86,9 @@ class deid :
params = {'sample':sample,'cols':cols}
if pop is not None :
params['pop'] = pop
if pop_size > 0 :
params['pop_size'] = pop_size
r = self.evaluate(**params)
#
# let's put the policy in place
@ -75,137 +99,148 @@ class deid :
o.index = np.arange(o.shape[0]).astype(np.int64)
return o
def evaluate(self,**args) :
def evaluate(self, **args):
"""
This function will compute the marketer, if a population is provided it will evaluate the marketer risk relative to both the population and sample
@param smaple data-frame with the data to be processed
@param policy the columns to be considered.
@param pop population dataset
@params flag user defined flag (no computation use)
This function has the ability to evaluate risk associated with either a population or a sample dataset
:sample sample dataset
:pop population dataset
:cols list of columns of interest or policies
:flag user provided flag for the context of the evaluation
"""
if (args and 'sample' not in args) or not args :
x_i = pd.DataFrame(self._df)
elif args and 'sample' in args :
x_i = args['sample']
if 'sample' in args :
sample = pd.DataFrame(args['sample'])
else:
sample = pd.DataFrame(self._df)
if not args or 'cols' not in args:
cols = x_i.columns.tolist()
# cols = self._df.columns.tolist()
elif args and 'cols' in args :
cols = sample.columns.tolist()
elif args and 'cols' in args:
cols = args['cols']
flag = args['flag'] if 'flag' in args else 'UNFLAGGED'
MIN_GROUP_SIZE = args['min_group_size'] if 'min_group_size' in args else 1
# if args and 'sample' in args :
# x_i = pd.DataFrame(self._df)
# else :
# cols = args['cols'] if 'cols' in args else self._df.columns.tolist()
# x_i = x_i.groupby(cols,as_index=False).size().values
x_i_values = x_i.groupby(cols,as_index=False).size().values
SAMPLE_GROUP_COUNT = x_i_values.size
SAMPLE_FIELD_COUNT = len(cols)
SAMPLE_POPULATION = x_i_values.sum()
UNIQUE_REC_RATIO = np.divide(np.sum(x_i_values <= MIN_GROUP_SIZE) , np.float64( SAMPLE_POPULATION))
SAMPLE_MARKETER = SAMPLE_GROUP_COUNT / np.float64(SAMPLE_POPULATION)
SAMPLE_PROSECUTOR = 1/ np.min(x_i_values).astype(np.float64)
flag = 'UNFLAGGED' if 'flag' not in args else args['flag']
#
# @TODO: auto select the columns i.e removing the columns that will have the effect of an identifier
#
# if 'population' in args :
# pop = pd.DataFrame(args['population'])
r = {"flag":flag}
# if sample :
handle_sample = Sample()
xi = sample.groupby(cols,as_index=False).size().values
handle_sample.set('groups',xi)
if 'pop_size' in args :
pop_size = np.float64(args['pop_size'])
else:
pop_size = -1
#
#-- The following conditional line is to address the labels that will be returned
# @TODO: Find a more elegant way of doing this.
#
if 'pop' in args :
Yi = args['pop']
y_i= pd.DataFrame({"group_size":Yi.groupby(cols,as_index=False).size()}).reset_index()
UNIQUE_REC_RATIO = np.sum(y_i.group_size < MIN_GROUP_SIZE) , np.float64(Yi.shape[0])
# y_i['group'] = pd.DataFrame({"group_size":args['pop'].groupby(cols,as_index=False).size().values}).reset_index()
# x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size().values}).reset_index()
x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size()}).reset_index()
SAMPLE_RATIO = int(100 * x_i.size/args['pop'].shape[0])
r = pd.merge(x_i,y_i,on=cols,how='inner')
r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1)
r['sample %'] = np.repeat(SAMPLE_RATIO,r.shape[0])
r['tier'] = np.repeat(flag,r.shape[0])
r['sample marketer'] = np.repeat(SAMPLE_MARKETER,r.shape[0])
r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']]
r['sample marketer'] = handle_sample.marketer()
r['sample prosecutor'] = handle_sample.prosecutor()
r['sample unique ratio'] = handle_sample.unique_ratio()
r['sample group count'] = xi.size
else:
r = pd.DataFrame({"marketer":[SAMPLE_MARKETER],"flag":[flag],"prosecutor":[SAMPLE_PROSECUTOR],"field_count":[SAMPLE_FIELD_COUNT],"group_count":[SAMPLE_GROUP_COUNT]})
r['unique_row_ratio'] = np.repeat(UNIQUE_REC_RATIO,r.shape[0])
return r
r['marketer'] = handle_sample.marketer()
r['prosecutor'] = handle_sample.prosecutor()
r['unique ratio'] = handle_sample.unique_ratio()
r['group count'] = xi.size
if pop_size > 0 :
handle_sample.set('pop_size',pop_size)
r['pitman risk'] = handle_sample.pitman()
if 'pop' in args :
xi = pd.DataFrame({"sample_group_size":sample.groupby(cols,as_index=False).size()}).reset_index()
yi = pd.DataFrame({"population_group_size":args['population'].groupby(cols,as_index=False).size()}).reset_index()
merged_groups = pd.merge(xi,yi,on=cols,how='inner')
handle_population= Population()
handle_population.set('merged_groups',merged_groups)
r['pop. marketer'] = handle_population.marketer()
r['pitman risk'] = handle_population.pitman()
r['pop. group size'] = np.unique(yi.population_group_size).size
#
# At this point we have both columns for either sample,population or both
#
r['field count'] = len(cols)
return pd.DataFrame([r])
def _risk(self,**args):
class Risk :
"""
This class is an abstraction of how we chose to structure risk computation i.e in 2 sub classes:
- Sample computes risk associated with a sample dataset only
- Population computes risk associated with a population
"""
def __init__(self):
self.cache = {}
def set(self,key,value):
if id not in self.cache :
self.cache[id] = {}
self.cache[key] = value
class Sample(Risk):
"""
This class will compute risk for the sample dataset: the marketer and prosecutor risk are computed by default.
This class can optionally add pitman risk if the population size is known.
"""
def __init__(self):
Risk.__init__(self)
def marketer(self):
"""
@param id name of patient field
@params num_runs number of runs (default will be 100)
@params quasi_id list of quasi identifiers to be used (this will only perform a single run)
computing marketer risk for sample dataset
"""
groups = self.cache['groups']
group_count = groups.size
row_count = groups.sum()
return group_count / np.float64(row_count)
id = args['id']
if 'quasi_id' in args :
num_runs = 1
columns = list(set(args['quasi_id'])- set(id) )
else :
num_runs = args['num_runs'] if 'num_runs' in args else 100
columns = list(set(self._df.columns) - set([id]))
r = pd.DataFrame()
k = len(columns)
N = self._df.shape[0]
tmp = self._df.fillna(' ')
np.random.seed(int(time.time()) )
for i in range(0,num_runs) :
#
# let's chose a random number of columns and compute marketer and prosecutor risk
# Once the fields are selected we run a groupby clause
#
if 'quasi_id' not in args :
if 'field_count' in args :
#
# We chose to limit how many fields we passin
n = np.random.randint(2,int(args['field_count'])) #-- number of random fields we are picking
else :
n = np.random.randint(2,k) #-- number of random fields we are picking
ii = np.random.choice(k,n,replace=False)
cols = np.array(columns)[ii].tolist()
policy = np.zeros(k)
policy [ii] = 1
policy = pd.DataFrame(policy).T
else:
cols = columns
policy = np.ones(k)
policy = pd.DataFrame(policy).T
n = len(cols)
policy.columns = columns
N = tmp.shape[0]
x_ = tmp.groupby(cols).size().values
# print [id,i,n,k,self._df.groupby(cols).count()]
r = r.append(
pd.DataFrame(
[
{
"group_count":x_.size,
"patient_count":N,
"field_count":n,
"marketer": x_.size / np.float64(np.sum(x_)),
"prosecutor":1 / np.float64(np.min(x_))
}
]
).join(policy)
)
# g_size = x_.size
# n_ids = np.float64(np.sum(x_))
# sql = """
# SELECT COUNT(g_size) as group_count, :patient_count as patient_count,SUM(g_size) as rec_count, COUNT(g_size)/SUM(g_size) as marketer, 1/ MIN(g_size) as prosecutor, :n as field_count
# FROM (
# SELECT COUNT(*) as g_size,:key,:fields
# FROM :full_name
# GROUP BY :fields
# """.replace(":n",str(n)).replace(":fields",",".join(cols)).replace(":key",id).replace(":patient_count",str(N))
# r.append(self._df.query(sql.replace("\n"," ").replace("\r"," ") ))
return r
# df = pd.read_gbq("select * from deid_risk.risk_30k",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')
# r = df.deid.risk(id='person_id',num_runs=200)
# print r[['field_count','patient_count','marketer','prosecutor']]
def prosecutor(self):
"""
The prosecutor risk consists in determining 1 over the smallest group size
It identifies if there is at least one record that is unique
"""
groups = self.cache['groups']
return 1 / np.float64(groups.min())
def unique_ratio(self):
groups = self.cache['groups']
row_count = groups.sum()
return groups[groups == 1].sum() / np.float64(row_count)
def pitman(self):
"""
This function will approximate pitman de-identification risk based on pitman sampling
"""
groups = self.cache['groups']
si = groups[groups == 1].size
u = groups.size
alpha = np.divide(si , np.float64(u) )
f = np.divide(groups.sum(), np.float64(self.cache['pop_size']))
return np.power(f,1-alpha)
class Population(Sample):
"""
This class will compute risk for datasets that have population information or datasets associated with them.
This computation includes pitman risk (it requires minimal information about population)
"""
def __init__(self,**args):
Sample.__init__(self)
def set(self,key,value):
Sample.set(key,value)
if key == 'merged_groups' :
Sample.set('pop_size',np.float64(r.population_group_sizes.sum()) )
"""
This class will measure risk and account for the existance of a population
:merged_groups {sample_group_size, population_group_size} is a merged dataset with group sizes of both population and sample
"""
def marketer(self):
"""
This function requires
"""
r = self.cache['merged_groups']
sample_row_count = r.sample_group_size.sum()
#
# @TODO : make sure the above line is size (not sum)
# sample_row_count = r.sample_group_size.size
return r.apply(lambda row: (row.sample_group_size / np.float64(row.population_group_size)) /np.float64(sample_row_count) ,axis=1).sum()

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