diff --git a/Jenkinsfile_4 b/Jenkinsfile_4 index 7cb2e22..2777271 100644 --- a/Jenkinsfile_4 +++ b/Jenkinsfile_4 @@ -19,7 +19,7 @@ pipeline { stage('Load Artifact') { steps { script { - copyArtifacts fingerprintArtifacts: true, projectName: 's452627-training', selector: buildParameter("BUILD_SELECTOR") + copyArtifacts fingerprintArtifacts: true, projectName: 's452627-training/master/', selector: buildParameter("BUILD_SELECTOR") } } } diff --git a/cifar_net.pth b/cifar_net.pth index 88caf03..d49ea01 100644 Binary files a/cifar_net.pth and b/cifar_net.pth differ diff --git a/my_runs/1/config.json b/my_runs/1/config.json new file mode 100644 index 0000000..cbce8e9 --- /dev/null +++ b/my_runs/1/config.json @@ -0,0 +1,5 @@ +{ + "epochs": 10, + "learning_rate": 0.001, + "seed": 577991242 +} \ No newline at end of file diff --git a/my_runs/1/cout.txt b/my_runs/1/cout.txt new file mode 100644 index 0000000..1f9be72 --- /dev/null +++ b/my_runs/1/cout.txt @@ -0,0 +1 @@ +10 0.001 diff --git a/my_runs/1/metrics.json b/my_runs/1/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/my_runs/1/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_runs/1/run.json b/my_runs/1/run.json new file mode 100644 index 0000000..2301a04 --- /dev/null +++ b/my_runs/1/run.json @@ -0,0 +1,90 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "e:\\Pyton\\IUM\\ium_452627", + "dependencies": [ + "numpy==1.20.0", + "pandas==1.4.1", + "sacred==0.8.4", + "torch==1.8.1+cu102", + "torchvision==0.9.1+cu102" + ], + "mainfile": "sacred_train.py", + "name": "sacred_scopes", + "repositories": [ + { + "commit": "da87b5f0c3566577958feaa4005909d12ce8f0c7", + "dirty": false, + "url": "git@git.wmi.amu.edu.pl:s452627/ium_452627.git" + }, + { + "commit": "da87b5f0c3566577958feaa4005909d12ce8f0c7", + "dirty": false, + "url": "git@git.wmi.amu.edu.pl:s452627/ium_452627.git" + } + ], + "sources": [ + [ + "sacred_train.py", + "_sources\\sacred_train_69457f4c158c0d7ad12c17d05b383cdb.py" + ], + [ + "zadanie1.py", + "_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py" + ] + ] + }, + "heartbeat": "2023-05-11T17:46:55.740189", + "host": { + "ENV": {}, + "cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz", + "gpus": { + "driver_version": "472.12", + "gpus": [ + { + "model": "NVIDIA GeForce GTX 1070", + "persistence_mode": false, + "total_memory": 8192 + } + ] + }, + "hostname": "JAKUB-HENYK", + "os": [ + "Windows", + "Windows-10-10.0.19041-SP0" + ], + "python_version": "3.8.3" + }, + "meta": { + "command": "my_main", + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false + } + }, + "resources": [], + "result": null, + "start_time": "2023-05-11T17:46:55.705192", + "status": "COMPLETED", + "stop_time": "2023-05-11T17:46:55.740189" +} \ No newline at end of file diff --git a/my_runs/2/config.json b/my_runs/2/config.json new file mode 100644 index 0000000..a12250e --- /dev/null +++ b/my_runs/2/config.json @@ -0,0 +1,5 @@ +{ + "epochs": 10, + "learning_rate": 0.001, + "seed": 330865802 +} \ No newline at end of file diff --git a/my_runs/2/cout.txt b/my_runs/2/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/my_runs/2/metrics.json b/my_runs/2/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/my_runs/2/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_runs/2/run.json b/my_runs/2/run.json new file mode 100644 index 0000000..9893054 --- /dev/null +++ b/my_runs/2/run.json @@ -0,0 +1,97 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "e:\\Pyton\\IUM\\ium_452627", + "dependencies": [ + "numpy==1.20.0", + "pandas==1.4.1", + "sacred==0.8.4", + "torch==1.8.1+cu102", + "torchvision==0.9.1+cu102" + ], + "mainfile": "sacred_train.py", + "name": "sacred_scopes", + "repositories": [ + { + "commit": "da87b5f0c3566577958feaa4005909d12ce8f0c7", + "dirty": false, + "url": "git@git.wmi.amu.edu.pl:s452627/ium_452627.git" + }, + { + "commit": "da87b5f0c3566577958feaa4005909d12ce8f0c7", + "dirty": false, + "url": "git@git.wmi.amu.edu.pl:s452627/ium_452627.git" + } + ], + "sources": [ + [ + "sacred_train.py", + "_sources\\sacred_train_b6fddd734c43fc70e41befe914387fe7.py" + ], + [ + "zadanie1.py", + "_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\kubak\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"e:/Pyton/IUM/ium_452627/sacred_train.py\", line 87, in my_main\n trainNet(trainloader, criterion, optimizer, int(float(epochs)))\n", + " File \"e:/Pyton/IUM/ium_452627/sacred_train.py\", line 46, in trainNet\n outputs = net(inputs)\n", + "NameError: name 'net' is not defined\n" + ], + "heartbeat": "2023-05-11T17:51:09.781453", + "host": { + "ENV": {}, + "cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz", + "gpus": { + "driver_version": "472.12", + "gpus": [ + { + "model": "NVIDIA GeForce GTX 1070", + "persistence_mode": false, + "total_memory": 8192 + } + ] + }, + "hostname": "JAKUB-HENYK", + "os": [ + "Windows", + "Windows-10-10.0.19041-SP0" + ], + "python_version": "3.8.3" + }, + "meta": { + "command": "my_main", + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, 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Training diff --git a/my_runs/3/metrics.json b/my_runs/3/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/my_runs/3/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_runs/3/run.json b/my_runs/3/run.json new file mode 100644 index 0000000..d168fd7 --- /dev/null +++ b/my_runs/3/run.json @@ -0,0 +1,92 @@ +{ + "artifacts": [ + "cifar_net.pth" + ], + "command": "my_main", + "experiment": { + "base_dir": "e:\\Pyton\\IUM\\ium_452627", + "dependencies": [ + "numpy==1.20.0", + "pandas==1.4.1", + "sacred==0.8.4", + "torch==1.8.1+cu102", + "torchvision==0.9.1+cu102" + ], + "mainfile": "sacred_train.py", + "name": "sacred_scopes", + "repositories": [ + { + "commit": "da87b5f0c3566577958feaa4005909d12ce8f0c7", + "dirty": false, + "url": "git@git.wmi.amu.edu.pl:s452627/ium_452627.git" + }, + { + "commit": "da87b5f0c3566577958feaa4005909d12ce8f0c7", + "dirty": false, + "url": "git@git.wmi.amu.edu.pl:s452627/ium_452627.git" + } + ], + "sources": [ + [ + "sacred_train.py", + "_sources\\sacred_train_f2e895e7d4eaf1570420fc943ad98f4b.py" + ], + [ + "zadanie1.py", + "_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py" + ] + ] + }, + "heartbeat": "2023-05-11T17:52:55.082803", + "host": { + "ENV": {}, + "cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz", + "gpus": { + "driver_version": "472.12", + "gpus": [ + { + "model": "NVIDIA GeForce GTX 1070", + "persistence_mode": false, + "total_memory": 8192 + } + ] + }, + "hostname": "JAKUB-HENYK", + "os": [ + "Windows", + "Windows-10-10.0.19041-SP0" + ], + "python_version": "3.8.3" + }, + "meta": { + "command": "my_main", + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + 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+407,Female,51,66852,73,Executive,15,3 +408,Male,18,103896,36,Executive,16,4 +409,Male,21,187761,87,Healthcare,2,3 +410,Male,37,102717,93,Healthcare,7,4 +411,Male,83,102647,83,Entertainment,0,2 +412,Female,96,113585,31,Lawyer,0,4 +413,Female,65,86509,88,Artist,0,4 +414,Male,80,59099,44,Marketing,1,7 +415,Female,75,135088,5,Lawyer,1,5 +416,Female,87,110870,78,Engineer,1,4 +417,Female,86,187667,12,Artist,0,1 +418,Female,36,124614,25,Artist,8,5 +419,Female,78,166492,40,Homemaker,9,7 +420,Female,62,172758,69,Entertainment,1,2 +421,Male,89,122430,19,Healthcare,1,1 +422,Female,56,183540,96,Entertainment,0,6 +423,Female,35,96484,34,Artist,5,4 +424,Male,33,112587,71,Artist,1,4 +425,Female,58,155672,71,Entertainment,7,7 +426,Female,68,72847,93,Artist,1,2 +427,Male,38,71897,32,Entertainment,2,3 +428,Male,31,158479,57,Doctor,1,5 +429,Male,90,89251,4,Entertainment,1,1 +430,Male,28,179911,24,Engineer,1,1 +431,Male,1,111558,85,Healthcare,7,4 +432,Male,78,127615,78,Executive,10,7 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+619,Male,38,103356,20,Marketing,1,6 +620,Female,44,75195,79,Artist,1,3 +621,Male,58,123083,44,Entertainment,6,5 +622,Male,77,73884,30,Healthcare,1,2 +623,Female,56,91180,61,Entertainment,1,7 +624,Male,79,86235,52,Artist,0,2 +625,Female,97,96400,4,Doctor,1,7 +626,Male,78,83599,12,Healthcare,1,6 +627,Female,91,82535,50,Healthcare,0,1 +628,Male,35,92227,8,Lawyer,0,5 +629,Female,43,150444,27,Artist,13,4 +630,Female,37,149095,83,Lawyer,1,2 +631,Male,63,113605,42,Artist,3,3 +632,Female,16,93854,82,Healthcare,0,7 +633,Male,32,118280,92,Artist,0,5 +634,Male,91,94576,90,Artist,0,1 +635,Female,33,129257,70,Executive,6,3 +636,Female,57,70373,10,Healthcare,2,6 +637,Female,43,77600,66,Healthcare,0,1 +638,Female,26,113707,97,Entertainment,0,5 +639,Female,59,189672,8,Doctor,0,7 +640,Female,61,143180,58,Artist,0,5 +641,Female,59,174890,14,Marketing,3,4 +642,Male,66,121377,19,,7,7 +643,Male,26,154857,13,Healthcare,0,7 +644,Female,67,160945,51,Healthcare,0,5 +645,Female,85,119557,35,Artist,0,2 +646,Female,76,187141,33,Doctor,0,7 +647,Female,57,97468,22,Artist,0,1 +648,Female,77,160351,77,Lawyer,8,5 +649,Female,66,178627,94,Healthcare,1,2 +650,Female,49,170188,80,Lawyer,1,6 +651,Female,36,166007,40,Doctor,1,4 +652,Male,74,91794,83,Entertainment,0,7 +653,Female,16,189072,86,Artist,6,3 +654,Male,27,152717,15,Executive,1,6 +655,Female,3,158549,98,Executive,0,3 +656,Male,31,134263,94,Marketing,1,3 +657,Female,30,148236,28,Executive,1,1 +658,Male,94,171677,81,Artist,0,6 +659,Female,31,189446,38,Artist,1,6 +660,Male,49,106633,53,Artist,0,3 +661,Male,49,81084,48,Executive,6,5 +662,Male,53,148759,52,Doctor,7,7 +663,Female,3,117638,95,Healthcare,2,7 +664,Female,91,159537,37,Doctor,4,1 +665,Male,26,79526,6,Marketing,9,7 +666,Male,28,101414,64,,8,1 +667,Female,92,109519,72,Entertainment,1,4 +668,Female,59,156880,70,Artist,0,7 +669,Male,15,157278,58,Artist,0,6 +670,Female,23,144916,84,Engineer,1,7 +671,Male,54,59163,100,Marketing,3,3 +672,Female,93,93991,49,Artist,1,3 +673,Female,65,58318,45,Doctor,1,1 +674,Female,13,180786,88,Lawyer,1,5 +675,Male,71,84622,45,Doctor,0,6 +676,Male,1,92674,59,Artist,8,4 +677,Female,57,87553,94,Artist,1,1 +678,Male,14,74174,79,Entertainment,1,2 +679,Female,77,180831,33,Healthcare,1,2 +680,Female,89,137397,33,Doctor,1,7 +681,Male,17,96154,100,Artist,1,4 +682,Male,86,172477,69,Entertainment,1,3 +683,Male,42,59954,37,Executive,4,4 +684,Female,29,112240,95,Entertainment,0,4 +685,Female,54,145679,35,Artist,0,2 +686,Male,96,136265,49,Artist,12,5 +687,Female,58,74869,12,Healthcare,0,3 +688,Female,52,109425,46,Engineer,0,1 +689,Female,3,126015,57,Healthcare,0,1 +690,Female,13,156951,66,Healthcare,4,7 +691,Female,3,127172,68,Healthcare,1,2 +692,Male,89,113834,22,Doctor,0,3 +693,Male,83,132005,2,Entertainment,0,5 +694,Female,80,171323,78,Lawyer,10,4 +695,Female,74,92355,87,Entertainment,1,4 +696,Male,42,60982,3,Healthcare,0,5 +697,Female,91,51742,33,Artist,8,3 +698,Female,31,138612,71,Executive,9,2 +699,Male,8,158123,84,Artist,1,1 +700,Male,64,164675,65,Executive,0,5 +701,Female,41,172882,27,Healthcare,8,7 +702,Female,62,70119,93,Artist,0,1 +703,Male,41,113998,26,Healthcare,7,7 +704,Male,22,114011,40,,5,7 +705,Male,5,57265,89,Entertainment,7,7 +706,Female,38,130597,2,Artist,6,1 +707,Female,88,121597,7,Engineer,0,5 +708,Male,56,103210,100,Artist,6,6 +709,Male,47,66308,8,Executive,0,4 +710,Male,4,141336,93,Homemaker,2,1 +711,Male,82,153632,76,Entertainment,9,7 +712,Female,31,65520,71,Lawyer,1,2 +713,Female,69,153217,83,Healthcare,1,6 +714,Male,62,92768,75,Doctor,1,1 +715,Female,54,132345,10,Artist,0,2 +716,Female,60,186069,49,Marketing,1,2 +717,Female,31,53802,32,Artist,1,7 +718,Female,17,103739,62,Healthcare,6,4 +719,Female,15,50550,34,Healthcare,9,7 +720,Female,37,145648,21,Healthcare,7,6 +721,Male,36,85737,37,Executive,0,3 +722,Female,27,99950,20,Entertainment,0,4 +723,Female,11,83220,21,Engineer,7,4 +724,Male,83,73410,50,Entertainment,8,5 +725,Female,71,64310,43,Marketing,2,1 +726,Female,20,59844,79,Doctor,0,1 +727,Male,3,67787,1,Executive,1,6 +728,Male,52,172841,4,Engineer,0,1 +729,Male,75,145528,61,Healthcare,1,2 +730,Male,36,173807,42,Artist,0,2 +731,Male,60,185794,69,Entertainment,7,1 +732,Male,8,119780,71,Healthcare,1,3 +733,Female,9,158596,86,Artist,1,2 +734,Female,41,118122,52,Entertainment,0,6 +735,Male,38,99964,11,Healthcare,7,4 +736,Female,69,120752,27,Executive,1,6 +737,Female,33,189630,53,Executive,5,5 +738,Male,64,125510,6,Healthcare,3,3 +739,Male,19,101799,49,Engineer,1,1 +740,Female,88,92914,74,Artist,0,3 +741,Female,91,162465,59,Engineer,9,3 +742,Male,84,163818,27,Engineer,2,1 +743,Female,43,53832,56,Entertainment,4,1 +744,Female,30,140190,49,Engineer,1,4 +745,Male,16,125836,69,Healthcare,1,4 +746,Male,89,100938,27,Entertainment,0,7 +747,Male,5,168670,37,Artist,1,3 +748,Female,54,176885,10,Entertainment,0,3 +749,Female,41,111174,28,Artist,4,5 +750,Male,80,127335,97,Artist,6,6 +751,Male,37,149103,3,Healthcare,10,1 +752,Male,87,167705,31,Healthcare,0,7 +753,Female,50,183548,85,Entertainment,1,4 +754,Female,14,50396,46,Lawyer,0,6 +755,Female,17,63557,3,Marketing,1,1 +756,Female,98,87878,55,Artist,1,6 +757,Male,3,55634,53,Engineer,8,3 +758,Female,83,118110,26,Engineer,1,5 +759,Male,13,129396,36,Artist,6,6 +760,Female,46,62628,64,Entertainment,8,6 +761,Female,25,51700,17,Healthcare,4,6 +762,Female,4,106816,49,Artist,7,5 +763,Male,64,93000,67,Lawyer,0,2 +764,Female,84,144148,71,Doctor,1,4 +765,Male,12,101388,90,Artist,0,2 +766,Female,56,171560,78,Engineer,0,2 +767,Male,58,156366,60,Entertainment,8,1 +768,Female,0,143910,22,Marketing,1,7 +769,Female,25,106569,84,Engineer,1,3 +770,Male,28,173009,25,Healthcare,9,6 +771,Male,78,54252,29,Engineer,0,5 +772,Male,56,104600,57,Entertainment,6,3 +773,Male,91,50250,54,Artist,0,2 +774,Male,77,93242,84,Entertainment,0,7 +775,Female,83,156839,38,Artist,3,7 +776,Female,64,71305,39,Artist,9,2 +777,Male,19,85975,73,Entertainment,0,6 +778,Male,94,188107,52,Engineer,10,2 +779,Male,19,82333,35,Marketing,1,2 +780,Male,84,125016,25,Entertainment,1,7 +781,Female,10,148908,10,Healthcare,2,3 +782,Female,19,58217,70,Artist,0,1 +783,Male,74,76521,29,Engineer,7,4 +784,Female,95,117763,42,Artist,1,2 +785,Female,34,115184,93,Homemaker,1,4 +786,Male,30,54251,7,Artist,2,6 +787,Female,53,112530,24,Entertainment,2,6 +788,Male,67,107409,2,Artist,1,6 +789,Female,23,84793,23,Homemaker,9,6 +790,Female,25,66952,79,Entertainment,1,2 +791,Female,84,153465,11,Lawyer,1,4 +792,Female,1,178214,17,Artist,1,7 +793,Male,70,152069,42,Entertainment,4,4 +794,Female,56,132860,17,Healthcare,7,2 +795,Female,0,147719,86,Lawyer,1,7 +796,Female,91,172915,29,Doctor,9,1 +797,Female,81,148594,29,Entertainment,3,5 +798,Male,33,61986,77,Executive,0,1 +799,Male,75,91456,13,Artist,1,4 +800,Male,11,166247,84,Executive,1,2 +801,Female,61,140516,50,Artist,0,3 +802,Male,81,148208,36,,5,7 +803,Female,62,133822,78,Marketing,0,4 +804,Female,44,175424,32,Artist,4,5 +805,Female,67,121528,92,Healthcare,8,4 +806,Female,30,189369,69,Healthcare,0,3 +807,Female,92,80808,26,Artist,1,1 +808,Male,25,57535,84,Entertainment,1,7 +809,Female,61,163509,51,Healthcare,0,4 +810,Female,72,75884,57,Executive,0,7 +811,Female,89,157759,52,Artist,1,6 +812,Female,37,184813,44,Executive,0,2 +813,Male,59,187253,51,Executive,8,7 +814,Male,41,81718,36,Entertainment,1,3 +815,Male,60,150019,37,Doctor,1,2 +816,Female,81,53452,73,Entertainment,1,6 +817,Male,85,150765,42,Homemaker,8,2 +818,Female,91,154456,71,,0,7 +819,Female,65,56647,12,Doctor,6,4 +820,Female,19,117477,48,Healthcare,6,7 +821,Female,78,79677,9,Artist,10,7 +822,Female,0,116759,28,Marketing,7,3 +823,Male,26,180702,21,Entertainment,6,1 +824,Female,2,122698,100,Entertainment,8,1 +825,Female,60,169346,55,Healthcare,7,2 +826,Female,67,93176,25,Artist,4,1 +827,Female,4,75853,40,Homemaker,4,5 +828,Male,7,119362,58,Engineer,8,1 +829,Male,28,81545,36,Artist,4,7 +830,Male,67,67372,75,Artist,0,7 +831,Female,30,109703,61,Executive,1,1 +832,Male,38,166697,39,Marketing,13,4 +833,Female,40,124982,34,Engineer,1,7 +834,Female,6,146776,83,Lawyer,1,7 +835,Female,3,147548,45,Executive,4,5 +836,Female,51,117620,60,Healthcare,7,6 +837,Male,63,85351,57,Healthcare,0,2 +838,Male,65,174432,8,Artist,0,7 +839,Male,44,70787,68,Doctor,1,3 +840,Female,90,75473,63,Healthcare,0,7 +841,Male,76,90082,49,Healthcare,1,6 +842,Female,62,185388,54,Healthcare,0,2 +843,Female,5,168110,69,Doctor,14,6 +844,Female,67,184628,38,Artist,12,2 +845,Female,38,176551,45,Healthcare,8,1 +846,Female,31,76422,21,Healthcare,9,7 +847,Male,2,164248,51,Entertainment,9,3 +848,Female,82,109188,38,Lawyer,5,7 +849,Female,66,132951,26,Healthcare,4,1 +850,Female,75,164598,32,Healthcare,8,1 +851,Male,69,186655,32,,7,2 +852,Female,62,111859,86,Healthcare,6,1 +853,Female,0,121926,41,Lawyer,7,1 +854,Female,11,143047,42,Executive,1,3 +855,Female,22,131748,23,Artist,1,3 +856,Male,76,142801,23,Lawyer,0,1 +857,Male,85,142723,46,Engineer,0,6 +858,Male,21,155151,16,Marketing,14,3 +859,Female,64,153607,48,Healthcare,1,4 +860,Male,94,144192,34,Healthcare,0,3 +861,Female,83,174159,22,Lawyer,4,4 +862,Female,93,73642,50,Marketing,0,3 +863,Female,12,170319,46,Artist,8,7 +864,Female,0,141351,10,Engineer,0,2 +865,Female,79,167411,9,Marketing,12,5 +866,Male,90,58289,49,Artist,5,6 +867,Female,45,79801,77,Doctor,0,2 +868,Female,82,146118,52,Marketing,8,7 +869,Female,57,57337,14,Healthcare,1,4 +870,Female,43,113575,6,Entertainment,1,2 +871,Male,63,122658,65,Engineer,6,2 +872,Female,62,151836,13,Artist,0,6 +873,Female,43,185900,80,Homemaker,9,4 +874,Female,34,94029,98,Healthcare,1,4 +875,Female,30,157290,22,Artist,2,2 +876,Male,97,175996,42,Entertainment,10,4 +877,Male,68,102016,37,Artist,0,4 +878,Male,16,77902,67,Artist,1,2 +879,Female,15,81138,86,Healthcare,9,7 +880,Male,25,128258,48,Executive,0,2 +881,Female,29,62475,24,Artist,6,4 +882,Female,85,82854,75,Artist,6,4 +883,Female,30,59284,38,Doctor,7,7 +884,Female,13,141580,92,Healthcare,0,4 +885,Female,88,171362,90,Executive,6,4 +886,Male,5,78274,65,Artist,9,2 +887,Female,95,76646,61,Artist,0,1 +888,Female,92,78308,66,Executive,5,2 +889,Female,63,78971,89,Engineer,1,2 +890,Female,75,65528,73,Marketing,1,5 +891,Male,67,94199,71,Artist,5,7 +892,Female,58,135705,48,Engineer,8,7 +893,Male,52,130573,31,Entertainment,8,3 +894,Female,85,152303,68,Artist,1,5 +895,Female,31,144582,89,Doctor,1,1 +896,Female,84,93288,60,Artist,10,6 +897,Female,93,88192,3,Marketing,10,2 +898,Male,18,62362,81,Lawyer,7,3 +899,Male,73,74350,39,Lawyer,10,1 +900,Male,3,144103,42,Healthcare,8,7 +901,Male,1,75518,17,Lawyer,7,3 +902,Male,53,160980,62,Doctor,4,2 +903,Female,75,102904,28,Artist,7,6 +904,Female,15,174501,37,,9,7 +905,Female,47,157661,57,Healthcare,6,6 +906,Female,66,118385,8,Healthcare,10,3 +907,Female,17,116463,100,Artist,8,4 +908,Female,1,161212,14,Artist,9,6 +909,Male,16,172601,50,Marketing,4,6 +910,Female,52,167214,94,Homemaker,8,7 +911,Male,21,69644,19,Artist,5,1 +912,Female,19,120215,46,Homemaker,7,2 +913,Female,29,180401,23,Engineer,7,6 +914,Female,40,113739,93,Artist,5,7 +915,Male,76,111717,41,Healthcare,1,2 +916,Male,91,62808,20,Engineer,0,4 +917,Female,24,67582,13,Artist,1,3 +918,Male,4,96543,60,Artist,9,1 +919,Male,90,89667,69,Artist,0,2 +920,Female,56,98980,65,Engineer,0,1 +921,Male,14,124998,17,Lawyer,1,3 +922,Male,52,189091,12,Artist,0,6 +923,Female,14,153842,3,Artist,1,3 +924,Male,30,106314,16,Artist,0,7 +925,Female,70,62901,60,Artist,8,5 +926,Male,14,149618,78,Healthcare,4,6 +927,Female,95,72068,97,Healthcare,0,6 +928,Male,25,81367,87,,0,3 +929,Female,95,118575,10,Entertainment,1,3 +930,Female,12,82382,97,Executive,1,3 +931,Male,82,114776,55,Entertainment,10,3 +932,Female,2,145597,81,Artist,1,6 +933,Male,51,162072,86,Engineer,1,1 +934,Male,63,172872,11,Healthcare,0,7 +935,Female,30,182162,6,Artist,1,3 +936,Female,68,71467,81,Artist,5,7 +937,Female,21,85568,1,Healthcare,0,6 +938,Female,99,56851,72,Entertainment,6,3 +939,Female,0,116782,73,Artist,9,5 +940,Female,80,67781,1,Entertainment,4,4 +941,Female,51,70152,29,Healthcare,0,2 +942,Male,18,185861,82,Artist,3,6 +943,Male,43,162595,17,Artist,3,3 +944,Female,29,80098,54,Lawyer,10,6 +945,Female,20,184801,8,Lawyer,1,1 +946,Female,39,58908,37,Artist,6,6 +947,Female,9,146011,69,Marketing,9,5 +948,Female,45,135174,39,Artist,9,4 +949,Female,65,114244,64,Artist,0,1 +950,Female,64,157207,52,Healthcare,0,4 +951,Female,81,128834,23,Entertainment,5,4 +952,Male,54,51166,24,Entertainment,0,7 +953,Female,32,130118,36,Artist,0,4 +954,Male,29,87014,71,Doctor,11,4 +955,Female,25,111379,93,Artist,0,2 +956,Male,5,83704,82,Entertainment,1,1 +957,Female,55,90558,49,Healthcare,8,2 +958,Male,12,187343,14,Healthcare,6,3 +959,Female,81,111174,48,Entertainment,9,6 +960,Male,87,64461,63,Engineer,4,4 +961,Male,12,109618,97,Artist,7,3 +962,Male,91,189357,4,Lawyer,7,1 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+1016,Female,32,100890,60,Entertainment,4,4 +1017,Female,99,55972,83,Entertainment,1,7 +1018,Female,85,134282,39,Artist,2,5 +1019,Female,25,106669,22,Lawyer,1,7 +1020,Female,99,145777,94,Executive,1,5 +1021,Male,41,119259,91,Healthcare,1,5 +1022,Female,1,150174,59,Artist,2,2 +1023,Female,45,55472,51,Healthcare,1,3 +1024,Male,55,99423,82,Healthcare,0,5 +1025,Female,20,146221,25,Marketing,0,3 +1026,Female,33,84238,31,Artist,1,2 +1027,Male,92,57032,75,Entertainment,1,5 +1028,Male,93,88811,52,Engineer,0,4 +1029,Male,60,167420,38,Artist,5,2 +1030,Male,38,101846,17,Doctor,3,2 +1031,Male,15,114061,87,Artist,2,5 +1032,Male,74,147449,16,Artist,1,5 +1033,Female,47,83043,76,Executive,0,1 +1034,Female,96,168990,81,Doctor,9,5 +1035,Male,97,73867,65,Healthcare,4,5 +1036,Female,35,165772,51,Healthcare,1,4 +1037,Female,19,178217,38,Marketing,0,3 +1038,Male,60,162612,98,Engineer,1,6 +1039,Male,37,99872,53,Entertainment,11,3 +1040,Female,49,142566,77,Artist,1,7 +1041,Female,1,111424,80,Entertainment,8,5 +1042,Male,43,187841,38,Artist,1,4 +1043,Female,46,171921,50,Artist,1,6 +1044,Female,65,115299,87,Artist,6,3 +1045,Male,75,94129,27,Healthcare,6,7 +1046,Male,40,172378,39,Entertainment,5,6 +1047,Male,28,146597,31,Engineer,1,4 +1048,Female,78,64057,44,Doctor,0,3 +1049,Female,22,163585,49,Artist,9,5 +1050,Male,6,93186,83,Entertainment,1,3 +1051,Male,46,149764,1,Entertainment,0,6 +1052,Male,17,116822,84,Doctor,1,3 +1053,Female,46,109346,37,Entertainment,1,2 +1054,Female,72,111587,99,Artist,7,4 +1055,Female,38,166644,66,Artist,8,6 +1056,Female,50,181270,56,Artist,1,1 +1057,Male,47,65207,81,Healthcare,1,2 +1058,Female,72,112146,21,Homemaker,8,4 +1059,Male,8,104953,57,Doctor,1,6 +1060,Female,34,139009,24,Artist,0,4 +1061,Female,53,67916,82,Healthcare,0,6 +1062,Female,82,99983,88,Executive,0,1 +1063,Male,63,131122,40,Artist,9,6 +1064,Female,58,173190,37,Doctor,9,4 +1065,Male,23,122712,21,Marketing,1,1 +1066,Female,80,55792,30,Artist,1,6 +1067,Male,45,73892,45,Doctor,1,4 +1068,Female,30,78821,46,,1,4 +1069,Female,84,152475,12,Artist,0,7 +1070,Male,45,152226,87,Artist,4,4 +1071,Male,85,117679,18,Entertainment,1,3 +1072,Male,23,146473,87,Artist,1,4 +1073,Male,79,66051,75,Healthcare,1,7 +1074,Male,47,162332,72,Entertainment,1,5 +1075,Female,93,166018,71,Doctor,0,3 +1076,Female,95,67398,81,Entertainment,2,5 +1077,Male,44,73760,70,Entertainment,1,6 +1078,Male,95,167578,66,Entertainment,0,1 +1079,Male,28,130267,45,Engineer,8,4 +1080,Male,91,94846,95,Engineer,0,5 +1081,Female,81,155396,1,Engineer,9,5 +1082,Female,90,138013,16,Artist,8,7 +1083,Male,79,148286,77,Entertainment,0,4 +1084,Female,6,101031,28,Artist,1,1 +1085,Female,38,52884,24,Lawyer,1,1 +1086,Male,87,153230,15,Healthcare,8,1 +1087,Female,93,63261,13,Engineer,1,7 +1088,Male,44,90167,84,Engineer,0,3 +1089,Female,10,162076,78,,0,3 +1090,Female,84,53622,27,Artist,0,3 +1091,Female,7,172913,94,Executive,3,4 +1092,Female,41,94316,86,Artist,0,6 +1093,Male,87,141383,3,,7,5 +1094,Female,33,67874,55,Artist,0,2 +1095,Female,34,176554,62,Healthcare,1,2 +1096,Female,53,135341,56,Healthcare,10,2 +1097,Female,47,129019,57,Healthcare,0,3 +1098,Male,53,107879,3,Doctor,1,6 +1099,Male,92,79531,4,Artist,1,1 +1100,Male,89,63379,59,Artist,12,7 +1101,Female,27,156258,54,Entertainment,0,4 +1102,Male,70,67874,41,Entertainment,9,3 +1103,Female,86,159964,57,Engineer,5,6 +1104,Female,99,103706,50,Entertainment,1,2 +1105,Female,96,64713,84,Entertainment,2,7 +1106,Female,65,90414,55,Artist,1,5 +1107,Female,82,171100,34,Executive,0,5 +1108,Male,36,187305,85,Engineer,6,7 +1109,Female,30,139758,46,Lawyer,0,6 +1110,Female,96,91226,56,Healthcare,0,5 +1111,Female,69,143928,30,Engineer,9,7 +1112,Female,34,92366,14,Healthcare,0,6 +1113,Male,74,175041,25,Artist,4,1 +1114,Male,97,147301,93,Lawyer,1,5 +1115,Male,56,119296,49,Engineer,0,7 +1116,Female,30,126306,44,Artist,0,1 +1117,Male,13,100647,49,Executive,1,3 +1118,Female,95,168663,16,Engineer,11,5 +1119,Female,2,105391,42,Executive,1,7 +1120,Female,78,175179,43,Executive,9,4 +1121,Female,54,165019,12,Entertainment,1,2 +1122,Female,85,68212,26,Healthcare,7,4 +1123,Male,13,64572,71,Artist,1,2 +1124,Female,85,92591,99,Artist,8,2 +1125,Female,41,69730,97,Artist,1,4 +1126,Female,25,156793,57,Marketing,2,4 +1127,Female,74,56848,41,Artist,5,1 +1128,Male,8,148450,55,Healthcare,0,6 +1129,Male,62,150619,32,Artist,10,6 +1130,Male,52,100261,10,Artist,1,6 +1131,Female,36,109895,56,Healthcare,5,2 +1132,Male,11,152684,24,Lawyer,0,3 +1133,Female,84,133765,83,Artist,1,3 +1134,Female,99,57677,99,Lawyer,0,5 +1135,Female,51,112358,27,Artist,0,6 +1136,Female,57,73845,52,Artist,1,7 +1137,Male,92,89531,42,Healthcare,6,2 +1138,Male,29,118092,100,Entertainment,7,3 +1139,Male,62,144242,49,Entertainment,1,4 +1140,Female,43,140876,90,Entertainment,0,4 +1141,Male,72,76888,26,Marketing,0,1 +1142,Female,28,116644,88,Artist,6,3 +1143,Female,57,143021,3,Executive,5,4 +1144,Female,86,145679,91,Lawyer,9,6 +1145,Female,26,64398,7,Lawyer,1,6 +1146,Female,13,104512,40,Marketing,9,3 +1147,Male,1,71564,68,Artist,1,5 +1148,Female,89,122707,64,Homemaker,2,3 +1149,Female,91,51733,96,Healthcare,0,5 +1150,Female,67,138087,63,Marketing,1,7 +1151,Male,63,147278,51,Lawyer,0,4 +1152,Female,90,187339,62,Healthcare,0,6 +1153,Female,87,129972,97,Executive,1,6 +1154,Female,89,151176,55,Healthcare,1,2 +1155,Female,34,74358,60,Artist,0,6 +1156,Male,89,83953,82,Healthcare,0,3 +1157,Male,31,62925,24,Engineer,1,6 +1158,Male,55,65257,93,Doctor,2,3 +1159,Female,3,188557,4,Healthcare,9,2 +1160,Male,64,163783,84,Healthcare,3,1 +1161,Female,1,108342,97,Doctor,0,3 +1162,Female,20,155951,21,Healthcare,7,4 +1163,Female,18,183071,29,Artist,7,4 +1164,Female,83,124104,20,Healthcare,4,6 +1165,Female,21,136747,3,Healthcare,1,1 +1166,Male,19,189498,18,Artist,4,1 +1167,Female,74,76149,48,Artist,9,6 +1168,Female,44,185550,54,Artist,0,1 +1169,Female,52,119567,19,Artist,5,2 +1170,Female,14,79806,30,Engineer,1,6 +1171,Male,10,180774,66,Entertainment,1,5 +1172,Female,80,90142,50,Lawyer,1,2 +1173,Female,82,184481,84,Executive,0,2 +1174,Female,32,66089,75,Artist,6,3 +1175,Female,16,60044,83,Artist,4,1 +1176,Male,61,52543,23,Executive,0,6 +1177,Male,57,50429,23,Artist,0,1 +1178,Male,95,178228,45,Engineer,1,2 +1179,Female,38,161130,98,Artist,8,3 +1180,Male,70,87976,38,Lawyer,1,6 +1181,Female,40,179150,91,Artist,5,2 +1182,Female,92,163442,32,Executive,1,6 +1183,Female,73,108159,18,Entertainment,8,7 +1184,Female,62,73419,14,Healthcare,0,1 +1185,Female,12,138425,78,Doctor,11,6 +1186,Male,65,54431,93,Healthcare,1,7 +1187,Female,88,168437,81,Artist,2,2 +1188,Female,55,71322,54,Entertainment,1,6 +1189,Female,99,122548,14,,2,5 +1190,Female,20,141428,32,Entertainment,1,5 +1191,Male,97,142214,32,Artist,7,4 +1192,Female,93,62590,43,Artist,9,6 +1193,Male,84,172523,83,Artist,6,2 +1194,Female,16,157588,76,Executive,2,6 +1195,Female,10,143313,57,Doctor,4,7 +1196,Female,59,119833,38,Doctor,1,2 +1197,Female,54,70803,35,Entertainment,0,5 +1198,Male,82,139008,47,Artist,1,4 +1199,Male,83,126669,53,Artist,1,6 +1200,Male,87,122722,10,Executive,9,4 +1201,Male,1,79800,47,Artist,0,1 +1202,Male,89,100155,5,Healthcare,6,1 +1203,Female,32,110846,49,Artist,0,2 +1204,Female,1,106841,69,Entertainment,0,3 +1205,Female,54,117181,66,Doctor,12,5 +1206,Female,56,86898,76,Healthcare,0,6 +1207,Female,31,143241,81,Executive,4,7 +1208,Female,26,146028,33,Healthcare,0,5 +1209,Male,7,165287,65,Doctor,1,3 +1210,Female,60,132486,9,Artist,1,2 +1211,Male,92,96424,74,Artist,8,5 +1212,Female,93,151468,41,Artist,1,5 +1213,Female,8,76561,79,Healthcare,0,1 +1214,Female,81,125316,81,Lawyer,0,6 +1215,Male,21,109373,22,Entertainment,4,2 +1216,Male,96,135042,12,Artist,0,7 +1217,Female,78,59784,81,Artist,3,3 +1218,Male,86,140687,63,Artist,1,2 +1219,Male,1,182927,55,Healthcare,1,1 +1220,Female,62,175534,44,Artist,1,6 +1221,Male,68,170957,6,Lawyer,1,3 +1222,Male,70,81197,76,Artist,0,4 +1223,Female,21,104888,8,Healthcare,0,4 +1224,Male,74,184838,18,,8,5 +1225,Female,62,139588,66,Executive,0,2 +1226,Male,22,126543,45,Homemaker,0,3 +1227,Female,87,177260,45,Entertainment,14,2 +1228,Male,44,156823,42,Healthcare,9,5 +1229,Female,27,183201,52,Engineer,8,6 +1230,Female,95,84925,46,Artist,4,6 +1231,Male,89,69719,47,Artist,9,2 +1232,Female,41,61355,13,Healthcare,1,3 +1233,Male,41,156014,49,Doctor,14,5 +1234,Male,21,184088,21,Entertainment,11,7 +1235,Female,59,158372,98,Artist,1,1 +1236,Female,64,135742,67,Healthcare,1,7 +1237,Female,38,145704,82,Marketing,0,4 +1238,Female,70,157307,23,Executive,4,3 +1239,Female,25,88064,73,Executive,1,5 +1240,Female,3,167226,59,Homemaker,3,1 +1241,Female,70,153821,8,Marketing,1,4 +1242,Male,29,170669,78,Doctor,9,5 +1243,Male,88,93555,13,Artist,3,3 +1244,Female,20,101941,18,Healthcare,1,3 +1245,Female,58,78795,44,Engineer,0,6 +1246,Female,70,55875,94,Engineer,0,5 +1247,Female,44,177513,81,Lawyer,1,5 +1248,Female,87,100850,83,Doctor,0,3 +1249,Female,0,143082,63,Entertainment,0,7 +1250,Female,96,85447,4,Artist,9,6 +1251,Female,75,114213,33,Doctor,9,4 +1252,Male,14,161120,69,Executive,0,1 +1253,Female,8,186682,86,Entertainment,1,1 +1254,Male,58,105302,75,Doctor,0,3 +1255,Female,5,133400,47,Engineer,0,6 +1256,Male,70,56432,23,Engineer,0,5 +1257,Female,49,136467,72,Healthcare,3,3 +1258,Male,60,189945,20,Engineer,0,5 +1259,Female,25,163966,48,Lawyer,1,5 +1260,Male,47,52152,17,Artist,0,3 +1261,Male,53,145720,62,Homemaker,12,7 +1262,Male,24,188034,61,Healthcare,1,5 +1263,Female,46,127911,3,Homemaker,9,2 +1264,Female,70,174114,31,Lawyer,1,1 +1265,Male,53,144489,10,Artist,14,3 +1266,Male,63,77440,47,Lawyer,0,5 +1267,Female,24,86119,44,Artist,1,6 +1268,Female,5,100056,49,Entertainment,1,3 +1269,Male,7,66686,50,Artist,8,7 +1270,Female,71,74696,41,Doctor,1,6 +1271,Male,23,65641,50,Entertainment,4,5 +1272,Female,0,61228,81,Entertainment,1,6 +1273,Female,2,138529,87,Healthcare,0,4 +1274,Female,72,152405,4,Artist,1,4 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+1301,Female,61,146350,43,Artist,1,3 +1302,Female,77,81112,61,Healthcare,5,3 +1303,Male,52,80605,20,Doctor,1,6 +1304,Male,40,171949,11,Doctor,1,6 +1305,Male,93,159006,26,Artist,8,6 +1306,Female,45,177368,96,Healthcare,1,2 +1307,Female,83,187643,3,Artist,1,1 +1308,Male,23,93841,25,Lawyer,0,7 +1309,Male,15,131061,92,Healthcare,0,7 +1310,Male,79,146130,20,Entertainment,6,6 +1311,Male,80,84471,79,Doctor,1,5 +1312,Female,35,144485,15,Entertainment,0,4 +1313,Female,39,158638,78,Healthcare,2,5 +1314,Male,4,96693,28,Entertainment,0,4 +1315,Female,83,129507,93,Healthcare,4,7 +1316,Female,94,114510,39,Healthcare,0,4 +1317,Female,33,109396,17,,1,2 +1318,Female,12,149031,96,Artist,10,5 +1319,Female,77,66256,13,Lawyer,1,2 +1320,Female,48,118285,69,Doctor,8,4 +1321,Male,97,128723,58,Artist,4,5 +1322,Female,28,178285,91,Artist,4,7 +1323,Female,99,144176,74,Marketing,7,2 +1324,Male,27,82474,31,Artist,9,3 +1325,Female,95,159010,84,Lawyer,6,2 +1326,Female,6,76164,83,Artist,6,2 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+1381,Female,21,123904,91,Artist,7,5 +1382,Female,62,89801,88,Artist,5,4 +1383,Male,73,173466,57,Entertainment,10,5 +1384,Female,34,55690,4,Marketing,4,6 +1385,Female,50,104467,72,,5,1 +1386,Male,39,100419,19,Artist,5,4 +1387,Female,82,62951,47,Lawyer,8,2 +1388,Male,39,118554,1,Lawyer,4,6 +1389,Female,13,140673,49,Artist,6,7 +1390,Female,68,75561,87,Healthcare,5,3 +1391,Female,77,147306,50,Healthcare,6,6 +1392,Female,35,111871,30,Doctor,10,1 +1393,Male,5,112884,43,Healthcare,0,6 +1394,Female,47,121267,58,Engineer,1,1 +1395,Female,48,151519,93,Artist,7,2 +1396,Female,34,105448,40,Artist,0,5 +1397,Female,20,92048,91,Healthcare,1,1 +1398,Male,61,99980,94,Healthcare,10,1 +1399,Male,16,84003,68,Artist,9,6 +1400,Male,2,110889,95,Executive,1,2 +1401,Female,52,82478,95,Executive,10,1 +1402,Male,99,139685,20,Healthcare,1,4 +1403,Female,84,182374,62,Entertainment,0,6 +1404,Female,58,119366,94,Engineer,4,2 +1405,Female,91,53788,57,Doctor,0,2 +1406,Female,1,90341,92,Doctor,0,2 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+1433,Female,25,136146,98,Artist,6,6 +1434,Female,14,76001,46,Artist,7,3 +1435,Female,12,84885,24,Healthcare,6,2 +1436,Female,18,82812,1,Artist,6,2 +1437,Male,74,51260,43,Entertainment,5,5 +1438,Male,21,159572,42,Healthcare,5,5 +1439,Male,9,130186,88,Artist,9,7 +1440,Female,49,176954,57,Artist,5,6 +1441,Male,58,85908,99,Artist,5,4 +1442,Female,16,75831,5,Healthcare,10,7 +1443,Female,55,123045,56,Engineer,7,3 +1444,Female,31,161211,10,Lawyer,9,1 +1445,Female,59,81702,27,Marketing,6,4 +1446,Female,25,173043,3,Artist,8,6 +1447,Male,63,126380,90,Artist,6,1 +1448,Female,60,128205,27,Healthcare,6,5 +1449,Female,41,181355,73,Artist,10,3 +1450,Female,84,97931,70,Healthcare,9,7 +1451,Male,72,146432,25,Artist,6,1 +1452,Female,97,125731,41,Artist,6,4 +1453,Female,43,132701,70,Healthcare,5,2 +1454,Female,63,116095,8,Artist,5,1 +1455,Female,7,135773,42,Artist,9,5 +1456,Male,54,136417,45,Healthcare,9,6 +1457,Male,27,80783,82,Artist,9,3 +1458,Male,28,182580,60,Executive,6,6 +1459,Female,84,143567,48,Artist,7,2 +1460,Male,71,85908,85,Entertainment,5,4 +1461,Female,88,139806,99,Entertainment,4,4 +1462,Female,75,77269,24,Artist,8,1 +1463,Female,96,105968,65,Engineer,4,5 +1464,Female,83,125660,19,Engineer,10,4 +1465,Female,86,98444,76,Entertainment,7,4 +1466,Male,60,179503,9,Artist,4,3 +1467,Female,5,60575,14,Engineer,5,2 +1468,Female,65,75685,19,Marketing,7,3 +1469,Female,49,85374,45,Artist,8,5 +1470,Female,78,128642,19,Marketing,5,6 +1471,Male,25,182292,78,Artist,7,5 +1472,Female,5,124702,94,Artist,10,3 +1473,Female,58,127833,2,Artist,7,7 +1474,Female,80,178866,97,Artist,8,4 +1475,Female,94,183643,51,Artist,5,5 +1476,Male,62,113306,4,Executive,5,6 +1477,Male,20,84008,77,Artist,8,4 +1478,Male,85,85758,35,Lawyer,10,4 +1479,Female,72,63128,45,Engineer,8,7 +1480,Male,95,70174,6,Executive,10,6 +1481,Female,97,170133,42,Engineer,5,5 +1482,Female,25,165978,42,Artist,7,3 +1483,Female,17,149727,78,Artist,7,3 +1484,Female,19,147052,86,,9,4 +1485,Female,59,113389,79,Doctor,4,6 +1486,Male,30,111690,73,Entertainment,10,4 +1487,Female,63,184830,60,Doctor,9,3 +1488,Female,23,91549,23,Artist,4,3 +1489,Female,97,94629,17,Marketing,6,3 +1490,Female,97,167558,99,Artist,4,7 +1491,Male,94,119189,57,Artist,9,3 +1492,Female,28,96207,67,Artist,5,5 +1493,Male,21,85339,6,Doctor,4,7 +1494,Female,25,111942,7,Artist,5,5 +1495,Female,48,157003,45,Engineer,5,4 +1496,Female,53,117123,23,Artist,9,7 +1497,Female,97,172747,76,Artist,7,2 +1498,Male,33,157358,52,Healthcare,7,4 +1499,Male,5,156655,20,Entertainment,4,5 +1500,Male,72,166043,93,Doctor,7,6 +1501,Male,40,67768,15,Doctor,10,6 +1502,Male,34,171353,34,Doctor,8,4 +1503,Female,63,116971,76,Executive,7,5 +1504,Female,85,97822,38,Doctor,5,3 +1505,Female,86,117203,83,Healthcare,10,3 +1506,Female,63,88720,100,Artist,10,1 +1507,Female,9,173233,18,,5,1 +1508,Female,76,139447,36,Artist,8,7 +1509,Male,19,179269,93,Entertainment,6,3 +1510,Female,84,136254,90,Engineer,5,6 +1511,Male,38,174594,56,Executive,8,2 +1512,Female,1,100429,19,Entertainment,9,1 +1513,Female,30,73107,48,Artist,9,1 +1514,Female,86,98344,25,Artist,6,6 +1515,Male,21,163509,21,Healthcare,10,1 +1516,Male,15,116204,16,Engineer,8,2 +1517,Female,87,95853,53,Healthcare,9,1 +1518,Male,40,156411,70,Artist,6,5 +1519,Male,2,72889,36,Homemaker,5,7 +1520,Female,63,106691,79,Healthcare,9,3 +1521,Male,26,144832,42,Artist,5,3 +1522,Male,29,132297,53,Entertainment,8,1 +1523,Female,7,170345,3,Executive,9,5 +1524,Male,28,157383,13,Lawyer,8,2 +1525,Female,99,150782,18,Executive,8,2 +1526,Male,69,143987,87,Healthcare,10,5 +1527,Female,13,129659,59,Entertainment,6,7 +1528,Male,73,104826,36,Healthcare,5,1 +1529,Female,4,181471,59,Healthcare,10,4 +1530,Female,42,152547,56,Artist,7,4 +1531,Male,43,85347,9,Doctor,1,1 +1532,Female,79,122828,32,Executive,0,5 +1533,Male,18,107794,17,Engineer,0,5 +1534,Male,39,152566,11,Entertainment,1,7 +1535,Female,41,91715,99,Entertainment,0,3 +1536,Female,60,99222,48,Artist,1,6 +1537,Female,39,70280,99,Engineer,0,4 +1538,Female,90,70030,86,Healthcare,1,2 +1539,Female,83,102083,24,Artist,1,1 +1540,Female,92,166163,87,Engineer,8,5 +1541,Female,6,182871,27,Engineer,8,6 +1542,Male,50,187545,61,Executive,4,6 +1543,Male,90,182910,86,Entertainment,0,1 +1544,Female,13,174530,30,Lawyer,1,6 +1545,Female,27,115438,37,Artist,0,6 +1546,Female,93,93964,80,Artist,4,2 +1547,Female,61,180341,75,Healthcare,1,3 +1548,Female,91,107323,1,Engineer,7,7 +1549,Female,79,119435,55,Entertainment,0,7 +1550,Female,80,68341,22,Artist,13,6 +1551,Female,16,95365,60,Artist,0,5 +1552,Male,4,131093,79,Lawyer,1,7 +1553,Female,34,100621,3,Healthcare,1,1 +1554,Male,10,116521,47,Doctor,0,2 +1555,Female,3,136316,88,Executive,0,3 +1556,Male,96,172039,51,Lawyer,0,6 +1557,Female,48,70844,22,Entertainment,0,3 +1558,Male,25,100964,96,Marketing,1,6 +1559,Female,98,128037,39,Entertainment,6,2 +1560,Male,84,97081,75,Marketing,1,3 +1561,Male,88,92247,47,Marketing,0,6 +1562,Male,86,129914,92,Artist,1,3 +1563,Female,0,94014,53,Engineer,9,5 +1564,Female,56,138864,93,Artist,0,5 +1565,Male,6,169299,46,Marketing,1,5 +1566,Male,2,119097,40,Healthcare,0,7 +1567,Female,73,68161,23,Doctor,1,2 +1568,Female,38,181108,32,Homemaker,1,1 +1569,Male,37,87764,59,Engineer,9,7 +1570,Female,41,102543,54,Artist,7,3 +1571,Male,94,139417,13,Artist,1,3 +1572,Female,57,81802,29,Homemaker,9,1 +1573,Female,96,102788,51,Artist,0,1 +1574,Female,59,163179,19,Healthcare,1,7 +1575,Male,87,71318,22,Healthcare,1,3 +1576,Male,55,63683,34,Healthcare,3,1 +1577,Female,16,189689,37,Healthcare,8,5 +1578,Male,82,121610,36,Artist,0,5 +1579,Female,83,150682,25,Artist,1,1 +1580,Female,96,124550,46,Marketing,0,3 +1581,Male,55,148875,99,Artist,0,4 +1582,Male,6,107117,44,Artist,1,3 +1583,Male,33,109223,43,Engineer,1,5 +1584,Female,0,120899,7,Marketing,2,6 +1585,Female,27,101948,31,Artist,0,5 +1586,Male,72,143169,58,Engineer,0,4 +1587,Female,13,167619,4,Healthcare,7,6 +1588,Female,19,181795,59,Healthcare,3,5 +1589,Female,48,186445,41,Engineer,0,1 +1590,Female,58,102205,24,Artist,1,1 +1591,Female,21,52326,69,Healthcare,0,2 +1592,Male,91,82992,5,Entertainment,1,5 +1593,Male,44,145520,55,Homemaker,1,7 +1594,Female,80,104820,96,Artist,2,4 +1595,Female,28,69025,79,Entertainment,0,2 +1596,Male,74,164976,89,Entertainment,0,4 +1597,Female,79,178576,51,Executive,5,5 +1598,Female,42,72415,52,Marketing,0,5 +1599,Male,80,112146,24,Entertainment,0,3 +1600,Male,8,144830,43,Artist,0,1 +1601,Female,24,51661,79,Entertainment,0,7 +1602,Female,71,152910,90,Artist,1,6 +1603,Male,1,114556,64,Entertainment,0,7 +1604,Male,0,143455,62,Healthcare,1,6 +1605,Male,30,173096,92,Healthcare,0,3 +1606,Female,52,121172,97,Healthcare,5,4 +1607,Female,34,56492,15,Homemaker,8,1 +1608,Male,64,156422,39,Entertainment,1,7 +1609,Male,17,131863,94,Artist,1,7 +1610,Male,71,118096,36,Lawyer,0,3 +1611,Male,87,149685,30,Executive,9,6 +1612,Female,68,89765,28,Artist,0,7 +1613,Female,14,177701,49,Entertainment,0,6 +1614,Male,24,64738,67,Artist,0,6 +1615,Female,10,92734,28,Lawyer,1,4 +1616,Female,53,146877,47,Healthcare,4,3 +1617,Female,39,62935,60,Artist,0,7 +1618,Female,16,67178,17,Entertainment,8,3 +1619,Female,64,115754,35,Engineer,1,3 +1620,Female,41,186470,21,Artist,0,7 +1621,Male,16,100939,34,Healthcare,7,6 +1622,Female,94,86172,55,Artist,4,4 +1623,Female,40,184222,17,Healthcare,0,2 +1624,Male,81,71543,1,Entertainment,1,4 +1625,Female,18,174836,63,Healthcare,0,4 +1626,Female,78,130706,72,,3,6 +1627,Male,8,74407,100,Doctor,0,6 +1628,Male,64,130055,46,Healthcare,2,1 +1629,Male,45,88707,81,Healthcare,0,5 +1630,Male,99,162762,52,Healthcare,1,1 +1631,Male,59,154821,77,Doctor,0,2 +1632,Male,53,57248,66,Lawyer,5,5 +1633,Female,66,99902,54,Engineer,10,2 +1634,Female,1,165711,50,Artist,1,2 +1635,Male,73,97455,55,,6,4 +1636,Female,34,93866,43,Entertainment,9,2 +1637,Female,1,141307,17,Healthcare,1,3 +1638,Male,73,172011,9,Doctor,1,5 +1639,Male,90,68138,18,Healthcare,2,7 +1640,Female,32,123246,100,Homemaker,8,6 +1641,Female,47,124194,32,Doctor,1,7 +1642,Male,15,188696,87,Homemaker,8,3 +1643,Female,78,91213,84,Lawyer,3,5 +1644,Female,83,166650,99,Healthcare,1,3 +1645,Male,2,52727,60,Artist,1,3 +1646,Male,75,126469,86,Entertainment,1,2 +1647,Male,45,138775,17,Executive,0,7 +1648,Female,49,68901,70,Doctor,7,7 +1649,Female,41,118621,7,Homemaker,0,7 +1650,Male,34,109263,77,Entertainment,0,1 +1651,Male,82,150012,83,Healthcare,3,6 +1652,Male,37,133850,85,Marketing,9,3 +1653,Female,25,81640,95,Entertainment,1,6 +1654,Female,52,133214,1,Marketing,9,7 +1655,Female,23,186723,42,Entertainment,8,5 +1656,Female,96,92100,23,Artist,9,3 +1657,Male,5,64484,10,Homemaker,0,5 +1658,Female,47,69024,3,Healthcare,4,5 +1659,Male,81,171194,55,Artist,0,5 +1660,Female,4,183282,34,Artist,1,1 +1661,Female,8,100099,74,Healthcare,1,3 +1662,Female,35,164435,26,Lawyer,1,3 +1663,Male,67,172407,60,Doctor,9,3 +1664,Female,9,84781,14,Executive,1,3 +1665,Male,82,186130,61,Lawyer,3,7 +1666,Female,2,56782,10,Artist,9,5 +1667,Male,95,137206,4,Artist,0,4 +1668,Female,9,146950,92,Doctor,6,5 +1669,Female,99,174275,4,Healthcare,0,2 +1670,Male,96,97705,41,Homemaker,8,2 +1671,Male,92,188984,69,Lawyer,1,4 +1672,Male,24,119238,55,Artist,1,3 +1673,Male,58,87923,19,Entertainment,0,5 +1674,Male,15,128573,97,Executive,1,1 +1675,Female,13,132374,96,Marketing,0,3 +1676,Female,51,135164,75,Artist,9,3 +1677,Male,9,165194,10,Artist,8,3 +1678,Male,0,126060,92,Doctor,1,3 +1679,Male,67,89366,55,Healthcare,9,3 +1680,Male,58,95172,77,Healthcare,1,5 +1681,Female,55,95975,49,Executive,0,1 +1682,Female,52,165573,59,Healthcare,0,7 +1683,Male,83,180887,59,Entertainment,1,1 +1684,Female,83,142720,86,Executive,0,1 +1685,Female,90,113460,54,Lawyer,1,7 +1686,Male,87,110434,20,Artist,7,3 +1687,Female,67,146016,38,Artist,1,6 +1688,Male,81,101073,67,Executive,0,7 +1689,Female,36,62617,10,Artist,9,3 +1690,Female,51,96028,37,Artist,0,5 +1691,Female,31,186334,37,Artist,0,1 +1692,Female,57,163473,52,Artist,0,1 +1693,Male,83,89819,59,Artist,4,2 +1694,Female,51,69388,33,Engineer,3,4 +1695,Female,15,134748,76,Marketing,1,6 +1696,Female,18,149915,94,Doctor,14,1 +1697,Female,88,104098,48,Healthcare,1,6 +1698,Male,70,80160,10,Artist,8,5 +1699,Male,9,124125,91,Artist,1,2 +1700,Male,6,170226,31,Lawyer,0,3 +1701,Female,8,53306,98,Engineer,0,2 +1702,Male,55,103933,31,Executive,6,3 +1703,Female,69,85401,10,Entertainment,4,6 +1704,Female,18,53812,32,Homemaker,9,5 +1705,Female,13,60771,50,Entertainment,1,7 +1706,Female,69,56398,75,Entertainment,8,3 +1707,Female,69,171118,91,Engineer,9,3 +1708,Male,69,79806,73,Healthcare,0,5 +1709,Female,89,150187,35,Healthcare,0,5 +1710,Female,45,129455,14,Lawyer,0,3 +1711,Female,75,116813,72,Entertainment,10,6 +1712,Female,54,95734,39,Homemaker,8,7 +1713,Male,33,76654,48,Artist,4,1 +1714,Male,67,86289,96,Artist,1,5 +1715,Male,26,176147,19,Healthcare,0,1 +1716,Female,5,142351,97,Entertainment,0,7 +1717,Male,65,186673,51,Doctor,1,1 +1718,Female,94,131521,62,Artist,0,6 +1719,Female,95,50223,15,Entertainment,8,1 +1720,Female,48,74308,23,Entertainment,1,2 +1721,Female,41,155881,22,Lawyer,0,7 +1722,Female,92,168303,16,Executive,9,4 +1723,Male,44,182748,58,Engineer,1,7 +1724,Female,35,52374,73,Artist,1,1 +1725,Female,34,86154,17,Doctor,1,2 +1726,Female,52,163612,51,Executive,0,5 +1727,Female,64,64756,17,Marketing,10,1 +1728,Male,62,185257,58,Artist,1,4 +1729,Male,58,78669,75,Artist,1,7 +1730,Male,15,164520,47,Artist,1,7 +1731,Female,65,92135,49,Homemaker,6,4 +1732,Male,20,103195,68,Doctor,0,7 +1733,Female,21,161663,36,Executive,1,7 +1734,Female,79,57039,34,Artist,2,6 +1735,Female,91,54754,4,Artist,0,2 +1736,Female,92,136531,76,Engineer,1,4 +1737,Male,62,120838,79,Artist,1,4 +1738,Male,94,127927,48,Doctor,0,4 +1739,Male,10,164214,77,Lawyer,0,4 +1740,Female,53,145498,66,Artist,8,4 +1741,Male,7,129215,69,Entertainment,9,6 +1742,Female,92,152429,76,Entertainment,0,2 +1743,Female,12,89252,31,Entertainment,0,5 +1744,Female,31,147447,23,Doctor,6,7 +1745,Female,93,74467,33,Lawyer,5,1 +1746,Female,66,127410,37,Engineer,8,2 +1747,Male,49,91290,44,Healthcare,7,7 +1748,Female,64,92812,97,Artist,5,7 +1749,Female,27,170166,18,Artist,10,4 +1750,Female,33,150823,75,Artist,4,1 +1751,Male,79,95839,30,Executive,7,2 +1752,Female,14,60175,60,Executive,9,7 +1753,Female,22,153024,91,Artist,10,7 +1754,Female,8,116858,67,Artist,5,7 +1755,Female,6,122961,47,Doctor,8,2 +1756,Male,81,141471,45,Artist,8,6 +1757,Male,69,76155,14,Healthcare,8,7 +1758,Male,0,108169,24,Executive,10,3 +1759,Female,61,139520,39,Artist,8,4 +1760,Male,62,182631,52,Artist,5,3 +1761,Female,95,177134,78,Healthcare,10,2 +1762,Female,32,181703,54,Lawyer,6,2 +1763,Female,13,61917,16,Artist,7,2 +1764,Female,1,140648,36,Executive,9,7 +1765,Female,63,176783,40,Doctor,9,4 +1766,Male,75,59741,67,Artist,10,5 +1767,Female,43,156567,89,Engineer,7,6 +1768,Female,29,110309,21,Artist,6,2 +1769,Female,82,111077,55,Artist,9,7 +1770,Female,87,170333,96,Entertainment,4,2 +1771,Male,66,183339,39,Artist,7,4 +1772,Female,99,84167,46,Artist,10,7 +1773,Female,54,184815,50,Lawyer,6,3 +1774,Female,49,122556,74,Artist,9,7 +1775,Female,60,174768,66,Entertainment,9,2 +1776,Male,75,172309,82,Executive,4,1 +1777,Male,77,117920,4,Doctor,10,4 +1778,Male,34,134882,61,Artist,10,3 +1779,Female,63,77259,11,Lawyer,6,6 +1780,Male,38,170019,38,Artist,7,2 +1781,Female,68,176884,94,Engineer,10,2 +1782,Female,80,106709,93,Marketing,10,2 +1783,Female,8,168723,31,Homemaker,7,2 +1784,Female,86,90228,68,Engineer,7,5 +1785,Female,1,89473,85,Lawyer,10,4 +1786,Male,56,131911,42,Healthcare,7,7 +1787,Female,14,140107,63,Marketing,6,6 +1788,Female,4,170403,44,Artist,4,5 +1789,Female,58,51416,46,,7,5 +1790,Female,6,52648,2,Artist,5,2 +1791,Male,17,125471,48,Engineer,7,1 +1792,Female,55,106153,62,Artist,10,1 +1793,Male,14,124785,28,Lawyer,7,2 +1794,Female,22,101474,14,Homemaker,10,2 +1795,Female,8,141359,65,Healthcare,4,5 +1796,Female,70,57596,96,Artist,4,1 +1797,Female,67,78489,81,Artist,7,5 +1798,Male,50,112957,71,Executive,5,3 +1799,Male,72,82377,37,Engineer,9,2 +1800,Male,76,82315,53,Engineer,6,1 +1801,Male,80,93253,51,Artist,4,5 +1802,Male,20,189650,60,Lawyer,8,4 +1803,Female,89,114858,53,Artist,10,1 +1804,Female,18,62151,18,Artist,8,2 +1805,Female,75,135159,12,Engineer,7,6 +1806,Female,5,126263,55,Marketing,6,3 +1807,Female,79,123064,41,Artist,10,4 +1808,Female,31,181773,40,Healthcare,8,1 +1809,Male,92,171794,65,Artist,6,5 +1810,Female,15,156339,27,Homemaker,6,2 +1811,Male,94,65483,84,Marketing,10,7 +1812,Female,35,145654,22,Executive,6,3 +1813,Female,68,91609,43,Entertainment,8,1 +1814,Female,29,53002,73,Lawyer,5,5 +1815,Male,87,115699,61,Healthcare,9,1 +1816,Male,33,149609,44,Executive,9,2 +1817,Female,29,165254,7,Executive,9,7 +1818,Male,43,50418,49,Healthcare,5,1 +1819,Male,43,103531,90,Artist,10,5 +1820,Female,66,184060,98,Artist,10,5 +1821,Male,23,75961,70,Executive,5,7 +1822,Male,87,174163,28,Artist,4,2 +1823,Female,81,105255,54,Executive,8,3 +1824,Male,42,51860,4,Lawyer,4,5 +1825,Female,32,185218,83,Healthcare,4,6 +1826,Male,7,189709,18,Artist,6,6 +1827,Female,91,134665,66,Artist,7,2 +1828,Male,95,91352,9,Doctor,7,3 +1829,Female,10,154623,21,Artist,6,6 +1830,Female,20,113767,88,Artist,6,2 +1831,Male,5,160469,61,Lawyer,8,1 +1832,Female,84,99418,27,Artist,4,2 +1833,Male,54,56215,34,Healthcare,10,4 +1834,Male,12,55639,63,Healthcare,8,1 +1835,Female,90,62504,99,Artist,10,4 +1836,Female,19,151382,38,Lawyer,1,1 +1837,Female,91,156574,96,Marketing,0,7 +1838,Female,90,60998,35,Entertainment,1,7 +1839,Female,60,108509,36,Doctor,0,2 +1840,Female,68,85540,49,Homemaker,9,1 +1841,Female,43,73915,47,Artist,11,2 +1842,Male,52,148901,77,Healthcare,4,4 +1843,Male,81,152602,91,Entertainment,0,1 +1844,Female,54,58999,22,Entertainment,7,3 +1845,Female,43,133740,37,Artist,5,4 +1846,Female,20,113217,41,Lawyer,9,1 +1847,Female,71,60209,46,Artist,1,3 +1848,Female,26,145491,72,Entertainment,9,5 +1849,Female,75,61393,65,Doctor,7,5 +1850,Female,52,133252,55,Artist,1,1 +1851,Female,40,187234,14,Doctor,0,5 +1852,Male,5,145563,60,Entertainment,9,6 +1853,Female,46,165810,38,Healthcare,3,5 +1854,Male,98,125181,30,Artist,0,7 +1855,Female,84,56809,96,Healthcare,1,2 +1856,Male,35,69419,68,Healthcare,9,1 +1857,Female,78,70689,54,Artist,1,4 +1858,Male,64,55011,53,Artist,1,7 +1859,Female,98,87955,79,Engineer,0,2 +1860,Male,66,153660,24,Healthcare,0,5 +1861,Male,32,104494,28,Healthcare,6,4 +1862,Male,22,184443,44,Executive,3,7 +1863,Female,17,150778,35,Entertainment,1,7 +1864,Female,22,88729,81,Engineer,1,1 +1865,Male,9,151902,18,Artist,0,2 +1866,Male,24,51604,4,Artist,0,2 +1867,Female,8,157316,75,Artist,9,3 +1868,Female,52,125508,15,Artist,1,6 +1869,Male,62,186157,54,Healthcare,1,1 +1870,Female,37,169381,45,Entertainment,0,4 +1871,Male,68,110195,47,Homemaker,9,2 +1872,Female,63,81199,80,Entertainment,1,2 +1873,Female,15,58459,67,Executive,0,6 +1874,Female,44,173682,89,Artist,12,6 +1875,Male,18,162983,16,Entertainment,0,6 +1876,Male,30,94480,1,Executive,0,4 +1877,Female,69,168710,80,Doctor,1,4 +1878,Male,16,150193,83,Engineer,2,2 +1879,Female,71,91823,74,Artist,1,4 +1880,Female,24,173091,24,Entertainment,6,2 +1881,Male,57,102692,94,Healthcare,0,2 +1882,Male,97,156812,77,Healthcare,0,6 +1883,Male,68,51468,48,Artist,0,3 +1884,Female,11,114071,37,Healthcare,0,2 +1885,Female,63,155638,81,,1,4 +1886,Male,68,129971,61,Artist,8,3 +1887,Female,69,104160,77,Engineer,4,4 +1888,Female,77,124995,66,Healthcare,0,1 +1889,Female,73,180437,50,Entertainment,1,5 +1890,Female,19,134644,96,Executive,1,2 +1891,Female,1,130089,67,Artist,0,5 +1892,Male,71,149318,27,Artist,4,1 +1893,Male,6,125591,79,Healthcare,8,6 +1894,Female,68,173047,17,Healthcare,0,3 +1895,Female,39,61298,91,Artist,0,5 +1896,Male,75,132993,55,Entertainment,8,2 +1897,Female,65,72313,36,Lawyer,1,5 +1898,Female,30,84885,9,Entertainment,6,5 +1899,Male,46,126228,95,Marketing,8,2 +1900,Male,40,51199,29,Executive,7,7 +1901,Female,40,54631,70,Artist,0,6 +1902,Female,20,131810,16,Healthcare,5,7 +1903,Male,88,138482,59,Marketing,8,4 +1904,Male,85,74081,87,Healthcare,4,3 +1905,Male,94,112426,54,Engineer,9,6 +1906,Female,52,164038,53,,5,4 +1907,Female,68,53552,97,Marketing,7,6 +1908,Male,15,142147,78,Artist,8,4 +1909,Male,4,180324,41,Artist,9,2 +1910,Male,59,149067,78,Doctor,9,7 +1911,Male,32,184408,38,Entertainment,6,7 +1912,Female,8,59116,45,Artist,10,6 +1913,Female,92,174786,94,Executive,4,3 +1914,Male,9,71076,2,Artist,8,1 +1915,Female,41,56837,4,Healthcare,8,4 +1916,Female,18,134752,47,Engineer,6,5 +1917,Female,78,112887,80,Entertainment,9,6 +1918,Female,0,73128,74,Artist,7,5 +1919,Female,11,152765,17,Healthcare,8,1 +1920,Female,36,140637,19,Executive,8,7 +1921,Male,46,137973,50,Healthcare,7,7 +1922,Female,95,126791,34,Artist,6,4 +1923,Female,97,75125,89,Executive,8,3 +1924,Male,2,101718,11,Executive,8,3 +1925,Female,94,69654,30,Homemaker,4,4 +1926,Female,0,105935,46,Doctor,4,5 +1927,Male,3,74607,69,Healthcare,6,1 +1928,Male,77,151985,29,Healthcare,4,7 +1929,Male,70,76892,64,Executive,5,2 +1930,Male,58,87602,25,Executive,5,3 +1931,Male,72,182317,46,Healthcare,8,6 +1932,Male,90,158280,92,Executive,6,7 +1933,Female,23,59905,79,,7,5 +1934,Female,87,128153,42,Artist,7,6 +1935,Male,64,142612,58,Doctor,4,6 +1936,Female,15,180163,9,Doctor,10,7 +1937,Female,14,68009,17,Artist,9,3 +1938,Male,54,166183,65,Artist,7,6 +1939,Male,76,132537,44,Doctor,5,3 +1940,Female,76,146308,49,Artist,5,1 +1941,Female,52,63454,38,Artist,5,5 +1942,Male,96,110519,6,Artist,9,4 +1943,Female,55,66728,50,Healthcare,10,5 +1944,Female,54,170638,64,Artist,4,5 +1945,Male,19,129635,100,Artist,6,4 +1946,Male,29,133587,74,Marketing,5,3 +1947,Male,96,126225,47,Engineer,7,4 +1948,Female,20,148776,21,Artist,8,5 +1949,Female,91,142602,64,Lawyer,7,6 +1950,Male,37,102413,38,Healthcare,5,5 +1951,Male,42,68765,69,Entertainment,4,1 +1952,Male,1,57522,75,Artist,10,5 +1953,Female,58,149237,80,Entertainment,8,6 +1954,Female,42,184844,24,Entertainment,6,1 +1955,Female,48,131656,45,Lawyer,9,6 +1956,Female,11,181026,39,Marketing,6,2 +1957,Male,59,101698,87,Artist,9,3 +1958,Female,45,152560,32,Entertainment,9,2 +1959,Male,73,142502,97,Doctor,5,1 +1960,Female,85,155432,21,Engineer,4,6 +1961,Female,70,108544,49,Entertainment,4,5 +1962,Female,52,106530,46,Engineer,7,2 +1963,Male,70,124359,51,Homemaker,7,7 +1964,Female,88,146970,90,Artist,7,6 +1965,Male,61,182176,96,Artist,9,5 +1966,Female,46,114435,34,Artist,8,3 +1967,Male,65,134213,32,Homemaker,8,4 +1968,Female,84,115580,73,Entertainment,8,5 +1969,Female,76,124034,88,Executive,10,2 +1970,Male,31,67894,42,Doctor,9,1 +1971,Male,55,107099,89,Executive,4,6 +1972,Male,45,140263,88,Entertainment,10,5 +1973,Male,98,104249,12,Engineer,5,2 +1974,Male,40,168821,36,Executive,4,5 +1975,Female,14,153145,59,Healthcare,8,6 +1976,Female,41,128960,43,Executive,9,4 +1977,Male,60,127438,82,Engineer,7,2 +1978,Male,60,125968,100,Lawyer,8,2 +1979,Male,84,104589,85,Artist,10,2 +1980,Male,0,165321,93,Doctor,8,1 +1981,Female,10,86925,76,Artist,7,2 +1982,Female,62,149797,19,Artist,5,6 +1983,Male,33,137094,68,Lawyer,4,1 +1984,Female,52,55395,41,Marketing,10,1 +1985,Female,2,153622,51,Lawyer,6,6 +1986,Male,27,74050,44,Entertainment,8,1 +1987,Female,4,68094,61,Doctor,4,7 +1988,Male,63,59244,80,Artist,7,1 +1989,Female,54,118944,77,Artist,4,4 +1990,Female,47,75293,55,Doctor,6,7 +1991,Female,30,166983,69,Artist,7,3 +1992,Female,97,129444,96,Entertainment,5,6 +1993,Male,94,181183,24,Marketing,9,3 +1994,Female,64,175254,100,Artist,9,5 +1995,Female,19,54121,89,Engineer,6,3 +1996,Female,71,184387,40,Artist,8,7 +1997,Female,91,73158,32,Doctor,7,7 +1998,Male,87,90961,14,Healthcare,9,2 +1999,Male,77,182109,4,Executive,7,2 +2000,Male,90,110610,52,Entertainment,5,2 diff --git a/my_runs/_sources/sacred_train_69457f4c158c0d7ad12c17d05b383cdb.py b/my_runs/_sources/sacred_train_69457f4c158c0d7ad12c17d05b383cdb.py new file mode 100644 index 0000000..b98349a --- /dev/null +++ b/my_runs/_sources/sacred_train_69457f4c158c0d7ad12c17d05b383cdb.py @@ -0,0 +1,95 @@ +#!/usr/bin/python + +import pandas as pd +import numpy as np +import zadanie1 as z +import torch +import torchvision +import torchvision.transforms as transforms +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from sacred import Experiment +from sacred.observers import FileStorageObserver + +class Net(nn.Module): + def __init__(self): + super().__init__() + #self.conv1 = nn.Conv2d(3, 6, 5) + #self.pool = nn.MaxPool2d(2, 2) + #self.conv2 = nn.Conv2d(6, 16, 5) + #self.fc1 = nn.Linear(16 * 5 * 5, 120) + #self.fc2 = nn.Linear(20, 6) + self.fc3 = nn.Linear(6, 6) + + def forward(self, x): + #x = self.pool(F.relu(self.conv1(x))) + #x = self.pool(F.relu(self.conv2(x))) + #x = torch.flatten(x, 1) + #x = F.relu(self.fc1(x)) + #x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +def trainNet(trainloader, criterion, optimizer, epochs=20): + for epoch in range(epochs): + + for i, data in enumerate(trainloader, 0): + inputs, labels = data + + labelsX = torch.Tensor([x for x in labels]) + labels = labelsX.type(torch.LongTensor) + + optimizer.zero_grad() + + outputs = net(inputs) + + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + print('Finished Training') + +ex = Experiment("sacred_scopes", interactive=True) +ex.observers.append(FileStorageObserver('my_runs')) + +@ex.config +def my_config(): + epochs = 10 + learning_rate = 0.001 + +@ex.main +def my_main(epochs, learning_rate): + print(f'{epochs} {learning_rate}') + +if __name__ == '__main__': + + ex.run() + train, dev, test = z.prepareData() + + batch_size = 4 + + trainlist = train.values.tolist() + testlist = test.values.tolist() + + trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist] + trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, + shuffle=True, num_workers=2) + + testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist] + testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, + shuffle=False, num_workers=2) + + classes = ('male', 'female') + + net = Net() + + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) + + #trainNet(trainloader, criterion, optimizer, int(float(epochs))) + + #PATH = './cifar_net.pth' + #torch.save(net.state_dict(), PATH) + diff --git a/my_runs/_sources/sacred_train_9b02b8fd3ddb3f4ddfc9a057f76bf6a2.py b/my_runs/_sources/sacred_train_9b02b8fd3ddb3f4ddfc9a057f76bf6a2.py new file mode 100644 index 0000000..c2422a7 --- /dev/null +++ b/my_runs/_sources/sacred_train_9b02b8fd3ddb3f4ddfc9a057f76bf6a2.py @@ -0,0 +1,103 @@ +#!/usr/bin/python + +import pandas as pd +import numpy as np +import zadanie1 as z +import torch +import torchvision +import torchvision.transforms as transforms +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from sacred import Experiment +from sacred.observers import FileStorageObserver +from sacred.observers import MongoObserver + +class Net(nn.Module): + def __init__(self): + super().__init__() + #self.conv1 = nn.Conv2d(3, 6, 5) + #self.pool = nn.MaxPool2d(2, 2) + #self.conv2 = nn.Conv2d(6, 16, 5) + #self.fc1 = nn.Linear(16 * 5 * 5, 120) + #self.fc2 = nn.Linear(20, 6) + self.fc3 = nn.Linear(6, 6) + + def forward(self, x): + #x = self.pool(F.relu(self.conv1(x))) + #x = self.pool(F.relu(self.conv2(x))) + #x = torch.flatten(x, 1) + #x = F.relu(self.fc1(x)) + #x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +def trainNet(_run, trainloader, criterion, optimizer, net, epochs=20): + for epoch in range(epochs): + + for i, data in enumerate(trainloader, 0): + inputs, labels = data + + labelsX = torch.Tensor([x for x in labels]) + labels = labelsX.type(torch.LongTensor) + + optimizer.zero_grad() + + outputs = net(inputs) + + loss = criterion(outputs, labels) + + _run.log_scalar("training.loss", loss) + loss.backward() + optimizer.step() + + print('Finished Training') + +ex = Experiment("s452627", interactive=True) +ex.observers.append(FileStorageObserver('my_runs')) +#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) + +@ex.config +def my_config(): + epochs = 10 + learning_rate = 0.001 + +@ex.main +def my_main(epochs, learning_rate, _run): + + ex.open_resource("Customers.csv", "r") + + train, dev, test = z.prepareData() + + batch_size = 4 + + trainlist = train.values.tolist() + testlist = test.values.tolist() + + trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist] + trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, + shuffle=True, num_workers=2) + + testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist] + testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, + shuffle=False, num_workers=2) + + classes = ('male', 'female') + + net = Net() + + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9) + + trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs))) + + PATH = './cifar_net.pth' + torch.save(net.state_dict(), PATH) + + ex.add_artifact("cifar_net.pth") + +if __name__ == '__main__': + + ex.run() + diff --git a/my_runs/_sources/sacred_train_b6fddd734c43fc70e41befe914387fe7.py b/my_runs/_sources/sacred_train_b6fddd734c43fc70e41befe914387fe7.py new file mode 100644 index 0000000..e83fb25 --- /dev/null +++ b/my_runs/_sources/sacred_train_b6fddd734c43fc70e41befe914387fe7.py @@ -0,0 +1,96 @@ +#!/usr/bin/python + +import pandas as pd +import numpy as np +import zadanie1 as z +import torch +import torchvision +import torchvision.transforms as transforms +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from sacred import Experiment +from sacred.observers import FileStorageObserver + +class Net(nn.Module): + def __init__(self): + super().__init__() + #self.conv1 = nn.Conv2d(3, 6, 5) + #self.pool = nn.MaxPool2d(2, 2) + #self.conv2 = nn.Conv2d(6, 16, 5) + #self.fc1 = nn.Linear(16 * 5 * 5, 120) + #self.fc2 = nn.Linear(20, 6) + self.fc3 = nn.Linear(6, 6) + + def forward(self, x): + #x = self.pool(F.relu(self.conv1(x))) + #x = self.pool(F.relu(self.conv2(x))) + #x = torch.flatten(x, 1) + #x = F.relu(self.fc1(x)) + #x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +def trainNet(trainloader, criterion, optimizer, epochs=20): + for epoch in range(epochs): + + for i, data in enumerate(trainloader, 0): + inputs, labels = data + + labelsX = torch.Tensor([x for x in labels]) + labels = labelsX.type(torch.LongTensor) + + optimizer.zero_grad() + + outputs = net(inputs) + + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + print('Finished Training') + +ex = Experiment("sacred_scopes", interactive=True) +ex.observers.append(FileStorageObserver('my_runs')) + +@ex.config +def my_config(): + epochs = 10 + learning_rate = 0.001 + +@ex.main +def my_main(epochs, learning_rate): + + train, dev, test = z.prepareData() + + batch_size = 4 + + trainlist = train.values.tolist() + testlist = test.values.tolist() + + trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist] + trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, + shuffle=True, num_workers=2) + + testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist] + testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, + shuffle=False, num_workers=2) + + classes = ('male', 'female') + + net = Net() + + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9) + + trainNet(trainloader, criterion, optimizer, int(float(epochs))) + + PATH = './cifar_net.pth' + torch.save(net.state_dict(), PATH) + ex.add_artifact("cifar_net.pth") + +if __name__ == '__main__': + + ex.run() + diff --git a/my_runs/_sources/sacred_train_f2e895e7d4eaf1570420fc943ad98f4b.py b/my_runs/_sources/sacred_train_f2e895e7d4eaf1570420fc943ad98f4b.py new file mode 100644 index 0000000..48dd38c --- /dev/null +++ b/my_runs/_sources/sacred_train_f2e895e7d4eaf1570420fc943ad98f4b.py @@ -0,0 +1,96 @@ +#!/usr/bin/python + +import pandas as pd +import numpy as np +import zadanie1 as z +import torch +import torchvision +import torchvision.transforms as transforms +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from sacred import Experiment +from sacred.observers import FileStorageObserver + +class Net(nn.Module): + def __init__(self): + super().__init__() + #self.conv1 = nn.Conv2d(3, 6, 5) + #self.pool = nn.MaxPool2d(2, 2) + #self.conv2 = nn.Conv2d(6, 16, 5) + #self.fc1 = nn.Linear(16 * 5 * 5, 120) + #self.fc2 = nn.Linear(20, 6) + self.fc3 = nn.Linear(6, 6) + + def forward(self, x): + #x = self.pool(F.relu(self.conv1(x))) + #x = self.pool(F.relu(self.conv2(x))) + #x = torch.flatten(x, 1) + #x = F.relu(self.fc1(x)) + #x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +def trainNet(trainloader, criterion, optimizer, net, epochs=20): + for epoch in range(epochs): + + for i, data in enumerate(trainloader, 0): + inputs, labels = data + + labelsX = torch.Tensor([x for x in labels]) + labels = labelsX.type(torch.LongTensor) + + optimizer.zero_grad() + + outputs = net(inputs) + + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + print('Finished Training') + +ex = Experiment("sacred_scopes", interactive=True) +ex.observers.append(FileStorageObserver('my_runs')) + +@ex.config +def my_config(): + epochs = 10 + learning_rate = 0.001 + +@ex.main +def my_main(epochs, learning_rate): + + train, dev, test = z.prepareData() + + batch_size = 4 + + trainlist = train.values.tolist() + testlist = test.values.tolist() + + trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist] + trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, + shuffle=True, num_workers=2) + + testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist] + testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, + shuffle=False, num_workers=2) + + classes = ('male', 'female') + + net = Net() + + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9) + + trainNet(trainloader, criterion, optimizer, net, int(float(epochs))) + + PATH = './cifar_net.pth' + torch.save(net.state_dict(), PATH) + ex.add_artifact("cifar_net.pth") + +if __name__ == '__main__': + + ex.run() + diff --git a/my_runs/_sources/zadanie1_214ad86c108ac00197ed071c54ee3658.py b/my_runs/_sources/zadanie1_214ad86c108ac00197ed071c54ee3658.py new file mode 100644 index 0000000..d42dacb --- /dev/null +++ b/my_runs/_sources/zadanie1_214ad86c108ac00197ed071c54ee3658.py @@ -0,0 +1,40 @@ +#!/usr/bin/python + +import pandas as pd +import numpy as np + + +def prepareData(): + data = pd.read_csv("Customers.csv") + #print(data[:10]) + + dataF = data + + mapping = {'NaN' : 0, 'Healthcare' : 1, 'Engineer' : 2, 'Lawyer' : 3, 'Entertainment' : 4, 'Artist' : 5, 'Executive' : 6, + 'Doctor' : 7, 'Homemaker' : 8, 'Marketing' : 9} + + mapping2 = {'Male' : 0, 'Female' : 1} + + dataF = dataF.replace({'Profession': mapping}) + dataF = dataF.replace({'Gender': mapping2}) + + dataF = dataF.drop(columns=['CustomerID']) + + dataF['Profession'] = dataF['Profession'].fillna(0) + + normalized_dataF = (dataF - dataF.min())/(dataF.max() - dataF.min()) + + #print(normalized_dataF[:10]) + + train_data = normalized_dataF[0:1600] + dev_data = normalized_dataF[1600:1800] + test_data = normalized_dataF[1800:] + + #print(f"Wielkość zbioru Customers: {len(data)} elementów") + #print(f"Wielkość zbioru trenującego: {len(train_data)} elementów") + #print(f"Wielkość zbioru walidującego: {len(dev_data)} elementów") + #print(f"Wielkość zbioru testującego: {len(test_data)} elementów") + + #print(f" \nDane i wartości na temat zbioru: \n \n {normalized_dataF.describe()}") + + return train_data, dev_data, test_data diff --git a/sacred_train.py b/sacred_train.py new file mode 100644 index 0000000..0623a99 --- /dev/null +++ b/sacred_train.py @@ -0,0 +1,103 @@ +#!/usr/bin/python + +import pandas as pd +import numpy as np +import zadanie1 as z +import torch +import torchvision +import torchvision.transforms as transforms +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from sacred import Experiment +from sacred.observers import FileStorageObserver +from sacred.observers import MongoObserver + +class Net(nn.Module): + def __init__(self): + super().__init__() + #self.conv1 = nn.Conv2d(3, 6, 5) + #self.pool = nn.MaxPool2d(2, 2) + #self.conv2 = nn.Conv2d(6, 16, 5) + #self.fc1 = nn.Linear(16 * 5 * 5, 120) + #self.fc2 = nn.Linear(20, 6) + self.fc3 = nn.Linear(6, 6) + + def forward(self, x): + #x = self.pool(F.relu(self.conv1(x))) + #x = self.pool(F.relu(self.conv2(x))) + #x = torch.flatten(x, 1) + #x = F.relu(self.fc1(x)) + #x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +def trainNet(_run, trainloader, criterion, optimizer, net, epochs=20): + for epoch in range(epochs): + + for i, data in enumerate(trainloader, 0): + inputs, labels = data + + labelsX = torch.Tensor([x for x in labels]) + labels = labelsX.type(torch.LongTensor) + + optimizer.zero_grad() + + outputs = net(inputs) + + loss = criterion(outputs, labels) + + _run.log_scalar("training.loss", loss) + loss.backward() + optimizer.step() + + print('Finished Training') + +ex = Experiment("s452627", interactive=True) +ex.observers.append(FileStorageObserver('my_runs')) +ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) + +@ex.config +def my_config(): + epochs = 10 + learning_rate = 0.001 + +@ex.main +def my_main(epochs, learning_rate, _run): + + ex.open_resource("Customers.csv", "r") + + train, dev, test = z.prepareData() + + batch_size = 4 + + trainlist = train.values.tolist() + testlist = test.values.tolist() + + trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist] + trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, + shuffle=True, num_workers=2) + + testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist] + testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, + shuffle=False, num_workers=2) + + classes = ('male', 'female') + + net = Net() + + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9) + + trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs))) + + PATH = './cifar_net.pth' + torch.save(net.state_dict(), PATH) + + ex.add_artifact("cifar_net.pth") + +if __name__ == '__main__': + + ex.run() +