Главная Обзор Помощь
Вход
fyrr
/
ConvGeNBenchmarkResults
1
0
Ответвить 0
Файлы Задачи 0 Запросы на слияние 0 Вики
Дерево: 5889e260a5
Ветки Метки
ConvGeNBench...
/
data_result
/
ConvGeN-majority-5
/
folding_car_good.log

folding_car_good.log 146 KB
История Исходник

1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798991001011021031041051061071081091101111121131141151161171181191201211221231241251261271281291301311321331341351361371381391401411421431441451461471481491501511521531541551561571581591601611621631641651661671681691701711721731741751761771781791801811821831841851861871881891901911921931941951961971981992002012022032042052062072082092102112122132142152162172182192202212222232242252262272282292302312322332342352362372382392402412422432442452462472482492502512522532542552562572582592602612622632642652662672682692702712722732742752762772782792802812822832842852862872882892902912922932942952962972982993003013023033043053063073083093103113123133143153163173183193203213223233243253263273283293303313323333343353363373383393403413423433443453463473483493503513523533543553563573583593603613623633643653663673683693703713723733743753763773783793803813823833843853863873883893903913923933943953963973983994004014024034044054064074084094104114124134144154164174184194204214224234244254264274284294304314324334344354364374384394404414424434444454464474484494504514524534544554564574584594604614624634644654664674684694704714724734744754764774784794804814824834844854864874884894904914924934944954964974984995005015025035045055065075085095105115125135145155165175185195205215225235245255265275285295305315325335345355365375385395405415425435445455465475485495505515525535545555565575585595605615625635645655665675685695705715725735745755765775785795805815825835845855865875885895905915925935945955965975985996006016026036046056066076086096106116126136146156166176186196206216226236246256266276286296306316326336346356366376386396406416426436446456466476486496506516526536546556566576586596606616626636646656666676686696706716726736746756766776786796806816826836846856866876886896906916926936946956966976986997007017027037047057067077087097107117127137147157167177187197207217227237247257267277287297307317327337347357367377387397407417427437447457467477487497507517527537547557567577587597607617627637647657667677687697707717727737747757767777787797807817827837847857867877887897907917927937947957967977987998008018028038048058068078088098108118128138148158168178188198208218228238248258268278288298308318328338348358368378388398408418428438448458468478488498508518528538548558568578588598608618628638648658668678688698708718728738748758768778788798808818828838848858868878888898908918928938948958968978988999009019029039049059069079089099109119129139149159169179189199209219229239249259269279289299309319329339349359369379389399409419429439449459469479489499509519529539549559569579589599609619629639649659669679689699709719729739749759769779789799809819829839849859869879889899909919929939949959969979989991000100110021003100410051006100710081009101010111012101310141015101610171018101910201021102210231024102510261027102810291030103110321033103410351036103710381039104010411042104310441045104610471048104910501051105210531054105510561057105810591060106110621063106410651066106710681069107010711072107310741075107610771078107910801081108210831084108510861087108810891090109110921093109410951096109710981099110011011102110311041105110611071108110911101111111211131114111511161117111811191120112111221123112411251126112711281129113011311132113311341135113611371138113911401141114211431144114511461147114811491150115111521153115411551156115711581159116011611162116311641165116611671168116911701171117211731174117511761177117811791180118111821183118411851186118711881189119011911192119311941195119611971198119912001201120212031204120512061207120812091210121112121213121412151216121712181219122012211222122312241225122612271228122912301231123212331234123512361237123812391240124112421243124412451246124712481249125012511252125312541255125612571258125912601261126212631264126512661267126812691270127112721273127412751276127712781279128012811282128312841285128612871288128912901291129212931294129512961297129812991300130113021303130413051306130713081309131013111312131313141315131613171318131913201321132213231324132513261327132813291330133113321333133413351336133713381339134013411342134313441345134613471348134913501351135213531354135513561357135813591360136113621363136413651366136713681369137013711372137313741375137613771378137913801381138213831384138513861387138813891390139113921393139413951396139713981399140014011402140314041405140614071408140914101411141214131414141514161417141814191420142114221423142414251426142714281429143014311432143314341435143614371438143914401441144214431444144514461447144814491450145114521453145414551456145714581459146014611462146314641465146614671468146914701471147214731474147514761477147814791480148114821483148414851486148714881489149014911492149314941495149614971498149915001501150215031504150515061507150815091510151115121513151415151516151715181519152015211522152315241525152615271528152915301531153215331534153515361537153815391540154115421543154415451546154715481549155015511552155315541555155615571558155915601561156215631564156515661567156815691570 
  1. 

  2. 

  3. ///////////////////////////////////////////
    4. 

  4. // Running convGAN-majority-5 on folding_car_good
    5. 

  5. ///////////////////////////////////////////
    6. 

  6. 

  7. Load 'data_input/folding_car_good'
    8. 

  8. from pickle file
    9. 

  9. Data loaded.
    10. 

  10. -> Shuffling data
    11. 

  11. ### Start exercise for synthetic point generator
    12. 

  12. 

  13. ====== Step 1/5 =======
    14. 

  14. -> Shuffling data
    15. 

  15. -> Spliting data to slices
    16. 

  16. 

  17. ------ Step 1/5: Slice 1/5 -------
    18. 

  18. -> Reset the GAN
    19. 

  19. -> Train generator for synthetic samples
    20. 

  20. -> create 1272 synthetic samples
    21. 

  21. -> retrain GAN for predict
    22. 

  22. Epoch 1/10
    23. 

  23. 
  1/133 [..............................] - ETA: 21s - loss: 0.0111
 36/133 [=======>......................] - ETA: 0s - loss: 0.0348 
 76/133 [================>.............] - ETA: 0s - loss: 0.0314
110/133 [=======================>......] - ETA: 0s - loss: 0.0381
133/133 [==============================] - 0s 1ms/step - loss: 0.0384
    24. 

  24. Epoch 2/10
    25. 

  25. 
  1/133 [..............................] - ETA: 0s - loss: 0.0811
 40/133 [========>.....................] - ETA: 0s - loss: 0.0478
 79/133 [================>.............] - ETA: 0s - loss: 0.0362
120/133 [==========================>...] - ETA: 0s - loss: 0.0368
133/133 [==============================] - 0s 1ms/step - loss: 0.0374
    26. 

  26. Epoch 3/10
    27. 

  27. 
  1/133 [..............................] - ETA: 0s - loss: 0.0107
 47/133 [=========>....................] - ETA: 0s - loss: 0.0451
 91/133 [===================>..........] - ETA: 0s - loss: 0.0383
133/133 [==============================] - 0s 1ms/step - loss: 0.0333
    28. 

  28. Epoch 4/10
    29. 

  29. 
  1/133 [..............................] - ETA: 0s - loss: 0.0092
 43/133 [========>.....................] - ETA: 0s - loss: 0.0364
 88/133 [==================>...........] - ETA: 0s - loss: 0.0315
132/133 [============================>.] - ETA: 0s - loss: 0.0329
133/133 [==============================] - 0s 1ms/step - loss: 0.0332
    30. 

  30. Epoch 5/10
    31. 

  31. 
  1/133 [..............................] - ETA: 0s - loss: 0.0152
 46/133 [=========>....................] - ETA: 0s - loss: 0.0404
 92/133 [===================>..........] - ETA: 0s - loss: 0.0296
133/133 [==============================] - 0s 1ms/step - loss: 0.0311
    32. 

  32. Epoch 6/10
    33. 

  33. 
  1/133 [..............................] - ETA: 0s - loss: 0.0134
 44/133 [========>.....................] - ETA: 0s - loss: 0.0216
 90/133 [===================>..........] - ETA: 0s - loss: 0.0296
133/133 [==============================] - 0s 1ms/step - loss: 0.0304
    34. 

  34. Epoch 7/10
    35. 

  35. 
  1/133 [..............................] - ETA: 0s - loss: 0.0073
 48/133 [=========>....................] - ETA: 0s - loss: 0.0187
 94/133 [====================>.........] - ETA: 0s - loss: 0.0280
133/133 [==============================] - 0s 1ms/step - loss: 0.0291
    36. 

  36. Epoch 8/10
    37. 

  37. 
  1/133 [..............................] - ETA: 0s - loss: 0.0140
 48/133 [=========>....................] - ETA: 0s - loss: 0.0215
 93/133 [===================>..........] - ETA: 0s - loss: 0.0277
133/133 [==============================] - 0s 1ms/step - loss: 0.0277
    38. 

  38. Epoch 9/10
    39. 

  39. 
  1/133 [..............................] - ETA: 0s - loss: 0.0770
 38/133 [=======>......................] - ETA: 0s - loss: 0.0220
 77/133 [================>.............] - ETA: 0s - loss: 0.0244
115/133 [========================>.....] - ETA: 0s - loss: 0.0267
133/133 [==============================] - 0s 1ms/step - loss: 0.0266
    40. 

  40. Epoch 10/10
    41. 

  41. 
  1/133 [..............................] - ETA: 0s - loss: 0.0076
 46/133 [=========>....................] - ETA: 0s - loss: 0.0182
 90/133 [===================>..........] - ETA: 0s - loss: 0.0244
133/133 [==============================] - 0s 1ms/step - loss: 0.0254
    42. 

  42. -> test with GAN.predict
    43. 

  43. GAN tn, fp: 328, 4
    44. 

  44. GAN fn, tp: 0, 14
    45. 

  45. GAN f1 score: 0.875
    46. 

  46. GAN cohens kappa score: 0.869
    47. 

  47. 

  48. -> test with 'LR'
    49. 

  49. LR tn, fp: 176, 156
    50. 

  50. LR fn, tp: 5, 9
    51. 

  51. LR f1 score: 0.101
    52. 

  52. LR cohens kappa score: 0.028
    53. 

  53. LR average precision score: 0.059
    54. 

  54. 

  55. -> test with 'RF'
    56. 

  56. RF tn, fp: 332, 0
    57. 

  57. RF fn, tp: 4, 10
    58. 

  58. RF f1 score: 0.833
    59. 

  59. RF cohens kappa score: 0.828
    60. 

  60. 

  61. -> test with 'GB'
    62. 

  62. GB tn, fp: 330, 2
    63. 

  63. GB fn, tp: 4, 10
    64. 

  64. GB f1 score: 0.769
    65. 

  65. GB cohens kappa score: 0.760
    66. 

  66. 

  67. -> test with 'KNN'
    68. 

  68. KNN tn, fp: 311, 21
    69. 

  69. KNN fn, tp: 1, 13
    70. 

  70. KNN f1 score: 0.542
    71. 

  71. KNN cohens kappa score: 0.514
    72. 

  72. 

  73. 

  74. ------ Step 1/5: Slice 2/5 -------
    75. 

  75. -> Reset the GAN
    76. 

  76. -> Train generator for synthetic samples
    77. 

  77. -> create 1272 synthetic samples
    78. 

  78. -> retrain GAN for predict
    79. 

  79. Epoch 1/10
    80. 

  80. 
  1/133 [..............................] - ETA: 19s - loss: 0.0552
 47/133 [=========>....................] - ETA: 0s - loss: 0.0453 
 92/133 [===================>..........] - ETA: 0s - loss: 0.0459
133/133 [==============================] - 0s 1ms/step - loss: 0.0424
    81. 

  81. Epoch 2/10
    82. 

  82. 
  1/133 [..............................] - ETA: 0s - loss: 0.2267
 47/133 [=========>....................] - ETA: 0s - loss: 0.0448
 93/133 [===================>..........] - ETA: 0s - loss: 0.0414
133/133 [==============================] - 0s 1ms/step - loss: 0.0395
    83. 

  83. Epoch 3/10
    84. 

  84. 
  1/133 [..............................] - ETA: 0s - loss: 0.0112
 47/133 [=========>....................] - ETA: 0s - loss: 0.0431
 92/133 [===================>..........] - ETA: 0s - loss: 0.0417
133/133 [==============================] - 0s 1ms/step - loss: 0.0399
    85. 

  85. Epoch 4/10
    86. 

  86. 
  1/133 [..............................] - ETA: 0s - loss: 0.0260
 47/133 [=========>....................] - ETA: 0s - loss: 0.0362
 93/133 [===================>..........] - ETA: 0s - loss: 0.0386
133/133 [==============================] - 0s 1ms/step - loss: 0.0357
    87. 

  87. Epoch 5/10
    88. 

  88. 
  1/133 [..............................] - ETA: 0s - loss: 0.0906
 47/133 [=========>....................] - ETA: 0s - loss: 0.0276
 93/133 [===================>..........] - ETA: 0s - loss: 0.0332
133/133 [==============================] - 0s 1ms/step - loss: 0.0349
    89. 

  89. Epoch 6/10
    90. 

  90. 
  1/133 [..............................] - ETA: 0s - loss: 0.0427
 48/133 [=========>....................] - ETA: 0s - loss: 0.0278
 94/133 [====================>.........] - ETA: 0s - loss: 0.0314
133/133 [==============================] - 0s 1ms/step - loss: 0.0322
    91. 

  91. Epoch 7/10
    92. 

  92. 
  1/133 [..............................] - ETA: 0s - loss: 0.0459
 45/133 [=========>....................] - ETA: 0s - loss: 0.0203
 87/133 [==================>...........] - ETA: 0s - loss: 0.0251
133/133 [==============================] - ETA: 0s - loss: 0.0305
133/133 [==============================] - 0s 1ms/step - loss: 0.0305
    93. 

  93. Epoch 8/10
    94. 

  94. 
  1/133 [..............................] - ETA: 0s - loss: 0.0217
 48/133 [=========>....................] - ETA: 0s - loss: 0.0263
 93/133 [===================>..........] - ETA: 0s - loss: 0.0295
133/133 [==============================] - 0s 1ms/step - loss: 0.0304
    95. 

  95. Epoch 9/10
    96. 

  96. 
  1/133 [..............................] - ETA: 0s - loss: 0.0150
 42/133 [========>.....................] - ETA: 0s - loss: 0.0348
 82/133 [=================>............] - ETA: 0s - loss: 0.0289
121/133 [==========================>...] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 1ms/step - loss: 0.0283
    97. 

  97. Epoch 10/10
    98. 

  98. 
  1/133 [..............................] - ETA: 0s - loss: 0.0081
 47/133 [=========>....................] - ETA: 0s - loss: 0.0312
 94/133 [====================>.........] - ETA: 0s - loss: 0.0318
133/133 [==============================] - 0s 1ms/step - loss: 0.0270
    99. 

  99. -> test with GAN.predict
    100. 

  100. GAN tn, fp: 326, 6
    101. 

  101. GAN fn, tp: 2, 12
    102. 

  102. GAN f1 score: 0.750
    103. 

  103. GAN cohens kappa score: 0.738
    104. 

  104. 

  105. -> test with 'LR'
    106. 

  106. LR tn, fp: 180, 152
    107. 

  107. LR fn, tp: 4, 10
    108. 

  108. LR f1 score: 0.114
    109. 

  109. LR cohens kappa score: 0.042
    110. 

  110. LR average precision score: 0.083
    111. 

  111. 

  112. -> test with 'RF'
    113. 

  113. RF tn, fp: 332, 0
    114. 

  114. RF fn, tp: 5, 9
    115. 

  115. RF f1 score: 0.783
    116. 

  116. RF cohens kappa score: 0.775
    117. 

  117. 

  118. -> test with 'GB'
    119. 

  119. GB tn, fp: 330, 2
    120. 

  120. GB fn, tp: 4, 10
    121. 

  121. GB f1 score: 0.769
    122. 

  122. GB cohens kappa score: 0.760
    123. 

  123. 

  124. -> test with 'KNN'
    125. 

  125. KNN tn, fp: 303, 29
    126. 

  126. KNN fn, tp: 1, 13
    127. 

  127. KNN f1 score: 0.464
    128. 

  128. KNN cohens kappa score: 0.430
    129. 

  129. 

  130. 

  131. ------ Step 1/5: Slice 3/5 -------
    132. 

  132. -> Reset the GAN
    133. 

  133. -> Train generator for synthetic samples
    134. 

  134. -> create 1272 synthetic samples
    135. 

  135. -> retrain GAN for predict
    136. 

  136. Epoch 1/10
    137. 

  137. 
  1/133 [..............................] - ETA: 19s - loss: 0.1097
 45/133 [=========>....................] - ETA: 0s - loss: 0.0386 
 89/133 [===================>..........] - ETA: 0s - loss: 0.0387
133/133 [==============================] - 0s 1ms/step - loss: 0.0388
    138. 

  138. Epoch 2/10
    139. 

  139. 
  1/133 [..............................] - ETA: 0s - loss: 0.0175
 11/133 [=>............................] - ETA: 1s - loss: 0.0619
 57/133 [===========>..................] - ETA: 0s - loss: 0.0456
105/133 [======================>.......] - ETA: 0s - loss: 0.0384
133/133 [==============================] - 0s 2ms/step - loss: 0.0364
    140. 

  140. Epoch 3/10
    141. 

  141. 
  1/133 [..............................] - ETA: 0s - loss: 0.0149
 49/133 [==========>...................] - ETA: 0s - loss: 0.0359
 97/133 [====================>.........] - ETA: 0s - loss: 0.0370
133/133 [==============================] - 0s 1ms/step - loss: 0.0351
    142. 

  142. Epoch 4/10
    143. 

  143. 
  1/133 [..............................] - ETA: 0s - loss: 0.0099
 49/133 [==========>...................] - ETA: 0s - loss: 0.0287
 97/133 [====================>.........] - ETA: 0s - loss: 0.0299
133/133 [==============================] - 0s 1ms/step - loss: 0.0328
    144. 

  144. Epoch 5/10
    145. 

  145. 
  1/133 [..............................] - ETA: 0s - loss: 0.0285
 49/133 [==========>...................] - ETA: 0s - loss: 0.0362
 96/133 [====================>.........] - ETA: 0s - loss: 0.0332
133/133 [==============================] - 0s 1ms/step - loss: 0.0335
    146. 

  146. Epoch 6/10
    147. 

  147. 
  1/133 [..............................] - ETA: 0s - loss: 0.0514
 49/133 [==========>...................] - ETA: 0s - loss: 0.0317
 96/133 [====================>.........] - ETA: 0s - loss: 0.0332
133/133 [==============================] - 0s 1ms/step - loss: 0.0304
    148. 

  148. Epoch 7/10
    149. 

  149. 
  1/133 [..............................] - ETA: 0s - loss: 0.0115
 47/133 [=========>....................] - ETA: 0s - loss: 0.0196
 93/133 [===================>..........] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 1ms/step - loss: 0.0293
    150. 

  150. Epoch 8/10
    151. 

  151. 
  1/133 [..............................] - ETA: 0s - loss: 0.0257
 49/133 [==========>...................] - ETA: 0s - loss: 0.0387
 97/133 [====================>.........] - ETA: 0s - loss: 0.0324
133/133 [==============================] - 0s 1ms/step - loss: 0.0282
    152. 

  152. Epoch 9/10
    153. 

  153. 
  1/133 [..............................] - ETA: 0s - loss: 0.0063
 49/133 [==========>...................] - ETA: 0s - loss: 0.0226
 97/133 [====================>.........] - ETA: 0s - loss: 0.0227
133/133 [==============================] - 0s 1ms/step - loss: 0.0271
    154. 

  154. Epoch 10/10
    155. 

  155. 
  1/133 [..............................] - ETA: 0s - loss: 0.0092
 48/133 [=========>....................] - ETA: 0s - loss: 0.0270
 95/133 [====================>.........] - ETA: 0s - loss: 0.0297
133/133 [==============================] - 0s 1ms/step - loss: 0.0253
    156. 

  156. -> test with GAN.predict
    157. 

  157. GAN tn, fp: 327, 5
    158. 

  158. GAN fn, tp: 2, 12
    159. 

  159. GAN f1 score: 0.774
    160. 

  160. GAN cohens kappa score: 0.764
    161. 

  161. 

  162. -> test with 'LR'
    163. 

  163. LR tn, fp: 175, 157
    164. 

  164. LR fn, tp: 5, 9
    165. 

  165. LR f1 score: 0.100
    166. 

  166. LR cohens kappa score: 0.027
    167. 

  167. LR average precision score: 0.058
    168. 

  168. 

  169. -> test with 'RF'
    170. 

  170. RF tn, fp: 330, 2
    171. 

  171. RF fn, tp: 6, 8
    172. 

  172. RF f1 score: 0.667
    173. 

  173. RF cohens kappa score: 0.655
    174. 

  174. 

  175. -> test with 'GB'
    176. 

  176. GB tn, fp: 331, 1
    177. 

  177. GB fn, tp: 6, 8
    178. 

  178. GB f1 score: 0.696
    179. 

  179. GB cohens kappa score: 0.686
    180. 

  180. 

  181. -> test with 'KNN'
    182. 

  182. KNN tn, fp: 303, 29
    183. 

  183. KNN fn, tp: 2, 12
    184. 

  184. KNN f1 score: 0.436
    185. 

  185. KNN cohens kappa score: 0.400
    186. 

  186. 

  187. 

  188. ------ Step 1/5: Slice 4/5 -------
    189. 

  189. -> Reset the GAN
    190. 

  190. -> Train generator for synthetic samples
    191. 

  191. -> create 1272 synthetic samples
    192. 

  192. -> retrain GAN for predict
    193. 

  193. Epoch 1/10
    194. 

  194. 
  1/133 [..............................] - ETA: 20s - loss: 0.1354
 46/133 [=========>....................] - ETA: 0s - loss: 0.0404 
 91/133 [===================>..........] - ETA: 0s - loss: 0.0378
133/133 [==============================] - 0s 1ms/step - loss: 0.0349
    195. 

  195. Epoch 2/10
    196. 

  196. 
  1/133 [..............................] - ETA: 0s - loss: 0.0033
 47/133 [=========>....................] - ETA: 0s - loss: 0.0266
 92/133 [===================>..........] - ETA: 0s - loss: 0.0300
133/133 [==============================] - 0s 1ms/step - loss: 0.0324
    197. 

  197. Epoch 3/10
    198. 

  198. 
  1/133 [..............................] - ETA: 0s - loss: 0.0292
 46/133 [=========>....................] - ETA: 0s - loss: 0.0297
 91/133 [===================>..........] - ETA: 0s - loss: 0.0343
133/133 [==============================] - 0s 1ms/step - loss: 0.0312
    199. 

  199. Epoch 4/10
    200. 

  200. 
  1/133 [..............................] - ETA: 0s - loss: 0.2217
 38/133 [=======>......................] - ETA: 0s - loss: 0.0384
 81/133 [=================>............] - ETA: 0s - loss: 0.0331
124/133 [==========================>...] - ETA: 0s - loss: 0.0294
133/133 [==============================] - 0s 1ms/step - loss: 0.0291
    201. 

  201. Epoch 5/10
    202. 

  202. 
  1/133 [..............................] - ETA: 0s - loss: 0.0134
 43/133 [========>.....................] - ETA: 0s - loss: 0.0243
 87/133 [==================>...........] - ETA: 0s - loss: 0.0267
130/133 [============================>.] - ETA: 0s - loss: 0.0285
133/133 [==============================] - 0s 1ms/step - loss: 0.0284
    203. 

  203. Epoch 6/10
    204. 

  204. 
  1/133 [..............................] - ETA: 0s - loss: 0.0069
 45/133 [=========>....................] - ETA: 0s - loss: 0.0304
 88/133 [==================>...........] - ETA: 0s - loss: 0.0296
133/133 [==============================] - ETA: 0s - loss: 0.0272
133/133 [==============================] - 0s 1ms/step - loss: 0.0272
    205. 

  205. Epoch 7/10
    206. 

  206. 
  1/133 [..............................] - ETA: 0s - loss: 0.0063
 45/133 [=========>....................] - ETA: 0s - loss: 0.0268
 89/133 [===================>..........] - ETA: 0s - loss: 0.0268
133/133 [==============================] - ETA: 0s - loss: 0.0256
133/133 [==============================] - 0s 1ms/step - loss: 0.0256
    207. 

  207. Epoch 8/10
    208. 

  208. 
  1/133 [..............................] - ETA: 0s - loss: 0.0121
 48/133 [=========>....................] - ETA: 0s - loss: 0.0171
 95/133 [====================>.........] - ETA: 0s - loss: 0.0215
133/133 [==============================] - 0s 1ms/step - loss: 0.0250
    209. 

  209. Epoch 9/10
    210. 

  210. 
  1/133 [..............................] - ETA: 0s - loss: 0.0335
 48/133 [=========>....................] - ETA: 0s - loss: 0.0277
 96/133 [====================>.........] - ETA: 0s - loss: 0.0245
133/133 [==============================] - 0s 1ms/step - loss: 0.0233
    211. 

  211. Epoch 10/10
    212. 

  212. 
  1/133 [..............................] - ETA: 0s - loss: 0.0274
 49/133 [==========>...................] - ETA: 0s - loss: 0.0295
 97/133 [====================>.........] - ETA: 0s - loss: 0.0227
133/133 [==============================] - 0s 1ms/step - loss: 0.0237
    213. 

  213. -> test with GAN.predict
    214. 

  214. GAN tn, fp: 323, 9
    215. 

  215. GAN fn, tp: 3, 11
    216. 

  216. GAN f1 score: 0.647
    217. 

  217. GAN cohens kappa score: 0.629
    218. 

  218. 

  219. -> test with 'LR'
    220. 

  220. LR tn, fp: 181, 151
    221. 

  221. LR fn, tp: 3, 11
    222. 

  222. LR f1 score: 0.125
    223. 

  223. LR cohens kappa score: 0.055
    224. 

  224. LR average precision score: 0.076
    225. 

  225. 

  226. -> test with 'RF'
    227. 

  227. RF tn, fp: 331, 1
    228. 

  228. RF fn, tp: 10, 4
    229. 

  229. RF f1 score: 0.421
    230. 

  230. RF cohens kappa score: 0.408
    231. 

  231. 

  232. -> test with 'GB'
    233. 

  233. GB tn, fp: 330, 2
    234. 

  234. GB fn, tp: 7, 7
    235. 

  235. GB f1 score: 0.609
    236. 

  236. GB cohens kappa score: 0.596
    237. 

  237. 

  238. -> test with 'KNN'
    239. 

  239. KNN tn, fp: 317, 15
    240. 

  240. KNN fn, tp: 0, 14
    241. 

  241. KNN f1 score: 0.651
    242. 

  242. KNN cohens kappa score: 0.631
    243. 

  243. 

  244. 

  245. ------ Step 1/5: Slice 5/5 -------
    246. 

  246. -> Reset the GAN
    247. 

  247. -> Train generator for synthetic samples
    248. 

  248. -> create 1272 synthetic samples
    249. 

  249. -> retrain GAN for predict
    250. 

  250. Epoch 1/10
    251. 

  251. 
  1/133 [..............................] - ETA: 20s - loss: 0.0226
 46/133 [=========>....................] - ETA: 0s - loss: 0.0355 
 93/133 [===================>..........] - ETA: 0s - loss: 0.0379
133/133 [==============================] - 0s 1ms/step - loss: 0.0376
    252. 

  252. Epoch 2/10
    253. 

  253. 
  1/133 [..............................] - ETA: 0s - loss: 0.0134
 48/133 [=========>....................] - ETA: 0s - loss: 0.0341
 95/133 [====================>.........] - ETA: 0s - loss: 0.0361
133/133 [==============================] - 0s 1ms/step - loss: 0.0345
    254. 

  254. Epoch 3/10
    255. 

  255. 
  1/133 [..............................] - ETA: 0s - loss: 0.0801
 48/133 [=========>....................] - ETA: 0s - loss: 0.0415
 95/133 [====================>.........] - ETA: 0s - loss: 0.0338
133/133 [==============================] - 0s 1ms/step - loss: 0.0332
    256. 

  256. Epoch 4/10
    257. 

  257. 
  1/133 [..............................] - ETA: 0s - loss: 0.0719
 49/133 [==========>...................] - ETA: 0s - loss: 0.0351
 96/133 [====================>.........] - ETA: 0s - loss: 0.0341
133/133 [==============================] - 0s 1ms/step - loss: 0.0313
    258. 

  258. Epoch 5/10
    259. 

  259. 
  1/133 [..............................] - ETA: 0s - loss: 0.0332
 48/133 [=========>....................] - ETA: 0s - loss: 0.0343
 95/133 [====================>.........] - ETA: 0s - loss: 0.0314
133/133 [==============================] - 0s 1ms/step - loss: 0.0301
    260. 

  260. Epoch 6/10
    261. 

  261. 
  1/133 [..............................] - ETA: 0s - loss: 0.0167
 49/133 [==========>...................] - ETA: 0s - loss: 0.0292
 97/133 [====================>.........] - ETA: 0s - loss: 0.0293
133/133 [==============================] - 0s 1ms/step - loss: 0.0287
    262. 

  262. Epoch 7/10
    263. 

  263. 
  1/133 [..............................] - ETA: 0s - loss: 0.0051
 41/133 [========>.....................] - ETA: 0s - loss: 0.0319
 79/133 [================>.............] - ETA: 0s - loss: 0.0316
120/133 [==========================>...] - ETA: 0s - loss: 0.0270
133/133 [==============================] - 0s 1ms/step - loss: 0.0279
    264. 

  264. Epoch 8/10
    265. 

  265. 
  1/133 [..............................] - ETA: 0s - loss: 0.0426
 47/133 [=========>....................] - ETA: 0s - loss: 0.0263
 94/133 [====================>.........] - ETA: 0s - loss: 0.0273
133/133 [==============================] - 0s 1ms/step - loss: 0.0254
    266. 

  266. Epoch 9/10
    267. 

  267. 
  1/133 [..............................] - ETA: 0s - loss: 0.0043
 48/133 [=========>....................] - ETA: 0s - loss: 0.0260
 96/133 [====================>.........] - ETA: 0s - loss: 0.0250
133/133 [==============================] - 0s 1ms/step - loss: 0.0238
    268. 

  268. Epoch 10/10
    269. 

  269. 
  1/133 [..............................] - ETA: 0s - loss: 0.0046
 48/133 [=========>....................] - ETA: 0s - loss: 0.0230
 93/133 [===================>..........] - ETA: 0s - loss: 0.0243
133/133 [==============================] - 0s 1ms/step - loss: 0.0235
    270. 

  270. -> test with GAN.predict
    271. 

  271. GAN tn, fp: 324, 7
    272. 

  272. GAN fn, tp: 2, 11
    273. 

  273. GAN f1 score: 0.710
    274. 

  274. GAN cohens kappa score: 0.696
    275. 

  275. 

  276. -> test with 'LR'
    277. 

  277. LR tn, fp: 179, 152
    278. 

  278. LR fn, tp: 4, 9
    279. 

  279. LR f1 score: 0.103
    280. 

  280. LR cohens kappa score: 0.036
    281. 

  281. LR average precision score: 0.057
    282. 

  282. 

  283. -> test with 'RF'
    284. 

  284. RF tn, fp: 329, 2
    285. 

  285. RF fn, tp: 9, 4
    286. 

  286. RF f1 score: 0.421
    287. 

  287. RF cohens kappa score: 0.407
    288. 

  288. 

  289. -> test with 'GB'
    290. 

  290. GB tn, fp: 328, 3
    291. 

  291. GB fn, tp: 3, 10
    292. 

  292. GB f1 score: 0.769
    293. 

  293. GB cohens kappa score: 0.760
    294. 

  294. 

  295. -> test with 'KNN'
    296. 

  296. KNN tn, fp: 312, 19
    297. 

  297. KNN fn, tp: 2, 11
    298. 

  298. KNN f1 score: 0.512
    299. 

  299. KNN cohens kappa score: 0.484
    300. 

  300. 

  301. 

  302. ====== Step 2/5 =======
    303. 

  303. -> Shuffling data
    304. 

  304. -> Spliting data to slices
    305. 

  305. 

  306. ------ Step 2/5: Slice 1/5 -------
    307. 

  307. -> Reset the GAN
    308. 

  308. -> Train generator for synthetic samples
    309. 

  309. -> create 1272 synthetic samples
    310. 

  310. -> retrain GAN for predict
    311. 

  311. Epoch 1/10
    312. 

  312. 
  1/133 [..............................] - ETA: 19s - loss: 0.0146
 45/133 [=========>....................] - ETA: 0s - loss: 0.0300 
 91/133 [===================>..........] - ETA: 0s - loss: 0.0327
131/133 [============================>.] - ETA: 0s - loss: 0.0319
133/133 [==============================] - 0s 1ms/step - loss: 0.0322
    313. 

  313. Epoch 2/10
    314. 

  314. 
  1/133 [..............................] - ETA: 0s - loss: 0.0083
 42/133 [========>.....................] - ETA: 0s - loss: 0.0297
 84/133 [=================>............] - ETA: 0s - loss: 0.0298
130/133 [============================>.] - ETA: 0s - loss: 0.0278
133/133 [==============================] - 0s 1ms/step - loss: 0.0282
    315. 

  315. Epoch 3/10
    316. 

  316. 
  1/133 [..............................] - ETA: 0s - loss: 0.0308
 47/133 [=========>....................] - ETA: 0s - loss: 0.0271
 93/133 [===================>..........] - ETA: 0s - loss: 0.0270
133/133 [==============================] - 0s 1ms/step - loss: 0.0271
    317. 

  317. Epoch 4/10
    318. 

  318. 
  1/133 [..............................] - ETA: 0s - loss: 0.0067
 48/133 [=========>....................] - ETA: 0s - loss: 0.0376
 94/133 [====================>.........] - ETA: 0s - loss: 0.0294
133/133 [==============================] - 0s 1ms/step - loss: 0.0260
    319. 

  319. Epoch 5/10
    320. 

  320. 
  1/133 [..............................] - ETA: 0s - loss: 0.1871
 47/133 [=========>....................] - ETA: 0s - loss: 0.0271
 92/133 [===================>..........] - ETA: 0s - loss: 0.0243
133/133 [==============================] - 0s 1ms/step - loss: 0.0248
    321. 

  321. Epoch 6/10
    322. 

  322. 
  1/133 [..............................] - ETA: 0s - loss: 0.0034
 47/133 [=========>....................] - ETA: 0s - loss: 0.0261
 92/133 [===================>..........] - ETA: 0s - loss: 0.0233
133/133 [==============================] - 0s 1ms/step - loss: 0.0238
    323. 

  323. Epoch 7/10
    324. 

  324. 
  1/133 [..............................] - ETA: 0s - loss: 0.0067
 49/133 [==========>...................] - ETA: 0s - loss: 0.0143
 95/133 [====================>.........] - ETA: 0s - loss: 0.0194
133/133 [==============================] - 0s 1ms/step - loss: 0.0223
    325. 

  325. Epoch 8/10
    326. 

  326. 
  1/133 [..............................] - ETA: 0s - loss: 0.0028
 47/133 [=========>....................] - ETA: 0s - loss: 0.0203
 93/133 [===================>..........] - ETA: 0s - loss: 0.0187
133/133 [==============================] - 0s 1ms/step - loss: 0.0215
    327. 

  327. Epoch 9/10
    328. 

  328. 
  1/133 [..............................] - ETA: 0s - loss: 0.0173
 46/133 [=========>....................] - ETA: 0s - loss: 0.0155
 93/133 [===================>..........] - ETA: 0s - loss: 0.0213
133/133 [==============================] - 0s 1ms/step - loss: 0.0201
    329. 

  329. Epoch 10/10
    330. 

  330. 
  1/133 [..............................] - ETA: 0s - loss: 0.0024
 46/133 [=========>....................] - ETA: 0s - loss: 0.0171
 87/133 [==================>...........] - ETA: 0s - loss: 0.0188
133/133 [==============================] - ETA: 0s - loss: 0.0204
133/133 [==============================] - 0s 1ms/step - loss: 0.0204
    331. 

  331. -> test with GAN.predict
    332. 

  332. GAN tn, fp: 325, 7
    333. 

  333. GAN fn, tp: 2, 12
    334. 

  334. GAN f1 score: 0.727
    335. 

  335. GAN cohens kappa score: 0.714
    336. 

  336. 

  337. -> test with 'LR'
    338. 

  338. LR tn, fp: 163, 169
    339. 

  339. LR fn, tp: 4, 10
    340. 

  340. LR f1 score: 0.104
    341. 

  341. LR cohens kappa score: 0.031
    342. 

  342. LR average precision score: 0.065
    343. 

  343. 

  344. -> test with 'RF'
    345. 

  345. RF tn, fp: 331, 1
    346. 

  346. RF fn, tp: 5, 9
    347. 

  347. RF f1 score: 0.750
    348. 

  348. RF cohens kappa score: 0.741
    349. 

  349. 

  350. -> test with 'GB'
    351. 

  351. GB tn, fp: 331, 1
    352. 

  352. GB fn, tp: 4, 10
    353. 

  353. GB f1 score: 0.800
    354. 

  354. GB cohens kappa score: 0.793
    355. 

  355. 

  356. -> test with 'KNN'
    357. 

  357. KNN tn, fp: 320, 12
    358. 

  358. KNN fn, tp: 2, 12
    359. 

  359. KNN f1 score: 0.632
    360. 

  360. KNN cohens kappa score: 0.612
    361. 

  361. 

  362. 

  363. ------ Step 2/5: Slice 2/5 -------
    364. 

  364. -> Reset the GAN
    365. 

  365. -> Train generator for synthetic samples
    366. 

  366. -> create 1272 synthetic samples
    367. 

  367. -> retrain GAN for predict
    368. 

  368. Epoch 1/10
    369. 

  369. 
  1/133 [..............................] - ETA: 23s - loss: 0.0215
 42/133 [========>.....................] - ETA: 0s - loss: 0.0295 
 78/133 [================>.............] - ETA: 0s - loss: 0.0410
113/133 [========================>.....] - ETA: 0s - loss: 0.0375
133/133 [==============================] - 0s 1ms/step - loss: 0.0392
    370. 

  370. Epoch 2/10
    371. 

  371. 
  1/133 [..............................] - ETA: 0s - loss: 0.0148
 41/133 [========>.....................] - ETA: 0s - loss: 0.0291
 82/133 [=================>............] - ETA: 0s - loss: 0.0351
125/133 [===========================>..] - ETA: 0s - loss: 0.0366
133/133 [==============================] - 0s 1ms/step - loss: 0.0370
    372. 

  372. Epoch 3/10
    373. 

  373. 
  1/133 [..............................] - ETA: 0s - loss: 0.0078
 47/133 [=========>....................] - ETA: 0s - loss: 0.0322
 92/133 [===================>..........] - ETA: 0s - loss: 0.0334
133/133 [==============================] - 0s 1ms/step - loss: 0.0354
    374. 

  374. Epoch 4/10
    375. 

  375. 
  1/133 [..............................] - ETA: 0s - loss: 0.0362
 48/133 [=========>....................] - ETA: 0s - loss: 0.0318
 95/133 [====================>.........] - ETA: 0s - loss: 0.0338
133/133 [==============================] - 0s 1ms/step - loss: 0.0341
    376. 

  376. Epoch 5/10
    377. 

  377. 
  1/133 [..............................] - ETA: 0s - loss: 0.0074
 47/133 [=========>....................] - ETA: 0s - loss: 0.0310
 93/133 [===================>..........] - ETA: 0s - loss: 0.0321
133/133 [==============================] - 0s 1ms/step - loss: 0.0323
    378. 

  378. Epoch 6/10
    379. 

  379. 
  1/133 [..............................] - ETA: 0s - loss: 0.0154
 48/133 [=========>....................] - ETA: 0s - loss: 0.0254
 94/133 [====================>.........] - ETA: 0s - loss: 0.0243
133/133 [==============================] - 0s 1ms/step - loss: 0.0296
    380. 

  380. Epoch 7/10
    381. 

  381. 
  1/133 [..............................] - ETA: 0s - loss: 0.0066
 47/133 [=========>....................] - ETA: 0s - loss: 0.0264
 94/133 [====================>.........] - ETA: 0s - loss: 0.0294
133/133 [==============================] - 0s 1ms/step - loss: 0.0301
    382. 

  382. Epoch 8/10
    383. 

  383. 
  1/133 [..............................] - ETA: 0s - loss: 0.0016
 45/133 [=========>....................] - ETA: 0s - loss: 0.0273
 88/133 [==================>...........] - ETA: 0s - loss: 0.0301
133/133 [==============================] - 0s 1ms/step - loss: 0.0283
    384. 

  384. Epoch 9/10
    385. 

  385. 
  1/133 [..............................] - ETA: 0s - loss: 0.0279
 47/133 [=========>....................] - ETA: 0s - loss: 0.0367
 94/133 [====================>.........] - ETA: 0s - loss: 0.0287
133/133 [==============================] - 0s 1ms/step - loss: 0.0275
    386. 

  386. Epoch 10/10
    387. 

  387. 
  1/133 [..............................] - ETA: 0s - loss: 0.0044
 47/133 [=========>....................] - ETA: 0s - loss: 0.0317
 94/133 [====================>.........] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 1ms/step - loss: 0.0262
    388. 

  388. -> test with GAN.predict
    389. 

  389. GAN tn, fp: 328, 4
    390. 

  390. GAN fn, tp: 1, 13
    391. 

  391. GAN f1 score: 0.839
    392. 

  392. GAN cohens kappa score: 0.831
    393. 

  393. 

  394. -> test with 'LR'
    395. 

  395. LR tn, fp: 183, 149
    396. 

  396. LR fn, tp: 4, 10
    397. 

  397. LR f1 score: 0.116
    398. 

  398. LR cohens kappa score: 0.045
    399. 

  399. LR average precision score: 0.059
    400. 

  400. 

  401. -> test with 'RF'
    402. 

  402. RF tn, fp: 332, 0
    403. 

  403. RF fn, tp: 5, 9
    404. 

  404. RF f1 score: 0.783
    405. 

  405. RF cohens kappa score: 0.775
    406. 

  406. 

  407. -> test with 'GB'
    408. 

  408. GB tn, fp: 330, 2
    409. 

  409. GB fn, tp: 3, 11
    410. 

  410. GB f1 score: 0.815
    411. 

  411. GB cohens kappa score: 0.807
    412. 

  412. 

  413. -> test with 'KNN'
    414. 

  414. KNN tn, fp: 302, 30
    415. 

  415. KNN fn, tp: 1, 13
    416. 

  416. KNN f1 score: 0.456
    417. 

  417. KNN cohens kappa score: 0.421
    418. 

  418. 

  419. 

  420. ------ Step 2/5: Slice 3/5 -------
    421. 

  421. -> Reset the GAN
    422. 

  422. -> Train generator for synthetic samples
    423. 

  423. -> create 1272 synthetic samples
    424. 

  424. -> retrain GAN for predict
    425. 

  425. Epoch 1/10
    426. 

  426. 
  1/133 [..............................] - ETA: 20s - loss: 0.0360
 48/133 [=========>....................] - ETA: 0s - loss: 0.0375 
 95/133 [====================>.........] - ETA: 0s - loss: 0.0361
133/133 [==============================] - 0s 1ms/step - loss: 0.0415
    427. 

  427. Epoch 2/10
    428. 

  428. 
  1/133 [..............................] - ETA: 0s - loss: 0.0674
 48/133 [=========>....................] - ETA: 0s - loss: 0.0315
 95/133 [====================>.........] - ETA: 0s - loss: 0.0375
133/133 [==============================] - 0s 1ms/step - loss: 0.0384
    429. 

  429. Epoch 3/10
    430. 

  430. 
  1/133 [..............................] - ETA: 0s - loss: 0.0159
 48/133 [=========>....................] - ETA: 0s - loss: 0.0390
 95/133 [====================>.........] - ETA: 0s - loss: 0.0405
133/133 [==============================] - 0s 1ms/step - loss: 0.0372
    431. 

  431. Epoch 4/10
    432. 

  432. 
  1/133 [..............................] - ETA: 0s - loss: 0.0543
 48/133 [=========>....................] - ETA: 0s - loss: 0.0337
 95/133 [====================>.........] - ETA: 0s - loss: 0.0375
133/133 [==============================] - 0s 1ms/step - loss: 0.0354
    433. 

  433. Epoch 5/10
    434. 

  434. 
  1/133 [..............................] - ETA: 0s - loss: 0.0859
 47/133 [=========>....................] - ETA: 0s - loss: 0.0331
 94/133 [====================>.........] - ETA: 0s - loss: 0.0374
133/133 [==============================] - 0s 1ms/step - loss: 0.0341
    435. 

  435. Epoch 6/10
    436. 

  436. 
  1/133 [..............................] - ETA: 0s - loss: 0.0152
 48/133 [=========>....................] - ETA: 0s - loss: 0.0238
 93/133 [===================>..........] - ETA: 0s - loss: 0.0273
128/133 [===========================>..] - ETA: 0s - loss: 0.0325
133/133 [==============================] - 0s 1ms/step - loss: 0.0322
    437. 

  437. Epoch 7/10
    438. 

  438. 
  1/133 [..............................] - ETA: 0s - loss: 0.0259
 45/133 [=========>....................] - ETA: 0s - loss: 0.0296
 92/133 [===================>..........] - ETA: 0s - loss: 0.0349
133/133 [==============================] - 0s 1ms/step - loss: 0.0315
    439. 

  439. Epoch 8/10
    440. 

  440. 
  1/133 [..............................] - ETA: 0s - loss: 0.0379
 47/133 [=========>....................] - ETA: 0s - loss: 0.0328
 94/133 [====================>.........] - ETA: 0s - loss: 0.0266
133/133 [==============================] - 0s 1ms/step - loss: 0.0291
    441. 

  441. Epoch 9/10
    442. 

  442. 
  1/133 [..............................] - ETA: 0s - loss: 0.0226
 48/133 [=========>....................] - ETA: 0s - loss: 0.0310
 95/133 [====================>.........] - ETA: 0s - loss: 0.0293
133/133 [==============================] - 0s 1ms/step - loss: 0.0280
    443. 

  443. Epoch 10/10
    444. 

  444. 
  1/133 [..............................] - ETA: 0s - loss: 0.0198
 47/133 [=========>....................] - ETA: 0s - loss: 0.0226
 93/133 [===================>..........] - ETA: 0s - loss: 0.0307
133/133 [==============================] - 0s 1ms/step - loss: 0.0274
    445. 

  445. -> test with GAN.predict
    446. 

  446. GAN tn, fp: 327, 5
    447. 

  447. GAN fn, tp: 1, 13
    448. 

  448. GAN f1 score: 0.813
    449. 

  449. GAN cohens kappa score: 0.804
    450. 

  450. 

  451. -> test with 'LR'
    452. 

  452. LR tn, fp: 191, 141
    453. 

  453. LR fn, tp: 4, 10
    454. 

  454. LR f1 score: 0.121
    455. 

  455. LR cohens kappa score: 0.051
    456. 

  456. LR average precision score: 0.071
    457. 

  457. 

  458. -> test with 'RF'
    459. 

  459. RF tn, fp: 332, 0
    460. 

  460. RF fn, tp: 7, 7
    461. 

  461. RF f1 score: 0.667
    462. 

  462. RF cohens kappa score: 0.657
    463. 

  463. 

  464. -> test with 'GB'
    465. 

  465. GB tn, fp: 332, 0
    466. 

  466. GB fn, tp: 8, 6
    467. 

  467. GB f1 score: 0.600
    468. 

  468. GB cohens kappa score: 0.590
    469. 

  469. 

  470. -> test with 'KNN'
    471. 

  471. KNN tn, fp: 309, 23
    472. 

  472. KNN fn, tp: 3, 11
    473. 

  473. KNN f1 score: 0.458
    474. 

  474. KNN cohens kappa score: 0.425
    475. 

  475. 

  476. 

  477. ------ Step 2/5: Slice 4/5 -------
    478. 

  478. -> Reset the GAN
    479. 

  479. -> Train generator for synthetic samples
    480. 

  480. -> create 1272 synthetic samples
    481. 

  481. -> retrain GAN for predict
    482. 

  482. Epoch 1/10
    483. 

  483. 
  1/133 [..............................] - ETA: 20s - loss: 0.0015
 45/133 [=========>....................] - ETA: 0s - loss: 0.0288 
 86/133 [==================>...........] - ETA: 0s - loss: 0.0307
127/133 [===========================>..] - ETA: 0s - loss: 0.0325
133/133 [==============================] - 0s 1ms/step - loss: 0.0316
    484. 

  484. Epoch 2/10
    485. 

  485. 
  1/133 [..............................] - ETA: 0s - loss: 0.0520
 41/133 [========>.....................] - ETA: 0s - loss: 0.0337
 86/133 [==================>...........] - ETA: 0s - loss: 0.0283
129/133 [============================>.] - ETA: 0s - loss: 0.0297
133/133 [==============================] - 0s 1ms/step - loss: 0.0301
    486. 

  486. Epoch 3/10
    487. 

  487. 
  1/133 [..............................] - ETA: 0s - loss: 0.0063
 48/133 [=========>....................] - ETA: 0s - loss: 0.0267
 93/133 [===================>..........] - ETA: 0s - loss: 0.0270
133/133 [==============================] - 0s 1ms/step - loss: 0.0289
    488. 

  488. Epoch 4/10
    489. 

  489. 
  1/133 [..............................] - ETA: 0s - loss: 0.0121
 47/133 [=========>....................] - ETA: 0s - loss: 0.0220
 93/133 [===================>..........] - ETA: 0s - loss: 0.0249
133/133 [==============================] - 0s 1ms/step - loss: 0.0279
    490. 

  490. Epoch 5/10
    491. 

  491. 
  1/133 [..............................] - ETA: 0s - loss: 0.0602
 47/133 [=========>....................] - ETA: 0s - loss: 0.0297
 94/133 [====================>.........] - ETA: 0s - loss: 0.0282
133/133 [==============================] - 0s 1ms/step - loss: 0.0271
    492. 

  492. Epoch 6/10
    493. 

  493. 
  1/133 [..............................] - ETA: 0s - loss: 0.0151
 47/133 [=========>....................] - ETA: 0s - loss: 0.0239
 92/133 [===================>..........] - ETA: 0s - loss: 0.0273
133/133 [==============================] - 0s 1ms/step - loss: 0.0254
    494. 

  494. Epoch 7/10
    495. 

  495. 
  1/133 [..............................] - ETA: 0s - loss: 0.0300
 48/133 [=========>....................] - ETA: 0s - loss: 0.0216
 95/133 [====================>.........] - ETA: 0s - loss: 0.0235
133/133 [==============================] - 0s 1ms/step - loss: 0.0251
    496. 

  496. Epoch 8/10
    497. 

  497. 
  1/133 [..............................] - ETA: 0s - loss: 0.0038
 48/133 [=========>....................] - ETA: 0s - loss: 0.0269
 94/133 [====================>.........] - ETA: 0s - loss: 0.0264
133/133 [==============================] - 0s 1ms/step - loss: 0.0238
    498. 

  498. Epoch 9/10
    499. 

  499. 
  1/133 [..............................] - ETA: 0s - loss: 0.0058
 46/133 [=========>....................] - ETA: 0s - loss: 0.0234
 92/133 [===================>..........] - ETA: 0s - loss: 0.0267
133/133 [==============================] - 0s 1ms/step - loss: 0.0235
    500. 

  500. Epoch 10/10
    501. 

  501. 
  1/133 [..............................] - ETA: 0s - loss: 0.0056
 46/133 [=========>....................] - ETA: 0s - loss: 0.0187
 93/133 [===================>..........] - ETA: 0s - loss: 0.0223
133/133 [==============================] - 0s 1ms/step - loss: 0.0223
    502. 

  502. -> test with GAN.predict
    503. 

  503. GAN tn, fp: 326, 6
    504. 

  504. GAN fn, tp: 5, 9
    505. 

  505. GAN f1 score: 0.621
    506. 

  506. GAN cohens kappa score: 0.604
    507. 

  507. 

  508. -> test with 'LR'
    509. 

  509. LR tn, fp: 192, 140
    510. 

  510. LR fn, tp: 8, 6
    511. 

  511. LR f1 score: 0.075
    512. 

  512. LR cohens kappa score: 0.001
    513. 

  513. LR average precision score: 0.051
    514. 

  514. 

  515. -> test with 'RF'
    516. 

  516. RF tn, fp: 332, 0
    517. 

  517. RF fn, tp: 10, 4
    518. 

  518. RF f1 score: 0.444
    519. 

  519. RF cohens kappa score: 0.434
    520. 

  520. 

  521. -> test with 'GB'
    522. 

  522. GB tn, fp: 331, 1
    523. 

  523. GB fn, tp: 4, 10
    524. 

  524. GB f1 score: 0.800
    525. 

  525. GB cohens kappa score: 0.793
    526. 

  526. 

  527. -> test with 'KNN'
    528. 

  528. KNN tn, fp: 315, 17
    529. 

  529. KNN fn, tp: 2, 12
    530. 

  530. KNN f1 score: 0.558
    531. 

  531. KNN cohens kappa score: 0.533
    532. 

  532. 

  533. 

  534. ------ Step 2/5: Slice 5/5 -------
    535. 

  535. -> Reset the GAN
    536. 

  536. -> Train generator for synthetic samples
    537. 

  537. -> create 1272 synthetic samples
    538. 

  538. -> retrain GAN for predict
    539. 

  539. Epoch 1/10
    540. 

  540. 
  1/133 [..............................] - ETA: 19s - loss: 0.0045
 46/133 [=========>....................] - ETA: 0s - loss: 0.0303 
 93/133 [===================>..........] - ETA: 0s - loss: 0.0381
133/133 [==============================] - 0s 1ms/step - loss: 0.0363
    541. 

  541. Epoch 2/10
    542. 

  542. 
  1/133 [..............................] - ETA: 0s - loss: 0.0041
 48/133 [=========>....................] - ETA: 0s - loss: 0.0396
 94/133 [====================>.........] - ETA: 0s - loss: 0.0362
133/133 [==============================] - 0s 1ms/step - loss: 0.0341
    543. 

  543. Epoch 3/10
    544. 

  544. 
  1/133 [..............................] - ETA: 0s - loss: 0.0036
 43/133 [========>.....................] - ETA: 0s - loss: 0.0316
 83/133 [=================>............] - ETA: 0s - loss: 0.0287
124/133 [==========================>...] - ETA: 0s - loss: 0.0351
133/133 [==============================] - 0s 1ms/step - loss: 0.0336
    545. 

  545. Epoch 4/10
    546. 

  546. 
  1/133 [..............................] - ETA: 0s - loss: 0.0287
 47/133 [=========>....................] - ETA: 0s - loss: 0.0296
 93/133 [===================>..........] - ETA: 0s - loss: 0.0289
133/133 [==============================] - 0s 1ms/step - loss: 0.0322
    547. 

  547. Epoch 5/10
    548. 

  548. 
  1/133 [..............................] - ETA: 0s - loss: 0.0088
 48/133 [=========>....................] - ETA: 0s - loss: 0.0319
 94/133 [====================>.........] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 1ms/step - loss: 0.0306
    549. 

  549. Epoch 6/10
    550. 

  550. 
  1/133 [..............................] - ETA: 0s - loss: 0.0046
 48/133 [=========>....................] - ETA: 0s - loss: 0.0270
 95/133 [====================>.........] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 1ms/step - loss: 0.0292
    551. 

  551. Epoch 7/10
    552. 

  552. 
  1/133 [..............................] - ETA: 0s - loss: 0.0124
 47/133 [=========>....................] - ETA: 0s - loss: 0.0305
 92/133 [===================>..........] - ETA: 0s - loss: 0.0305
133/133 [==============================] - 0s 1ms/step - loss: 0.0282
    553. 

  553. Epoch 8/10
    554. 

  554. 
  1/133 [..............................] - ETA: 0s - loss: 0.0061
 48/133 [=========>....................] - ETA: 0s - loss: 0.0255
 94/133 [====================>.........] - ETA: 0s - loss: 0.0260
133/133 [==============================] - 0s 1ms/step - loss: 0.0268
    555. 

  555. Epoch 9/10
    556. 

  556. 
  1/133 [..............................] - ETA: 0s - loss: 0.0109
 48/133 [=========>....................] - ETA: 0s - loss: 0.0194
 94/133 [====================>.........] - ETA: 0s - loss: 0.0195
133/133 [==============================] - 0s 1ms/step - loss: 0.0269
    557. 

  557. Epoch 10/10
    558. 

  558. 
  1/133 [..............................] - ETA: 0s - loss: 0.0087
 48/133 [=========>....................] - ETA: 0s - loss: 0.0151
 94/133 [====================>.........] - ETA: 0s - loss: 0.0276
133/133 [==============================] - 0s 1ms/step - loss: 0.0269
    559. 

  559. -> test with GAN.predict
    560. 

  560. GAN tn, fp: 327, 4
    561. 

  561. GAN fn, tp: 5, 8
    562. 

  562. GAN f1 score: 0.640
    563. 

  563. GAN cohens kappa score: 0.626
    564. 

  564. 

  565. -> test with 'LR'
    566. 

  566. LR tn, fp: 187, 144
    567. 

  567. LR fn, tp: 5, 8
    568. 

  568. LR f1 score: 0.097
    569. 

  569. LR cohens kappa score: 0.029
    570. 

  570. LR average precision score: 0.074
    571. 

  571. 

  572. -> test with 'RF'
    573. 

  573. RF tn, fp: 331, 0
    574. 

  574. RF fn, tp: 7, 6
    575. 

  575. RF f1 score: 0.632
    576. 

  576. RF cohens kappa score: 0.623
    577. 

  577. 

  578. -> test with 'GB'
    579. 

  579. GB tn, fp: 328, 3
    580. 

  580. GB fn, tp: 3, 10
    581. 

  581. GB f1 score: 0.769
    582. 

  582. GB cohens kappa score: 0.760
    583. 

  583. 

  584. -> test with 'KNN'
    585. 

  585. KNN tn, fp: 304, 27
    586. 

  586. KNN fn, tp: 0, 13
    587. 

  587. KNN f1 score: 0.491
    588. 

  588. KNN cohens kappa score: 0.460
    589. 

  589. 

  590. 

  591. ====== Step 3/5 =======
    592. 

  592. -> Shuffling data
    593. 

  593. -> Spliting data to slices
    594. 

  594. 

  595. ------ Step 3/5: Slice 1/5 -------
    596. 

  596. -> Reset the GAN
    597. 

  597. -> Train generator for synthetic samples
    598. 

  598. -> create 1272 synthetic samples
    599. 

  599. -> retrain GAN for predict
    600. 

  600. Epoch 1/10
    601. 

  601. 
  1/133 [..............................] - ETA: 28s - loss: 0.0284
 30/133 [=====>........................] - ETA: 0s - loss: 0.0480 
 60/133 [============>.................] - ETA: 0s - loss: 0.0391
 90/133 [===================>..........] - ETA: 0s - loss: 0.0307
123/133 [==========================>...] - ETA: 0s - loss: 0.0320
133/133 [==============================] - 0s 2ms/step - loss: 0.0320
    602. 

  602. Epoch 2/10
    603. 

  603. 
  1/133 [..............................] - ETA: 0s - loss: 0.0206
 32/133 [======>.......................] - ETA: 0s - loss: 0.0206
 56/133 [===========>..................] - ETA: 0s - loss: 0.0289
 80/133 [=================>............] - ETA: 0s - loss: 0.0301
105/133 [======================>.......] - ETA: 0s - loss: 0.0301
130/133 [============================>.] - ETA: 0s - loss: 0.0303
133/133 [==============================] - 0s 2ms/step - loss: 0.0301
    604. 

  604. Epoch 3/10
    605. 

  605. 
  1/133 [..............................] - ETA: 0s - loss: 0.0372
 26/133 [====>.........................] - ETA: 0s - loss: 0.0210
 53/133 [==========>...................] - ETA: 0s - loss: 0.0242
 82/133 [=================>............] - ETA: 0s - loss: 0.0288
109/133 [=======================>......] - ETA: 0s - loss: 0.0279
133/133 [==============================] - 0s 2ms/step - loss: 0.0285
    606. 

  606. Epoch 4/10
    607. 

  607. 
  1/133 [..............................] - ETA: 0s - loss: 0.0115
 29/133 [=====>........................] - ETA: 0s - loss: 0.0274
 52/133 [==========>...................] - ETA: 0s - loss: 0.0283
 80/133 [=================>............] - ETA: 0s - loss: 0.0235
108/133 [=======================>......] - ETA: 0s - loss: 0.0245
133/133 [==============================] - 0s 2ms/step - loss: 0.0268
    608. 

  608. Epoch 5/10
    609. 

  609. 
  1/133 [..............................] - ETA: 0s - loss: 0.0128
 29/133 [=====>........................] - ETA: 0s - loss: 0.0194
 56/133 [===========>..................] - ETA: 0s - loss: 0.0258
 87/133 [==================>...........] - ETA: 0s - loss: 0.0263
116/133 [=========================>....] - ETA: 0s - loss: 0.0246
133/133 [==============================] - 0s 2ms/step - loss: 0.0255
    610. 

  610. Epoch 6/10
    611. 

  611. 
  1/133 [..............................] - ETA: 0s - loss: 0.0290
 28/133 [=====>........................] - ETA: 0s - loss: 0.0172
 52/133 [==========>...................] - ETA: 0s - loss: 0.0215
 72/133 [===============>..............] - ETA: 0s - loss: 0.0242
 94/133 [====================>.........] - ETA: 0s - loss: 0.0233
113/133 [========================>.....] - ETA: 0s - loss: 0.0230
131/133 [============================>.] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 2ms/step - loss: 0.0246
    612. 

  612. Epoch 7/10
    613. 

  613. 
  1/133 [..............................] - ETA: 0s - loss: 0.0129
 17/133 [==>...........................] - ETA: 0s - loss: 0.0230
 31/133 [=====>........................] - ETA: 0s - loss: 0.0191
 49/133 [==========>...................] - ETA: 0s - loss: 0.0250
 66/133 [=============>................] - ETA: 0s - loss: 0.0234
 81/133 [=================>............] - ETA: 0s - loss: 0.0220
 97/133 [====================>.........] - ETA: 0s - loss: 0.0252
114/133 [========================>.....] - ETA: 0s - loss: 0.0253
133/133 [==============================] - ETA: 0s - loss: 0.0240
133/133 [==============================] - 0s 3ms/step - loss: 0.0240
    614. 

  614. Epoch 8/10
    615. 

  615. 
  1/133 [..............................] - ETA: 0s - loss: 0.0029
 24/133 [====>.........................] - ETA: 0s - loss: 0.0237
 47/133 [=========>....................] - ETA: 0s - loss: 0.0213
 66/133 [=============>................] - ETA: 0s - loss: 0.0210
 89/133 [===================>..........] - ETA: 0s - loss: 0.0196
112/133 [========================>.....] - ETA: 0s - loss: 0.0222
129/133 [============================>.] - ETA: 0s - loss: 0.0216
133/133 [==============================] - 0s 2ms/step - loss: 0.0214
    616. 

  616. Epoch 9/10
    617. 

  617. 
  1/133 [..............................] - ETA: 0s - loss: 0.0023
 17/133 [==>...........................] - ETA: 0s - loss: 0.0200
 41/133 [========>.....................] - ETA: 0s - loss: 0.0218
 71/133 [===============>..............] - ETA: 0s - loss: 0.0274
 99/133 [=====================>........] - ETA: 0s - loss: 0.0259
130/133 [============================>.] - ETA: 0s - loss: 0.0218
133/133 [==============================] - 0s 2ms/step - loss: 0.0215
    618. 

  618. Epoch 10/10
    619. 

  619. 
  1/133 [..............................] - ETA: 0s - loss: 0.0060
 30/133 [=====>........................] - ETA: 0s - loss: 0.0095
 59/133 [============>.................] - ETA: 0s - loss: 0.0183
 82/133 [=================>............] - ETA: 0s - loss: 0.0200
105/133 [======================>.......] - ETA: 0s - loss: 0.0208
127/133 [===========================>..] - ETA: 0s - loss: 0.0216
133/133 [==============================] - 0s 2ms/step - loss: 0.0211
    620. 

  620. -> test with GAN.predict
    621. 

  621. GAN tn, fp: 327, 5
    622. 

  622. GAN fn, tp: 2, 12
    623. 

  623. GAN f1 score: 0.774
    624. 

  624. GAN cohens kappa score: 0.764
    625. 

  625. 

  626. -> test with 'LR'
    627. 

  627. LR tn, fp: 169, 163
    628. 

  628. LR fn, tp: 3, 11
    629. 

  629. LR f1 score: 0.117
    630. 

  630. LR cohens kappa score: 0.046
    631. 

  631. LR average precision score: 0.076
    632. 

  632. 

  633. -> test with 'RF'
    634. 

  634. RF tn, fp: 332, 0
    635. 

  635. RF fn, tp: 7, 7
    636. 

  636. RF f1 score: 0.667
    637. 

  637. RF cohens kappa score: 0.657
    638. 

  638. 

  639. -> test with 'GB'
    640. 

  640. GB tn, fp: 331, 1
    641. 

  641. GB fn, tp: 3, 11
    642. 

  642. GB f1 score: 0.846
    643. 

  643. GB cohens kappa score: 0.840
    644. 

  644. 

  645. -> test with 'KNN'
    646. 

  646. KNN tn, fp: 320, 12
    647. 

  647. KNN fn, tp: 1, 13
    648. 

  648. KNN f1 score: 0.667
    649. 

  649. KNN cohens kappa score: 0.648
    650. 

  650. 

  651. 

  652. ------ Step 3/5: Slice 2/5 -------
    653. 

  653. -> Reset the GAN
    654. 

  654. -> Train generator for synthetic samples
    655. 

  655. -> create 1272 synthetic samples
    656. 

  656. -> retrain GAN for predict
    657. 

  657. Epoch 1/10
    658. 

  658. 
  1/133 [..............................] - ETA: 19s - loss: 0.0256
 43/133 [========>.....................] - ETA: 0s - loss: 0.0350 
 83/133 [=================>............] - ETA: 0s - loss: 0.0298
123/133 [==========================>...] - ETA: 0s - loss: 0.0341
133/133 [==============================] - 0s 1ms/step - loss: 0.0343
    659. 

  659. Epoch 2/10
    660. 

  660. 
  1/133 [..............................] - ETA: 0s - loss: 0.0712
 44/133 [========>.....................] - ETA: 0s - loss: 0.0278
 89/133 [===================>..........] - ETA: 0s - loss: 0.0333
133/133 [==============================] - ETA: 0s - loss: 0.0323
133/133 [==============================] - 0s 1ms/step - loss: 0.0323
    661. 

  661. Epoch 3/10
    662. 

  662. 
  1/133 [..............................] - ETA: 0s - loss: 0.0081
 48/133 [=========>....................] - ETA: 0s - loss: 0.0240
 93/133 [===================>..........] - ETA: 0s - loss: 0.0286
133/133 [==============================] - 0s 1ms/step - loss: 0.0319
    663. 

  663. Epoch 4/10
    664. 

  664. 
  1/133 [..............................] - ETA: 0s - loss: 0.0203
 46/133 [=========>....................] - ETA: 0s - loss: 0.0351
 92/133 [===================>..........] - ETA: 0s - loss: 0.0291
133/133 [==============================] - 0s 1ms/step - loss: 0.0297
    665. 

  665. Epoch 5/10
    666. 

  666. 
  1/133 [..............................] - ETA: 0s - loss: 0.0163
 48/133 [=========>....................] - ETA: 0s - loss: 0.0260
 95/133 [====================>.........] - ETA: 0s - loss: 0.0248
133/133 [==============================] - 0s 1ms/step - loss: 0.0281
    667. 

  667. Epoch 6/10
    668. 

  668. 
  1/133 [..............................] - ETA: 0s - loss: 0.0198
 46/133 [=========>....................] - ETA: 0s - loss: 0.0262
 87/133 [==================>...........] - ETA: 0s - loss: 0.0249
129/133 [============================>.] - ETA: 0s - loss: 0.0274
133/133 [==============================] - 0s 1ms/step - loss: 0.0272
    669. 

  669. Epoch 7/10
    670. 

  670. 
  1/133 [..............................] - ETA: 0s - loss: 0.0475
 48/133 [=========>....................] - ETA: 0s - loss: 0.0229
 96/133 [====================>.........] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 1ms/step - loss: 0.0261
    671. 

  671. Epoch 8/10
    672. 

  672. 
  1/133 [..............................] - ETA: 0s - loss: 0.0036
 49/133 [==========>...................] - ETA: 0s - loss: 0.0287
 98/133 [=====================>........] - ETA: 0s - loss: 0.0259
133/133 [==============================] - 0s 1ms/step - loss: 0.0242
    673. 

  673. Epoch 9/10
    674. 

  674. 
  1/133 [..............................] - ETA: 0s - loss: 0.0029
 49/133 [==========>...................] - ETA: 0s - loss: 0.0258
 97/133 [====================>.........] - ETA: 0s - loss: 0.0258
133/133 [==============================] - 0s 1ms/step - loss: 0.0241
    675. 

  675. Epoch 10/10
    676. 

  676. 
  1/133 [..............................] - ETA: 0s - loss: 0.0093
 49/133 [==========>...................] - ETA: 0s - loss: 0.0254
 97/133 [====================>.........] - ETA: 0s - loss: 0.0254
133/133 [==============================] - 0s 1ms/step - loss: 0.0235
    677. 

  677. -> test with GAN.predict
    678. 

  678. GAN tn, fp: 325, 7
    679. 

  679. GAN fn, tp: 3, 11
    680. 

  680. GAN f1 score: 0.688
    681. 

  681. GAN cohens kappa score: 0.673
    682. 

  682. 

  683. -> test with 'LR'
    684. 

  684. LR tn, fp: 188, 144
    685. 

  685. LR fn, tp: 3, 11
    686. 

  686. LR f1 score: 0.130
    687. 

  687. LR cohens kappa score: 0.060
    688. 

  688. LR average precision score: 0.066
    689. 

  689. 

  690. -> test with 'RF'
    691. 

  691. RF tn, fp: 330, 2
    692. 

  692. RF fn, tp: 8, 6
    693. 

  693. RF f1 score: 0.545
    694. 

  694. RF cohens kappa score: 0.532
    695. 

  695. 

  696. -> test with 'GB'
    697. 

  697. GB tn, fp: 330, 2
    698. 

  698. GB fn, tp: 0, 14
    699. 

  699. GB f1 score: 0.933
    700. 

  700. GB cohens kappa score: 0.930
    701. 

  701. 

  702. -> test with 'KNN'
    703. 

  703. KNN tn, fp: 319, 13
    704. 

  704. KNN fn, tp: 0, 14
    705. 

  705. KNN f1 score: 0.683
    706. 

  706. KNN cohens kappa score: 0.665
    707. 

  707. 

  708. 

  709. ------ Step 3/5: Slice 3/5 -------
    710. 

  710. -> Reset the GAN
    711. 

  711. -> Train generator for synthetic samples
    712. 

  712. -> create 1272 synthetic samples
    713. 

  713. -> retrain GAN for predict
    714. 

  714. Epoch 1/10
    715. 

  715. 
  1/133 [..............................] - ETA: 19s - loss: 0.0107
 47/133 [=========>....................] - ETA: 0s - loss: 0.0388 
 94/133 [====================>.........] - ETA: 0s - loss: 0.0324
133/133 [==============================] - 0s 1ms/step - loss: 0.0330
    716. 

  716. Epoch 2/10
    717. 

  717. 
  1/133 [..............................] - ETA: 0s - loss: 0.0080
 49/133 [==========>...................] - ETA: 0s - loss: 0.0332
 96/133 [====================>.........] - ETA: 0s - loss: 0.0306
133/133 [==============================] - 0s 1ms/step - loss: 0.0297
    718. 

  718. Epoch 3/10
    719. 

  719. 
  1/133 [..............................] - ETA: 0s - loss: 0.0053
 49/133 [==========>...................] - ETA: 0s - loss: 0.0260
 95/133 [====================>.........] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 1ms/step - loss: 0.0295
    720. 

  720. Epoch 4/10
    721. 

  721. 
  1/133 [..............................] - ETA: 0s - loss: 0.0081
 49/133 [==========>...................] - ETA: 0s - loss: 0.0302
 97/133 [====================>.........] - ETA: 0s - loss: 0.0256
133/133 [==============================] - 0s 1ms/step - loss: 0.0281
    722. 

  722. Epoch 5/10
    723. 

  723. 
  1/133 [..............................] - ETA: 0s - loss: 0.0061
 49/133 [==========>...................] - ETA: 0s - loss: 0.0248
 96/133 [====================>.........] - ETA: 0s - loss: 0.0262
133/133 [==============================] - 0s 1ms/step - loss: 0.0263
    724. 

  724. Epoch 6/10
    725. 

  725. 
  1/133 [..............................] - ETA: 0s - loss: 0.0150
 48/133 [=========>....................] - ETA: 0s - loss: 0.0223
 94/133 [====================>.........] - ETA: 0s - loss: 0.0227
133/133 [==============================] - 0s 1ms/step - loss: 0.0251
    726. 

  726. Epoch 7/10
    727. 

  727. 
  1/133 [..............................] - ETA: 0s - loss: 0.0148
 47/133 [=========>....................] - ETA: 0s - loss: 0.0264
 94/133 [====================>.........] - ETA: 0s - loss: 0.0272
133/133 [==============================] - 0s 1ms/step - loss: 0.0249
    728. 

  728. Epoch 8/10
    729. 

  729. 
  1/133 [..............................] - ETA: 0s - loss: 0.0062
 49/133 [==========>...................] - ETA: 0s - loss: 0.0241
 92/133 [===================>..........] - ETA: 0s - loss: 0.0195
133/133 [==============================] - 0s 1ms/step - loss: 0.0228
    730. 

  730. Epoch 9/10
    731. 

  731. 
  1/133 [..............................] - ETA: 0s - loss: 0.0074
 48/133 [=========>....................] - ETA: 0s - loss: 0.0183
 96/133 [====================>.........] - ETA: 0s - loss: 0.0212
133/133 [==============================] - 0s 1ms/step - loss: 0.0218
    732. 

  732. Epoch 10/10
    733. 

  733. 
  1/133 [..............................] - ETA: 0s - loss: 0.0118
 48/133 [=========>....................] - ETA: 0s - loss: 0.0251
 93/133 [===================>..........] - ETA: 0s - loss: 0.0231
133/133 [==============================] - 0s 1ms/step - loss: 0.0219
    734. 

  734. -> test with GAN.predict
    735. 

  735. GAN tn, fp: 330, 2
    736. 

  736. GAN fn, tp: 6, 8
    737. 

  737. GAN f1 score: 0.667
    738. 

  738. GAN cohens kappa score: 0.655
    739. 

  739. 

  740. -> test with 'LR'
    741. 

  741. LR tn, fp: 181, 151
    742. 

  742. LR fn, tp: 6, 8
    743. 

  743. LR f1 score: 0.092
    744. 

  744. LR cohens kappa score: 0.020
    745. 

  745. LR average precision score: 0.056
    746. 

  746. 

  747. -> test with 'RF'
    748. 

  748. RF tn, fp: 332, 0
    749. 

  749. RF fn, tp: 7, 7
    750. 

  750. RF f1 score: 0.667
    751. 

  751. RF cohens kappa score: 0.657
    752. 

  752. 

  753. -> test with 'GB'
    754. 

  754. GB tn, fp: 330, 2
    755. 

  755. GB fn, tp: 9, 5
    756. 

  756. GB f1 score: 0.476
    757. 

  757. GB cohens kappa score: 0.462
    758. 

  758. 

  759. -> test with 'KNN'
    760. 

  760. KNN tn, fp: 320, 12
    761. 

  761. KNN fn, tp: 2, 12
    762. 

  762. KNN f1 score: 0.632
    763. 

  763. KNN cohens kappa score: 0.612
    764. 

  764. 

  765. 

  766. ------ Step 3/5: Slice 4/5 -------
    767. 

  767. -> Reset the GAN
    768. 

  768. -> Train generator for synthetic samples
    769. 

  769. -> create 1272 synthetic samples
    770. 

  770. -> retrain GAN for predict
    771. 

  771. Epoch 1/10
    772. 

  772. 
  1/133 [..............................] - ETA: 22s - loss: 0.0091
 42/133 [========>.....................] - ETA: 0s - loss: 0.0370 
 88/133 [==================>...........] - ETA: 0s - loss: 0.0382
132/133 [============================>.] - ETA: 0s - loss: 0.0371
133/133 [==============================] - 0s 1ms/step - loss: 0.0371
    773. 

  773. Epoch 2/10
    774. 

  774. 
  1/133 [..............................] - ETA: 0s - loss: 0.0229
 46/133 [=========>....................] - ETA: 0s - loss: 0.0317
 91/133 [===================>..........] - ETA: 0s - loss: 0.0326
133/133 [==============================] - 0s 1ms/step - loss: 0.0355
    775. 

  775. Epoch 3/10
    776. 

  776. 
  1/133 [..............................] - ETA: 0s - loss: 0.0042
 45/133 [=========>....................] - ETA: 0s - loss: 0.0392
 90/133 [===================>..........] - ETA: 0s - loss: 0.0357
133/133 [==============================] - 0s 1ms/step - loss: 0.0349
    777. 

  777. Epoch 4/10
    778. 

  778. 
  1/133 [..............................] - ETA: 0s - loss: 0.0099
 44/133 [========>.....................] - ETA: 0s - loss: 0.0330
 86/133 [==================>...........] - ETA: 0s - loss: 0.0361
130/133 [============================>.] - ETA: 0s - loss: 0.0325
133/133 [==============================] - 0s 1ms/step - loss: 0.0321
    779. 

  779. Epoch 5/10
    780. 

  780. 
  1/133 [..............................] - ETA: 0s - loss: 0.0240
 46/133 [=========>....................] - ETA: 0s - loss: 0.0251
 86/133 [==================>...........] - ETA: 0s - loss: 0.0342
128/133 [===========================>..] - ETA: 0s - loss: 0.0306
133/133 [==============================] - 0s 1ms/step - loss: 0.0310
    781. 

  781. Epoch 6/10
    782. 

  782. 
  1/133 [..............................] - ETA: 0s - loss: 0.0156
 49/133 [==========>...................] - ETA: 0s - loss: 0.0281
 96/133 [====================>.........] - ETA: 0s - loss: 0.0296
133/133 [==============================] - 0s 1ms/step - loss: 0.0288
    783. 

  783. Epoch 7/10
    784. 

  784. 
  1/133 [..............................] - ETA: 0s - loss: 0.1046
 48/133 [=========>....................] - ETA: 0s - loss: 0.0259
 95/133 [====================>.........] - ETA: 0s - loss: 0.0276
133/133 [==============================] - 0s 1ms/step - loss: 0.0283
    785. 

  785. Epoch 8/10
    786. 

  786. 
  1/133 [..............................] - ETA: 0s - loss: 0.0432
 47/133 [=========>....................] - ETA: 0s - loss: 0.0358
 94/133 [====================>.........] - ETA: 0s - loss: 0.0284
133/133 [==============================] - 0s 1ms/step - loss: 0.0268
    787. 

  787. Epoch 9/10
    788. 

  788. 
  1/133 [..............................] - ETA: 0s - loss: 0.1162
 47/133 [=========>....................] - ETA: 0s - loss: 0.0238
 84/133 [=================>............] - ETA: 0s - loss: 0.0262
127/133 [===========================>..] - ETA: 0s - loss: 0.0257
133/133 [==============================] - 0s 1ms/step - loss: 0.0260
    789. 

  789. Epoch 10/10
    790. 

  790. 
  1/133 [..............................] - ETA: 0s - loss: 0.0018
 44/133 [========>.....................] - ETA: 0s - loss: 0.0266
 87/133 [==================>...........] - ETA: 0s - loss: 0.0254
127/133 [===========================>..] - ETA: 0s - loss: 0.0265
133/133 [==============================] - 0s 1ms/step - loss: 0.0258
    791. 

  791. -> test with GAN.predict
    792. 

  792. GAN tn, fp: 329, 3
    793. 

  793. GAN fn, tp: 3, 11
    794. 

  794. GAN f1 score: 0.786
    795. 

  795. GAN cohens kappa score: 0.777
    796. 

  796. 

  797. -> test with 'LR'
    798. 

  798. LR tn, fp: 184, 148
    799. 

  799. LR fn, tp: 2, 12
    800. 

  800. LR f1 score: 0.138
    801. 

  801. LR cohens kappa score: 0.069
    802. 

  802. LR average precision score: 0.081
    803. 

  803. 

  804. -> test with 'RF'
    805. 

  805. RF tn, fp: 331, 1
    806. 

  806. RF fn, tp: 6, 8
    807. 

  807. RF f1 score: 0.696
    808. 

  808. RF cohens kappa score: 0.686
    809. 

  809. 

  810. -> test with 'GB'
    811. 

  811. GB tn, fp: 332, 0
    812. 

  812. GB fn, tp: 7, 7
    813. 

  813. GB f1 score: 0.667
    814. 

  814. GB cohens kappa score: 0.657
    815. 

  815. 

  816. -> test with 'KNN'
    817. 

  817. KNN tn, fp: 316, 16
    818. 

  818. KNN fn, tp: 0, 14
    819. 

  819. KNN f1 score: 0.636
    820. 

  820. KNN cohens kappa score: 0.615
    821. 

  821. 

  822. 

  823. ------ Step 3/5: Slice 5/5 -------
    824. 

  824. -> Reset the GAN
    825. 

  825. -> Train generator for synthetic samples
    826. 

  826. -> create 1272 synthetic samples
    827. 

  827. -> retrain GAN for predict
    828. 

  828. Epoch 1/10
    829. 

  829. 
  1/133 [..............................] - ETA: 21s - loss: 0.0352
 46/133 [=========>....................] - ETA: 0s - loss: 0.0330 
 94/133 [====================>.........] - ETA: 0s - loss: 0.0329
133/133 [==============================] - 0s 1ms/step - loss: 0.0339
    830. 

  830. Epoch 2/10
    831. 

  831. 
  1/133 [..............................] - ETA: 0s - loss: 0.1184
 49/133 [==========>...................] - ETA: 0s - loss: 0.0308
 97/133 [====================>.........] - ETA: 0s - loss: 0.0314
133/133 [==============================] - 0s 1ms/step - loss: 0.0309
    832. 

  832. Epoch 3/10
    833. 

  833. 
  1/133 [..............................] - ETA: 0s - loss: 0.0211
 48/133 [=========>....................] - ETA: 0s - loss: 0.0245
 95/133 [====================>.........] - ETA: 0s - loss: 0.0285
133/133 [==============================] - 0s 1ms/step - loss: 0.0290
    834. 

  834. Epoch 4/10
    835. 

  835. 
  1/133 [..............................] - ETA: 0s - loss: 0.0103
 48/133 [=========>....................] - ETA: 0s - loss: 0.0325
 96/133 [====================>.........] - ETA: 0s - loss: 0.0291
133/133 [==============================] - 0s 1ms/step - loss: 0.0277
    836. 

  836. Epoch 5/10
    837. 

  837. 
  1/133 [..............................] - ETA: 0s - loss: 0.0568
 48/133 [=========>....................] - ETA: 0s - loss: 0.0374
 96/133 [====================>.........] - ETA: 0s - loss: 0.0283
133/133 [==============================] - 0s 1ms/step - loss: 0.0262
    838. 

  838. Epoch 6/10
    839. 

  839. 
  1/133 [..............................] - ETA: 0s - loss: 0.0072
 49/133 [==========>...................] - ETA: 0s - loss: 0.0263
 95/133 [====================>.........] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 1ms/step - loss: 0.0253
    840. 

  840. Epoch 7/10
    841. 

  841. 
  1/133 [..............................] - ETA: 0s - loss: 0.0024
 48/133 [=========>....................] - ETA: 0s - loss: 0.0232
 92/133 [===================>..........] - ETA: 0s - loss: 0.0236
131/133 [============================>.] - ETA: 0s - loss: 0.0246
133/133 [==============================] - 0s 1ms/step - loss: 0.0244
    842. 

  842. Epoch 8/10
    843. 

  843. 
  1/133 [..............................] - ETA: 0s - loss: 0.0371
 42/133 [========>.....................] - ETA: 0s - loss: 0.0327
 81/133 [=================>............] - ETA: 0s - loss: 0.0272
125/133 [===========================>..] - ETA: 0s - loss: 0.0223
133/133 [==============================] - 0s 1ms/step - loss: 0.0225
    844. 

  844. Epoch 9/10
    845. 

  845. 
  1/133 [..............................] - ETA: 0s - loss: 0.0094
 48/133 [=========>....................] - ETA: 0s - loss: 0.0173
 95/133 [====================>.........] - ETA: 0s - loss: 0.0205
133/133 [==============================] - 0s 1ms/step - loss: 0.0217
    846. 

  846. Epoch 10/10
    847. 

  847. 
  1/133 [..............................] - ETA: 0s - loss: 0.0088
 42/133 [========>.....................] - ETA: 0s - loss: 0.0240
 84/133 [=================>............] - ETA: 0s - loss: 0.0228
121/133 [==========================>...] - ETA: 0s - loss: 0.0210
133/133 [==============================] - 0s 1ms/step - loss: 0.0206
    848. 

  848. -> test with GAN.predict
    849. 

  849. GAN tn, fp: 326, 5
    850. 

  850. GAN fn, tp: 2, 11
    851. 

  851. GAN f1 score: 0.759
    852. 

  852. GAN cohens kappa score: 0.748
    853. 

  853. 

  854. -> test with 'LR'
    855. 

  855. LR tn, fp: 176, 155
    856. 

  856. LR fn, tp: 5, 8
    857. 

  857. LR f1 score: 0.091
    858. 

  858. LR cohens kappa score: 0.022
    859. 

  859. LR average precision score: 0.049
    860. 

  860. 

  861. -> test with 'RF'
    862. 

  862. RF tn, fp: 331, 0
    863. 

  863. RF fn, tp: 5, 8
    864. 

  864. RF f1 score: 0.762
    865. 

  865. RF cohens kappa score: 0.755
    866. 

  866. 

  867. -> test with 'GB'
    868. 

  868. GB tn, fp: 328, 3
    869. 

  869. GB fn, tp: 4, 9
    870. 

  870. GB f1 score: 0.720
    871. 

  871. GB cohens kappa score: 0.709
    872. 

  872. 

  873. -> test with 'KNN'
    874. 

  874. KNN tn, fp: 277, 54
    875. 

  875. KNN fn, tp: 1, 12
    876. 

  876. KNN f1 score: 0.304
    877. 

  877. KNN cohens kappa score: 0.257
    878. 

  878. 

  879. 

  880. ====== Step 4/5 =======
    881. 

  881. -> Shuffling data
    882. 

  882. -> Spliting data to slices
    883. 

  883. 

  884. ------ Step 4/5: Slice 1/5 -------
    885. 

  885. -> Reset the GAN
    886. 

  886. -> Train generator for synthetic samples
    887. 

  887. -> create 1272 synthetic samples
    888. 

  888. -> retrain GAN for predict
    889. 

  889. Epoch 1/10
    890. 

  890. 
  1/133 [..............................] - ETA: 19s - loss: 0.0297
 45/133 [=========>....................] - ETA: 0s - loss: 0.0415 
 90/133 [===================>..........] - ETA: 0s - loss: 0.0385
133/133 [==============================] - 0s 1ms/step - loss: 0.0360
    891. 

  891. Epoch 2/10
    892. 

  892. 
  1/133 [..............................] - ETA: 0s - loss: 0.0063
 48/133 [=========>....................] - ETA: 0s - loss: 0.0320
 96/133 [====================>.........] - ETA: 0s - loss: 0.0333
133/133 [==============================] - 0s 1ms/step - loss: 0.0321
    893. 

  893. Epoch 3/10
    894. 

  894. 
  1/133 [..............................] - ETA: 0s - loss: 0.0146
 49/133 [==========>...................] - ETA: 0s - loss: 0.0323
 97/133 [====================>.........] - ETA: 0s - loss: 0.0310
133/133 [==============================] - 0s 1ms/step - loss: 0.0312
    895. 

  895. Epoch 4/10
    896. 

  896. 
  1/133 [..............................] - ETA: 0s - loss: 0.0092
 49/133 [==========>...................] - ETA: 0s - loss: 0.0289
 96/133 [====================>.........] - ETA: 0s - loss: 0.0255
133/133 [==============================] - 0s 1ms/step - loss: 0.0300
    897. 

  897. Epoch 5/10
    898. 

  898. 
  1/133 [..............................] - ETA: 0s - loss: 0.0052
 49/133 [==========>...................] - ETA: 0s - loss: 0.0212
 95/133 [====================>.........] - ETA: 0s - loss: 0.0215
133/133 [==============================] - 0s 1ms/step - loss: 0.0285
    899. 

  899. Epoch 6/10
    900. 

  900. 
  1/133 [..............................] - ETA: 0s - loss: 0.0938
 49/133 [==========>...................] - ETA: 0s - loss: 0.0295
 97/133 [====================>.........] - ETA: 0s - loss: 0.0318
133/133 [==============================] - 0s 1ms/step - loss: 0.0282
    901. 

  901. Epoch 7/10
    902. 

  902. 
  1/133 [..............................] - ETA: 0s - loss: 0.0201
 49/133 [==========>...................] - ETA: 0s - loss: 0.0290
 98/133 [=====================>........] - ETA: 0s - loss: 0.0232
133/133 [==============================] - 0s 1ms/step - loss: 0.0259
    903. 

  903. Epoch 8/10
    904. 

  904. 
  1/133 [..............................] - ETA: 0s - loss: 0.0128
 50/133 [==========>...................] - ETA: 0s - loss: 0.0240
 98/133 [=====================>........] - ETA: 0s - loss: 0.0266
133/133 [==============================] - 0s 1ms/step - loss: 0.0258
    905. 

  905. Epoch 9/10
    906. 

  906. 
  1/133 [..............................] - ETA: 0s - loss: 0.0210
 49/133 [==========>...................] - ETA: 0s - loss: 0.0124
 98/133 [=====================>........] - ETA: 0s - loss: 0.0209
133/133 [==============================] - 0s 1ms/step - loss: 0.0231
    907. 

  907. Epoch 10/10
    908. 

  908. 
  1/133 [..............................] - ETA: 0s - loss: 0.0105
 49/133 [==========>...................] - ETA: 0s - loss: 0.0263
 95/133 [====================>.........] - ETA: 0s - loss: 0.0244
133/133 [==============================] - 0s 1ms/step - loss: 0.0229
    909. 

  909. -> test with GAN.predict
    910. 

  910. GAN tn, fp: 328, 4
    911. 

  911. GAN fn, tp: 2, 12
    912. 

  912. GAN f1 score: 0.800
    913. 

  913. GAN cohens kappa score: 0.791
    914. 

  914. 

  915. -> test with 'LR'
    916. 

  916. LR tn, fp: 182, 150
    917. 

  917. LR fn, tp: 3, 11
    918. 

  918. LR f1 score: 0.126
    919. 

  919. LR cohens kappa score: 0.055
    920. 

  920. LR average precision score: 0.067
    921. 

  921. 

  922. -> test with 'RF'
    923. 

  923. RF tn, fp: 332, 0
    924. 

  924. RF fn, tp: 5, 9
    925. 

  925. RF f1 score: 0.783
    926. 

  926. RF cohens kappa score: 0.775
    927. 

  927. 

  928. -> test with 'GB'
    929. 

  929. GB tn, fp: 332, 0
    930. 

  930. GB fn, tp: 5, 9
    931. 

  931. GB f1 score: 0.783
    932. 

  932. GB cohens kappa score: 0.775
    933. 

  933. 

  934. -> test with 'KNN'
    935. 

  935. KNN tn, fp: 328, 4
    936. 

  936. KNN fn, tp: 0, 14
    937. 

  937. KNN f1 score: 0.875
    938. 

  938. KNN cohens kappa score: 0.869
    939. 

  939. 

  940. 

  941. ------ Step 4/5: Slice 2/5 -------
    942. 

  942. -> Reset the GAN
    943. 

  943. -> Train generator for synthetic samples
    944. 

  944. -> create 1272 synthetic samples
    945. 

  945. -> retrain GAN for predict
    946. 

  946. Epoch 1/10
    947. 

  947. 
  1/133 [..............................] - ETA: 21s - loss: 0.0133
 49/133 [==========>...................] - ETA: 0s - loss: 0.0277 
 98/133 [=====================>........] - ETA: 0s - loss: 0.0297
133/133 [==============================] - 0s 1ms/step - loss: 0.0300
    948. 

  948. Epoch 2/10
    949. 

  949. 
  1/133 [..............................] - ETA: 0s - loss: 0.0172
 49/133 [==========>...................] - ETA: 0s - loss: 0.0379
 96/133 [====================>.........] - ETA: 0s - loss: 0.0312
133/133 [==============================] - 0s 1ms/step - loss: 0.0279
    950. 

  950. Epoch 3/10
    951. 

  951. 
  1/133 [..............................] - ETA: 0s - loss: 0.0142
 41/133 [========>.....................] - ETA: 0s - loss: 0.0227
 88/133 [==================>...........] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 1ms/step - loss: 0.0260
    952. 

  952. Epoch 4/10
    953. 

  953. 
  1/133 [..............................] - ETA: 0s - loss: 0.0412
 48/133 [=========>....................] - ETA: 0s - loss: 0.0213
 96/133 [====================>.........] - ETA: 0s - loss: 0.0241
133/133 [==============================] - 0s 1ms/step - loss: 0.0251
    954. 

  954. Epoch 5/10
    955. 

  955. 
  1/133 [..............................] - ETA: 0s - loss: 0.0108
 49/133 [==========>...................] - ETA: 0s - loss: 0.0199
 97/133 [====================>.........] - ETA: 0s - loss: 0.0246
133/133 [==============================] - 0s 1ms/step - loss: 0.0240
    956. 

  956. Epoch 6/10
    957. 

  957. 
  1/133 [..............................] - ETA: 0s - loss: 0.0056
 50/133 [==========>...................] - ETA: 0s - loss: 0.0243
 97/133 [====================>.........] - ETA: 0s - loss: 0.0225
133/133 [==============================] - 0s 1ms/step - loss: 0.0227
    958. 

  958. Epoch 7/10
    959. 

  959. 
  1/133 [..............................] - ETA: 0s - loss: 0.0031
 49/133 [==========>...................] - ETA: 0s - loss: 0.0158
 97/133 [====================>.........] - ETA: 0s - loss: 0.0223
133/133 [==============================] - 0s 1ms/step - loss: 0.0214
    960. 

  960. Epoch 8/10
    961. 

  961. 
  1/133 [..............................] - ETA: 0s - loss: 0.0054
 49/133 [==========>...................] - ETA: 0s - loss: 0.0160
 97/133 [====================>.........] - ETA: 0s - loss: 0.0183
133/133 [==============================] - 0s 1ms/step - loss: 0.0204
    962. 

  962. Epoch 9/10
    963. 

  963. 
  1/133 [..............................] - ETA: 0s - loss: 0.0063
 49/133 [==========>...................] - ETA: 0s - loss: 0.0235
 94/133 [====================>.........] - ETA: 0s - loss: 0.0188
133/133 [==============================] - 0s 1ms/step - loss: 0.0192
    964. 

  964. Epoch 10/10
    965. 

  965. 
  1/133 [..............................] - ETA: 0s - loss: 0.0190
 49/133 [==========>...................] - ETA: 0s - loss: 0.0222
 97/133 [====================>.........] - ETA: 0s - loss: 0.0188
133/133 [==============================] - 0s 1ms/step - loss: 0.0192
    966. 

  966. -> test with GAN.predict
    967. 

  967. GAN tn, fp: 327, 5
    968. 

  968. GAN fn, tp: 6, 8
    969. 

  969. GAN f1 score: 0.593
    970. 

  970. GAN cohens kappa score: 0.576
    971. 

  971. 

  972. -> test with 'LR'
    973. 

  973. LR tn, fp: 180, 152
    974. 

  974. LR fn, tp: 6, 8
    975. 

  975. LR f1 score: 0.092
    976. 

  976. LR cohens kappa score: 0.019
    977. 

  977. LR average precision score: 0.062
    978. 

  978. 

  979. -> test with 'RF'
    980. 

  980. RF tn, fp: 332, 0
    981. 

  981. RF fn, tp: 11, 3
    982. 

  982. RF f1 score: 0.353
    983. 

  983. RF cohens kappa score: 0.344
    984. 

  984. 

  985. -> test with 'GB'
    986. 

  986. GB tn, fp: 330, 2
    987. 

  987. GB fn, tp: 7, 7
    988. 

  988. GB f1 score: 0.609
    989. 

  989. GB cohens kappa score: 0.596
    990. 

  990. 

  991. -> test with 'KNN'
    992. 

  992. KNN tn, fp: 312, 20
    993. 

  993. KNN fn, tp: 0, 14
    994. 

  994. KNN f1 score: 0.583
    995. 

  995. KNN cohens kappa score: 0.558
    996. 

  996. 

  997. 

  998. ------ Step 4/5: Slice 3/5 -------
    999. 

  999. -> Reset the GAN
    1000. 

  1000. -> Train generator for synthetic samples
    1001. 

  1001. -> create 1272 synthetic samples
    1002. 

  1002. -> retrain GAN for predict
    1003. 

  1003. Epoch 1/10
    1004. 

  1004. 
  1/133 [..............................] - ETA: 18s - loss: 0.0115
 44/133 [========>.....................] - ETA: 0s - loss: 0.0420 
 88/133 [==================>...........] - ETA: 0s - loss: 0.0376
133/133 [==============================] - 0s 1ms/step - loss: 0.0367
    1005. 

  1005. Epoch 2/10
    1006. 

  1006. 
  1/133 [..............................] - ETA: 0s - loss: 0.0138
 49/133 [==========>...................] - ETA: 0s - loss: 0.0417
 97/133 [====================>.........] - ETA: 0s - loss: 0.0365
133/133 [==============================] - 0s 1ms/step - loss: 0.0338
    1007. 

  1007. Epoch 3/10
    1008. 

  1008. 
  1/133 [..............................] - ETA: 0s - loss: 0.0239
 49/133 [==========>...................] - ETA: 0s - loss: 0.0286
 96/133 [====================>.........] - ETA: 0s - loss: 0.0327
133/133 [==============================] - 0s 1ms/step - loss: 0.0320
    1009. 

  1009. Epoch 4/10
    1010. 

  1010. 
  1/133 [..............................] - ETA: 0s - loss: 0.0054
 49/133 [==========>...................] - ETA: 0s - loss: 0.0222
 97/133 [====================>.........] - ETA: 0s - loss: 0.0301
133/133 [==============================] - 0s 1ms/step - loss: 0.0310
    1011. 

  1011. Epoch 5/10
    1012. 

  1012. 
  1/133 [..............................] - ETA: 0s - loss: 0.0213
 49/133 [==========>...................] - ETA: 0s - loss: 0.0362
 97/133 [====================>.........] - ETA: 0s - loss: 0.0312
133/133 [==============================] - 0s 1ms/step - loss: 0.0291
    1013. 

  1013. Epoch 6/10
    1014. 

  1014. 
  1/133 [..............................] - ETA: 0s - loss: 0.0170
 46/133 [=========>....................] - ETA: 0s - loss: 0.0348
 93/133 [===================>..........] - ETA: 0s - loss: 0.0277
133/133 [==============================] - 0s 1ms/step - loss: 0.0285
    1015. 

  1015. Epoch 7/10
    1016. 

  1016. 
  1/133 [..............................] - ETA: 0s - loss: 0.0073
 50/133 [==========>...................] - ETA: 0s - loss: 0.0262
 98/133 [=====================>........] - ETA: 0s - loss: 0.0248
133/133 [==============================] - 0s 1ms/step - loss: 0.0269
    1017. 

  1017. Epoch 8/10
    1018. 

  1018. 
  1/133 [..............................] - ETA: 0s - loss: 0.0049
 46/133 [=========>....................] - ETA: 0s - loss: 0.0256
 86/133 [==================>...........] - ETA: 0s - loss: 0.0260
132/133 [============================>.] - ETA: 0s - loss: 0.0263
133/133 [==============================] - 0s 1ms/step - loss: 0.0261
    1019. 

  1019. Epoch 9/10
    1020. 

  1020. 
  1/133 [..............................] - ETA: 0s - loss: 0.0105
 49/133 [==========>...................] - ETA: 0s - loss: 0.0229
 97/133 [====================>.........] - ETA: 0s - loss: 0.0202
133/133 [==============================] - 0s 1ms/step - loss: 0.0252
    1021. 

  1021. Epoch 10/10
    1022. 

  1022. 
  1/133 [..............................] - ETA: 0s - loss: 0.1812
 49/133 [==========>...................] - ETA: 0s - loss: 0.0257
 97/133 [====================>.........] - ETA: 0s - loss: 0.0215
133/133 [==============================] - 0s 1ms/step - loss: 0.0237
    1023. 

  1023. -> test with GAN.predict
    1024. 

  1024. GAN tn, fp: 324, 8
    1025. 

  1025. GAN fn, tp: 2, 12
    1026. 

  1026. GAN f1 score: 0.706
    1027. 

  1027. GAN cohens kappa score: 0.691
    1028. 

  1028. 

  1029. -> test with 'LR'
    1030. 

  1030. LR tn, fp: 173, 159
    1031. 

  1031. LR fn, tp: 4, 10
    1032. 

  1032. LR f1 score: 0.109
    1033. 

  1033. LR cohens kappa score: 0.037
    1034. 

  1034. LR average precision score: 0.070
    1035. 

  1035. 

  1036. -> test with 'RF'
    1037. 

  1037. RF tn, fp: 331, 1
    1038. 

  1038. RF fn, tp: 8, 6
    1039. 

  1039. RF f1 score: 0.571
    1040. 

  1040. RF cohens kappa score: 0.560
    1041. 

  1041. 

  1042. -> test with 'GB'
    1043. 

  1043. GB tn, fp: 330, 2
    1044. 

  1044. GB fn, tp: 5, 9
    1045. 

  1045. GB f1 score: 0.720
    1046. 

  1046. GB cohens kappa score: 0.710
    1047. 

  1047. 

  1048. -> test with 'KNN'
    1049. 

  1049. KNN tn, fp: 302, 30
    1050. 

  1050. KNN fn, tp: 0, 14
    1051. 

  1051. KNN f1 score: 0.483
    1052. 

  1052. KNN cohens kappa score: 0.449
    1053. 

  1053. 

  1054. 

  1055. ------ Step 4/5: Slice 4/5 -------
    1056. 

  1056. -> Reset the GAN
    1057. 

  1057. -> Train generator for synthetic samples
    1058. 

  1058. -> create 1272 synthetic samples
    1059. 

  1059. -> retrain GAN for predict
    1060. 

  1060. Epoch 1/10
    1061. 

  1061. 
  1/133 [..............................] - ETA: 23s - loss: 0.0706
 49/133 [==========>...................] - ETA: 0s - loss: 0.0431 
 97/133 [====================>.........] - ETA: 0s - loss: 0.0384
133/133 [==============================] - 0s 1ms/step - loss: 0.0381
    1062. 

  1062. Epoch 2/10
    1063. 

  1063. 
  1/133 [..............................] - ETA: 0s - loss: 0.0033
 49/133 [==========>...................] - ETA: 0s - loss: 0.0388
 90/133 [===================>..........] - ETA: 0s - loss: 0.0359
133/133 [==============================] - 0s 1ms/step - loss: 0.0354
    1064. 

  1064. Epoch 3/10
    1065. 

  1065. 
  1/133 [..............................] - ETA: 0s - loss: 0.0849
 49/133 [==========>...................] - ETA: 0s - loss: 0.0380
 97/133 [====================>.........] - ETA: 0s - loss: 0.0356
133/133 [==============================] - 0s 1ms/step - loss: 0.0340
    1066. 

  1066. Epoch 4/10
    1067. 

  1067. 
  1/133 [..............................] - ETA: 0s - loss: 0.0179
 49/133 [==========>...................] - ETA: 0s - loss: 0.0272
 97/133 [====================>.........] - ETA: 0s - loss: 0.0330
133/133 [==============================] - 0s 1ms/step - loss: 0.0323
    1068. 

  1068. Epoch 5/10
    1069. 

  1069. 
  1/133 [..............................] - ETA: 0s - loss: 0.0186
 49/133 [==========>...................] - ETA: 0s - loss: 0.0238
 97/133 [====================>.........] - ETA: 0s - loss: 0.0276
133/133 [==============================] - 0s 1ms/step - loss: 0.0301
    1070. 

  1070. Epoch 6/10
    1071. 

  1071. 
  1/133 [..............................] - ETA: 0s - loss: 0.0063
 49/133 [==========>...................] - ETA: 0s - loss: 0.0300
 97/133 [====================>.........] - ETA: 0s - loss: 0.0270
133/133 [==============================] - 0s 1ms/step - loss: 0.0283
    1072. 

  1072. Epoch 7/10
    1073. 

  1073. 
  1/133 [..............................] - ETA: 0s - loss: 0.0083
 49/133 [==========>...................] - ETA: 0s - loss: 0.0284
 97/133 [====================>.........] - ETA: 0s - loss: 0.0273
133/133 [==============================] - 0s 1ms/step - loss: 0.0272
    1074. 

  1074. Epoch 8/10
    1075. 

  1075. 
  1/133 [..............................] - ETA: 0s - loss: 0.0046
 48/133 [=========>....................] - ETA: 0s - loss: 0.0231
 96/133 [====================>.........] - ETA: 0s - loss: 0.0257
133/133 [==============================] - 0s 1ms/step - loss: 0.0260
    1076. 

  1076. Epoch 9/10
    1077. 

  1077. 
  1/133 [..............................] - ETA: 0s - loss: 0.0031
 49/133 [==========>...................] - ETA: 0s - loss: 0.0259
 97/133 [====================>.........] - ETA: 0s - loss: 0.0255
133/133 [==============================] - 0s 1ms/step - loss: 0.0250
    1078. 

  1078. Epoch 10/10
    1079. 

  1079. 
  1/133 [..............................] - ETA: 0s - loss: 0.0094
 49/133 [==========>...................] - ETA: 0s - loss: 0.0235
 97/133 [====================>.........] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 1ms/step - loss: 0.0236
    1080. 

  1080. -> test with GAN.predict
    1081. 

  1081. GAN tn, fp: 330, 2
    1082. 

  1082. GAN fn, tp: 5, 9
    1083. 

  1083. GAN f1 score: 0.720
    1084. 

  1084. GAN cohens kappa score: 0.710
    1085. 

  1085. 

  1086. -> test with 'LR'
    1087. 

  1087. LR tn, fp: 194, 138
    1088. 

  1088. LR fn, tp: 6, 8
    1089. 

  1089. LR f1 score: 0.100
    1090. 

  1090. LR cohens kappa score: 0.028
    1091. 

  1091. LR average precision score: 0.055
    1092. 

  1092. 

  1093. -> test with 'RF'
    1094. 

  1094. RF tn, fp: 332, 0
    1095. 

  1095. RF fn, tp: 7, 7
    1096. 

  1096. RF f1 score: 0.667
    1097. 

  1097. RF cohens kappa score: 0.657
    1098. 

  1098. 

  1099. -> test with 'GB'
    1100. 

  1100. GB tn, fp: 330, 2
    1101. 

  1101. GB fn, tp: 4, 10
    1102. 

  1102. GB f1 score: 0.769
    1103. 

  1103. GB cohens kappa score: 0.760
    1104. 

  1104. 

  1105. -> test with 'KNN'
    1106. 

  1106. KNN tn, fp: 307, 25
    1107. 

  1107. KNN fn, tp: 0, 14
    1108. 

  1108. KNN f1 score: 0.528
    1109. 

  1109. KNN cohens kappa score: 0.498
    1110. 

  1110. 

  1111. 

  1112. ------ Step 4/5: Slice 5/5 -------
    1113. 

  1113. -> Reset the GAN
    1114. 

  1114. -> Train generator for synthetic samples
    1115. 

  1115. -> create 1272 synthetic samples
    1116. 

  1116. -> retrain GAN for predict
    1117. 

  1117. Epoch 1/10
    1118. 

  1118. 
  1/133 [..............................] - ETA: 20s - loss: 0.0049
 48/133 [=========>....................] - ETA: 0s - loss: 0.0385 
 96/133 [====================>.........] - ETA: 0s - loss: 0.0315
133/133 [==============================] - 0s 1ms/step - loss: 0.0289
    1119. 

  1119. Epoch 2/10
    1120. 

  1120. 
  1/133 [..............................] - ETA: 0s - loss: 0.0086
 46/133 [=========>....................] - ETA: 0s - loss: 0.0265
 87/133 [==================>...........] - ETA: 0s - loss: 0.0236
129/133 [============================>.] - ETA: 0s - loss: 0.0268
133/133 [==============================] - 0s 1ms/step - loss: 0.0267
    1121. 

  1121. Epoch 3/10
    1122. 

  1122. 
  1/133 [..............................] - ETA: 0s - loss: 0.0541
 45/133 [=========>....................] - ETA: 0s - loss: 0.0271
 93/133 [===================>..........] - ETA: 0s - loss: 0.0243
133/133 [==============================] - 0s 1ms/step - loss: 0.0255
    1123. 

  1123. Epoch 4/10
    1124. 

  1124. 
  1/133 [..............................] - ETA: 0s - loss: 0.1640
 49/133 [==========>...................] - ETA: 0s - loss: 0.0210
 96/133 [====================>.........] - ETA: 0s - loss: 0.0228
133/133 [==============================] - 0s 1ms/step - loss: 0.0243
    1125. 

  1125. Epoch 5/10
    1126. 

  1126. 
  1/133 [..............................] - ETA: 0s - loss: 0.0098
 42/133 [========>.....................] - ETA: 0s - loss: 0.0274
 84/133 [=================>............] - ETA: 0s - loss: 0.0213
126/133 [===========================>..] - ETA: 0s - loss: 0.0224
133/133 [==============================] - 0s 1ms/step - loss: 0.0231
    1127. 

  1127. Epoch 6/10
    1128. 

  1128. 
  1/133 [..............................] - ETA: 0s - loss: 0.0249
 49/133 [==========>...................] - ETA: 0s - loss: 0.0182
 97/133 [====================>.........] - ETA: 0s - loss: 0.0209
133/133 [==============================] - 0s 1ms/step - loss: 0.0220
    1129. 

  1129. Epoch 7/10
    1130. 

  1130. 
  1/133 [..............................] - ETA: 0s - loss: 0.0235
 49/133 [==========>...................] - ETA: 0s - loss: 0.0179
 97/133 [====================>.........] - ETA: 0s - loss: 0.0207
133/133 [==============================] - 0s 1ms/step - loss: 0.0215
    1131. 

  1131. Epoch 8/10
    1132. 

  1132. 
  1/133 [..............................] - ETA: 0s - loss: 0.0079
 49/133 [==========>...................] - ETA: 0s - loss: 0.0138
 97/133 [====================>.........] - ETA: 0s - loss: 0.0153
133/133 [==============================] - 0s 1ms/step - loss: 0.0203
    1133. 

  1133. Epoch 9/10
    1134. 

  1134. 
  1/133 [..............................] - ETA: 0s - loss: 0.0047
 47/133 [=========>....................] - ETA: 0s - loss: 0.0190
 95/133 [====================>.........] - ETA: 0s - loss: 0.0227
133/133 [==============================] - 0s 1ms/step - loss: 0.0193
    1135. 

  1135. Epoch 10/10
    1136. 

  1136. 
  1/133 [..............................] - ETA: 0s - loss: 0.0264
 49/133 [==========>...................] - ETA: 0s - loss: 0.0173
 96/133 [====================>.........] - ETA: 0s - loss: 0.0173
133/133 [==============================] - 0s 1ms/step - loss: 0.0195
    1137. 

  1137. -> test with GAN.predict
    1138. 

  1138. GAN tn, fp: 327, 4
    1139. 

  1139. GAN fn, tp: 3, 10
    1140. 

  1140. GAN f1 score: 0.741
    1141. 

  1141. GAN cohens kappa score: 0.730
    1142. 

  1142. 

  1143. -> test with 'LR'
    1144. 

  1144. LR tn, fp: 180, 151
    1145. 

  1145. LR fn, tp: 2, 11
    1146. 

  1146. LR f1 score: 0.126
    1147. 

  1147. LR cohens kappa score: 0.060
    1148. 

  1148. LR average precision score: 0.080
    1149. 

  1149. 

  1150. -> test with 'RF'
    1151. 

  1151. RF tn, fp: 330, 1
    1152. 

  1152. RF fn, tp: 8, 5
    1153. 

  1153. RF f1 score: 0.526
    1154. 

  1154. RF cohens kappa score: 0.515
    1155. 

  1155. 

  1156. -> test with 'GB'
    1157. 

  1157. GB tn, fp: 329, 2
    1158. 

  1158. GB fn, tp: 8, 5
    1159. 

  1159. GB f1 score: 0.500
    1160. 

  1160. GB cohens kappa score: 0.486
    1161. 

  1161. 

  1162. -> test with 'KNN'
    1163. 

  1163. KNN tn, fp: 304, 27
    1164. 

  1164. KNN fn, tp: 1, 12
    1165. 

  1165. KNN f1 score: 0.462
    1166. 

  1166. KNN cohens kappa score: 0.429
    1167. 

  1167. 

  1168. 

  1169. ====== Step 5/5 =======
    1170. 

  1170. -> Shuffling data
    1171. 

  1171. -> Spliting data to slices
    1172. 

  1172. 

  1173. ------ Step 5/5: Slice 1/5 -------
    1174. 

  1174. -> Reset the GAN
    1175. 

  1175. -> Train generator for synthetic samples
    1176. 

  1176. -> create 1272 synthetic samples
    1177. 

  1177. -> retrain GAN for predict
    1178. 

  1178. Epoch 1/10
    1179. 

  1179. 
  1/133 [..............................] - ETA: 18s - loss: 0.0055
 49/133 [==========>...................] - ETA: 0s - loss: 0.0356 
 98/133 [=====================>........] - ETA: 0s - loss: 0.0377
133/133 [==============================] - 0s 1ms/step - loss: 0.0374
    1180. 

  1180. Epoch 2/10
    1181. 

  1181. 
  1/133 [..............................] - ETA: 0s - loss: 0.0258
 49/133 [==========>...................] - ETA: 0s - loss: 0.0253
 98/133 [=====================>........] - ETA: 0s - loss: 0.0345
133/133 [==============================] - 0s 1ms/step - loss: 0.0355
    1182. 

  1182. Epoch 3/10
    1183. 

  1183. 
  1/133 [..............................] - ETA: 0s - loss: 0.0591
 50/133 [==========>...................] - ETA: 0s - loss: 0.0265
 99/133 [=====================>........] - ETA: 0s - loss: 0.0325
133/133 [==============================] - 0s 1ms/step - loss: 0.0346
    1184. 

  1184. Epoch 4/10
    1185. 

  1185. 
  1/133 [..............................] - ETA: 0s - loss: 0.0127
 49/133 [==========>...................] - ETA: 0s - loss: 0.0299
 96/133 [====================>.........] - ETA: 0s - loss: 0.0298
133/133 [==============================] - 0s 1ms/step - loss: 0.0331
    1186. 

  1186. Epoch 5/10
    1187. 

  1187. 
  1/133 [..............................] - ETA: 0s - loss: 0.0130
 50/133 [==========>...................] - ETA: 0s - loss: 0.0299
 99/133 [=====================>........] - ETA: 0s - loss: 0.0339
133/133 [==============================] - 0s 1ms/step - loss: 0.0315
    1188. 

  1188. Epoch 6/10
    1189. 

  1189. 
  1/133 [..............................] - ETA: 0s - loss: 0.0135
 50/133 [==========>...................] - ETA: 0s - loss: 0.0310
 97/133 [====================>.........] - ETA: 0s - loss: 0.0301
133/133 [==============================] - 0s 1ms/step - loss: 0.0301
    1190. 

  1190. Epoch 7/10
    1191. 

  1191. 
  1/133 [..............................] - ETA: 0s - loss: 0.0123
 49/133 [==========>...................] - ETA: 0s - loss: 0.0259
 97/133 [====================>.........] - ETA: 0s - loss: 0.0287
133/133 [==============================] - 0s 1ms/step - loss: 0.0284
    1192. 

  1192. Epoch 8/10
    1193. 

  1193. 
  1/133 [..............................] - ETA: 0s - loss: 0.0133
 49/133 [==========>...................] - ETA: 0s - loss: 0.0287
 98/133 [=====================>........] - ETA: 0s - loss: 0.0266
133/133 [==============================] - 0s 1ms/step - loss: 0.0273
    1194. 

  1194. Epoch 9/10
    1195. 

  1195. 
  1/133 [..............................] - ETA: 0s - loss: 0.0272
 49/133 [==========>...................] - ETA: 0s - loss: 0.0191
 97/133 [====================>.........] - ETA: 0s - loss: 0.0195
133/133 [==============================] - 0s 1ms/step - loss: 0.0247
    1196. 

  1196. Epoch 10/10
    1197. 

  1197. 
  1/133 [..............................] - ETA: 0s - loss: 0.0228
 49/133 [==========>...................] - ETA: 0s - loss: 0.0192
 97/133 [====================>.........] - ETA: 0s - loss: 0.0263
133/133 [==============================] - 0s 1ms/step - loss: 0.0257
    1198. 

  1198. -> test with GAN.predict
    1199. 

  1199. GAN tn, fp: 328, 4
    1200. 

  1200. GAN fn, tp: 2, 12
    1201. 

  1201. GAN f1 score: 0.800
    1202. 

  1202. GAN cohens kappa score: 0.791
    1203. 

  1203. 

  1204. -> test with 'LR'
    1205. 

  1205. LR tn, fp: 183, 149
    1206. 

  1206. LR fn, tp: 8, 6
    1207. 

  1207. LR f1 score: 0.071
    1208. 

  1208. LR cohens kappa score: -0.003
    1209. 

  1209. LR average precision score: 0.050
    1210. 

  1210. 

  1211. -> test with 'RF'
    1212. 

  1212. RF tn, fp: 329, 3
    1213. 

  1213. RF fn, tp: 9, 5
    1214. 

  1214. RF f1 score: 0.455
    1215. 

  1215. RF cohens kappa score: 0.438
    1216. 

  1216. 

  1217. -> test with 'GB'
    1218. 

  1218. GB tn, fp: 328, 4
    1219. 

  1219. GB fn, tp: 6, 8
    1220. 

  1220. GB f1 score: 0.615
    1221. 

  1221. GB cohens kappa score: 0.600
    1222. 

  1222. 

  1223. -> test with 'KNN'
    1224. 

  1224. KNN tn, fp: 307, 25
    1225. 

  1225. KNN fn, tp: 2, 12
    1226. 

  1226. KNN f1 score: 0.471
    1227. 

  1227. KNN cohens kappa score: 0.438
    1228. 

  1228. 

  1229. 

  1230. ------ Step 5/5: Slice 2/5 -------
    1231. 

  1231. -> Reset the GAN
    1232. 

  1232. -> Train generator for synthetic samples
    1233. 

  1233. -> create 1272 synthetic samples
    1234. 

  1234. -> retrain GAN for predict
    1235. 

  1235. Epoch 1/10
    1236. 

  1236. 
  1/133 [..............................] - ETA: 19s - loss: 0.0276
 45/133 [=========>....................] - ETA: 0s - loss: 0.0300 
 89/133 [===================>..........] - ETA: 0s - loss: 0.0284
131/133 [============================>.] - ETA: 0s - loss: 0.0360
133/133 [==============================] - 0s 1ms/step - loss: 0.0359
    1237. 

  1237. Epoch 2/10
    1238. 

  1238. 
  1/133 [..............................] - ETA: 0s - loss: 0.0161
 45/133 [=========>....................] - ETA: 0s - loss: 0.0356
 89/133 [===================>..........] - ETA: 0s - loss: 0.0321
133/133 [==============================] - ETA: 0s - loss: 0.0328
133/133 [==============================] - 0s 1ms/step - loss: 0.0328
    1239. 

  1239. Epoch 3/10
    1240. 

  1240. 
  1/133 [..............................] - ETA: 0s - loss: 0.0134
 45/133 [=========>....................] - ETA: 0s - loss: 0.0205
 88/133 [==================>...........] - ETA: 0s - loss: 0.0309
130/133 [============================>.] - ETA: 0s - loss: 0.0305
133/133 [==============================] - 0s 1ms/step - loss: 0.0305
    1241. 

  1241. Epoch 4/10
    1242. 

  1242. 
  1/133 [..............................] - ETA: 0s - loss: 0.0084
 42/133 [========>.....................] - ETA: 0s - loss: 0.0351
 84/133 [=================>............] - ETA: 0s - loss: 0.0294
122/133 [==========================>...] - ETA: 0s - loss: 0.0293
133/133 [==============================] - 0s 1ms/step - loss: 0.0301
    1243. 

  1243. Epoch 5/10
    1244. 

  1244. 
  1/133 [..............................] - ETA: 0s - loss: 0.0198
 39/133 [=======>......................] - ETA: 0s - loss: 0.0275
 78/133 [================>.............] - ETA: 0s - loss: 0.0284
119/133 [=========================>....] - ETA: 0s - loss: 0.0273
133/133 [==============================] - 0s 1ms/step - loss: 0.0285
    1245. 

  1245. Epoch 6/10
    1246. 

  1246. 
  1/133 [..............................] - ETA: 0s - loss: 0.0100
 43/133 [========>.....................] - ETA: 0s - loss: 0.0303
 85/133 [==================>...........] - ETA: 0s - loss: 0.0292
129/133 [============================>.] - ETA: 0s - loss: 0.0269
133/133 [==============================] - 0s 1ms/step - loss: 0.0273
    1247. 

  1247. Epoch 7/10
    1248. 

  1248. 
  1/133 [..............................] - ETA: 0s - loss: 0.0036
 43/133 [========>.....................] - ETA: 0s - loss: 0.0329
 86/133 [==================>...........] - ETA: 0s - loss: 0.0295
128/133 [===========================>..] - ETA: 0s - loss: 0.0257
133/133 [==============================] - 0s 1ms/step - loss: 0.0264
    1249. 

  1249. Epoch 8/10
    1250. 

  1250. 
  1/133 [..............................] - ETA: 0s - loss: 0.0215
 43/133 [========>.....................] - ETA: 0s - loss: 0.0186
 86/133 [==================>...........] - ETA: 0s - loss: 0.0212
130/133 [============================>.] - ETA: 0s - loss: 0.0264
133/133 [==============================] - 0s 1ms/step - loss: 0.0262
    1251. 

  1251. Epoch 9/10
    1252. 

  1252. 
  1/133 [..............................] - ETA: 0s - loss: 0.0054
 43/133 [========>.....................] - ETA: 0s - loss: 0.0251
 87/133 [==================>...........] - ETA: 0s - loss: 0.0235
132/133 [============================>.] - ETA: 0s - loss: 0.0248
133/133 [==============================] - 0s 1ms/step - loss: 0.0247
    1253. 

  1253. Epoch 10/10
    1254. 

  1254. 
  1/133 [..............................] - ETA: 0s - loss: 0.0153
 44/133 [========>.....................] - ETA: 0s - loss: 0.0197
 83/133 [=================>............] - ETA: 0s - loss: 0.0235
128/133 [===========================>..] - ETA: 0s - loss: 0.0232
133/133 [==============================] - 0s 1ms/step - loss: 0.0234
    1255. 

  1255. -> test with GAN.predict
    1256. 

  1256. GAN tn, fp: 331, 1
    1257. 

  1257. GAN fn, tp: 3, 11
    1258. 

  1258. GAN f1 score: 0.846
    1259. 

  1259. GAN cohens kappa score: 0.840
    1260. 

  1260. 

  1261. -> test with 'LR'
    1262. 

  1262. LR tn, fp: 189, 143
    1263. 

  1263. LR fn, tp: 4, 10
    1264. 

  1264. LR f1 score: 0.120
    1265. 

  1265. LR cohens kappa score: 0.049
    1266. 

  1266. LR average precision score: 0.069
    1267. 

  1267. 

  1268. -> test with 'RF'
    1269. 

  1269. RF tn, fp: 332, 0
    1270. 

  1270. RF fn, tp: 6, 8
    1271. 

  1271. RF f1 score: 0.727
    1272. 

  1272. RF cohens kappa score: 0.719
    1273. 

  1273. 

  1274. -> test with 'GB'
    1275. 

  1275. GB tn, fp: 330, 2
    1276. 

  1276. GB fn, tp: 8, 6
    1277. 

  1277. GB f1 score: 0.545
    1278. 

  1278. GB cohens kappa score: 0.532
    1279. 

  1279. 

  1280. -> test with 'KNN'
    1281. 

  1281. KNN tn, fp: 322, 10
    1282. 

  1282. KNN fn, tp: 0, 14
    1283. 

  1283. KNN f1 score: 0.737
    1284. 

  1284. KNN cohens kappa score: 0.723
    1285. 

  1285. 

  1286. 

  1287. ------ Step 5/5: Slice 3/5 -------
    1288. 

  1288. -> Reset the GAN
    1289. 

  1289. -> Train generator for synthetic samples
    1290. 

  1290. -> create 1272 synthetic samples
    1291. 

  1291. -> retrain GAN for predict
    1292. 

  1292. Epoch 1/10
    1293. 

  1293. 
  1/133 [..............................] - ETA: 18s - loss: 0.0297
 48/133 [=========>....................] - ETA: 0s - loss: 0.0512 
 96/133 [====================>.........] - ETA: 0s - loss: 0.0463
133/133 [==============================] - 0s 1ms/step - loss: 0.0396
    1294. 

  1294. Epoch 2/10
    1295. 

  1295. 
  1/133 [..............................] - ETA: 0s - loss: 0.0275
 43/133 [========>.....................] - ETA: 0s - loss: 0.0421
 86/133 [==================>...........] - ETA: 0s - loss: 0.0377
131/133 [============================>.] - ETA: 0s - loss: 0.0365
133/133 [==============================] - 0s 1ms/step - loss: 0.0364
    1296. 

  1296. Epoch 3/10
    1297. 

  1297. 
  1/133 [..............................] - ETA: 0s - loss: 0.0076
 49/133 [==========>...................] - ETA: 0s - loss: 0.0273
 97/133 [====================>.........] - ETA: 0s - loss: 0.0313
133/133 [==============================] - 0s 1ms/step - loss: 0.0343
    1298. 

  1298. Epoch 4/10
    1299. 

  1299. 
  1/133 [..............................] - ETA: 0s - loss: 0.0331
 49/133 [==========>...................] - ETA: 0s - loss: 0.0343
 97/133 [====================>.........] - ETA: 0s - loss: 0.0344
133/133 [==============================] - 0s 1ms/step - loss: 0.0332
    1300. 

  1300. Epoch 5/10
    1301. 

  1301. 
  1/133 [..............................] - ETA: 0s - loss: 0.0662
 50/133 [==========>...................] - ETA: 0s - loss: 0.0374
 93/133 [===================>..........] - ETA: 0s - loss: 0.0341
133/133 [==============================] - 0s 1ms/step - loss: 0.0314
    1302. 

  1302. Epoch 6/10
    1303. 

  1303. 
  1/133 [..............................] - ETA: 0s - loss: 0.0052
 50/133 [==========>...................] - ETA: 0s - loss: 0.0284
 98/133 [=====================>........] - ETA: 0s - loss: 0.0325
133/133 [==============================] - 0s 1ms/step - loss: 0.0308
    1304. 

  1304. Epoch 7/10
    1305. 

  1305. 
  1/133 [..............................] - ETA: 0s - loss: 0.0226
 50/133 [==========>...................] - ETA: 0s - loss: 0.0324
 98/133 [=====================>........] - ETA: 0s - loss: 0.0262
133/133 [==============================] - 0s 1ms/step - loss: 0.0289
    1306. 

  1306. Epoch 8/10
    1307. 

  1307. 
  1/133 [..............................] - ETA: 0s - loss: 0.0206
 49/133 [==========>...................] - ETA: 0s - loss: 0.0317
 97/133 [====================>.........] - ETA: 0s - loss: 0.0296
133/133 [==============================] - 0s 1ms/step - loss: 0.0289
    1308. 

  1308. Epoch 9/10
    1309. 

  1309. 
  1/133 [..............................] - ETA: 0s - loss: 0.0561
 50/133 [==========>...................] - ETA: 0s - loss: 0.0221
 99/133 [=====================>........] - ETA: 0s - loss: 0.0277
133/133 [==============================] - 0s 1ms/step - loss: 0.0272
    1310. 

  1310. Epoch 10/10
    1311. 

  1311. 
  1/133 [..............................] - ETA: 0s - loss: 0.0114
 49/133 [==========>...................] - ETA: 0s - loss: 0.0235
 95/133 [====================>.........] - ETA: 0s - loss: 0.0250
133/133 [==============================] - 0s 1ms/step - loss: 0.0254
    1312. 

  1312. -> test with GAN.predict
    1313. 

  1313. GAN tn, fp: 326, 6
    1314. 

  1314. GAN fn, tp: 3, 11
    1315. 

  1315. GAN f1 score: 0.710
    1316. 

  1316. GAN cohens kappa score: 0.696
    1317. 

  1317. 

  1318. -> test with 'LR'
    1319. 

  1319. LR tn, fp: 164, 168
    1320. 

  1320. LR fn, tp: 3, 11
    1321. 

  1321. LR f1 score: 0.114
    1322. 

  1322. LR cohens kappa score: 0.042
    1323. 

  1323. LR average precision score: 0.073
    1324. 

  1324. 

  1325. -> test with 'RF'
    1326. 

  1326. RF tn, fp: 330, 2
    1327. 

  1327. RF fn, tp: 4, 10
    1328. 

  1328. RF f1 score: 0.769
    1329. 

  1329. RF cohens kappa score: 0.760
    1330. 

  1330. 

  1331. -> test with 'GB'
    1332. 

  1332. GB tn, fp: 329, 3
    1333. 

  1333. GB fn, tp: 5, 9
    1334. 

  1334. GB f1 score: 0.692
    1335. 

  1335. GB cohens kappa score: 0.680
    1336. 

  1336. 

  1337. -> test with 'KNN'
    1338. 

  1338. KNN tn, fp: 302, 30
    1339. 

  1339. KNN fn, tp: 1, 13
    1340. 

  1340. KNN f1 score: 0.456
    1341. 

  1341. KNN cohens kappa score: 0.421
    1342. 

  1342. 

  1343. 

  1344. ------ Step 5/5: Slice 4/5 -------
    1345. 

  1345. -> Reset the GAN
    1346. 

  1346. -> Train generator for synthetic samples
    1347. 

  1347. -> create 1272 synthetic samples
    1348. 

  1348. -> retrain GAN for predict
    1349. 

  1349. Epoch 1/10
    1350. 

  1350. 
  1/133 [..............................] - ETA: 18s - loss: 0.0167
 49/133 [==========>...................] - ETA: 0s - loss: 0.0449 
 98/133 [=====================>........] - ETA: 0s - loss: 0.0420
133/133 [==============================] - 0s 1ms/step - loss: 0.0402
    1351. 

  1351. Epoch 2/10
    1352. 

  1352. 
  1/133 [..............................] - ETA: 0s - loss: 0.0096
 50/133 [==========>...................] - ETA: 0s - loss: 0.0378
 99/133 [=====================>........] - ETA: 0s - loss: 0.0327
133/133 [==============================] - 0s 1ms/step - loss: 0.0357
    1353. 

  1353. Epoch 3/10
    1354. 

  1354. 
  1/133 [..............................] - ETA: 0s - loss: 0.0907
 50/133 [==========>...................] - ETA: 0s - loss: 0.0437
 99/133 [=====================>........] - ETA: 0s - loss: 0.0362
133/133 [==============================] - 0s 1ms/step - loss: 0.0354
    1355. 

  1355. Epoch 4/10
    1356. 

  1356. 
  1/133 [..............................] - ETA: 0s - loss: 0.0508
 49/133 [==========>...................] - ETA: 0s - loss: 0.0369
 98/133 [=====================>........] - ETA: 0s - loss: 0.0329
133/133 [==============================] - 0s 1ms/step - loss: 0.0339
    1357. 

  1357. Epoch 5/10
    1358. 

  1358. 
  1/133 [..............................] - ETA: 0s - loss: 0.0119
 50/133 [==========>...................] - ETA: 0s - loss: 0.0361
 96/133 [====================>.........] - ETA: 0s - loss: 0.0364
133/133 [==============================] - 0s 1ms/step - loss: 0.0324
    1359. 

  1359. Epoch 6/10
    1360. 

  1360. 
  1/133 [..............................] - ETA: 0s - loss: 0.0269
 50/133 [==========>...................] - ETA: 0s - loss: 0.0314
 99/133 [=====================>........] - ETA: 0s - loss: 0.0293
133/133 [==============================] - 0s 1ms/step - loss: 0.0313
    1361. 

  1361. Epoch 7/10
    1362. 

  1362. 
  1/133 [..............................] - ETA: 0s - loss: 0.0169
 49/133 [==========>...................] - ETA: 0s - loss: 0.0273
 97/133 [====================>.........] - ETA: 0s - loss: 0.0308
133/133 [==============================] - 0s 1ms/step - loss: 0.0301
    1363. 

  1363. Epoch 8/10
    1364. 

  1364. 
  1/133 [..............................] - ETA: 0s - loss: 0.0230
 49/133 [==========>...................] - ETA: 0s - loss: 0.0244
 97/133 [====================>.........] - ETA: 0s - loss: 0.0269
133/133 [==============================] - 0s 1ms/step - loss: 0.0283
    1365. 

  1365. Epoch 9/10
    1366. 

  1366. 
  1/133 [..............................] - ETA: 0s - loss: 0.0035
 49/133 [==========>...................] - ETA: 0s - loss: 0.0242
 98/133 [=====================>........] - ETA: 0s - loss: 0.0264
133/133 [==============================] - 0s 1ms/step - loss: 0.0274
    1367. 

  1367. Epoch 10/10
    1368. 

  1368. 
  1/133 [..............................] - ETA: 0s - loss: 0.0401
 49/133 [==========>...................] - ETA: 0s - loss: 0.0188
 95/133 [====================>.........] - ETA: 0s - loss: 0.0283
133/133 [==============================] - 0s 1ms/step - loss: 0.0268
    1369. 

  1369. -> test with GAN.predict
    1370. 

  1370. GAN tn, fp: 330, 2
    1371. 

  1371. GAN fn, tp: 5, 9
    1372. 

  1372. GAN f1 score: 0.720
    1373. 

  1373. GAN cohens kappa score: 0.710
    1374. 

  1374. 

  1375. -> test with 'LR'
    1376. 

  1376. LR tn, fp: 178, 154
    1377. 

  1377. LR fn, tp: 3, 11
    1378. 

  1378. LR f1 score: 0.123
    1379. 

  1379. LR cohens kappa score: 0.052
    1380. 

  1380. LR average precision score: 0.059
    1381. 

  1381. 

  1382. -> test with 'RF'
    1383. 

  1383. RF tn, fp: 332, 0
    1384. 

  1384. RF fn, tp: 8, 6
    1385. 

  1385. RF f1 score: 0.600
    1386. 

  1386. RF cohens kappa score: 0.590
    1387. 

  1387. 

  1388. -> test with 'GB'
    1389. 

  1389. GB tn, fp: 330, 2
    1390. 

  1390. GB fn, tp: 5, 9
    1391. 

  1391. GB f1 score: 0.720
    1392. 

  1392. GB cohens kappa score: 0.710
    1393. 

  1393. 

  1394. -> test with 'KNN'
    1395. 

  1395. KNN tn, fp: 308, 24
    1396. 

  1396. KNN fn, tp: 1, 13
    1397. 

  1397. KNN f1 score: 0.510
    1398. 

  1398. KNN cohens kappa score: 0.479
    1399. 

  1399. 

  1400. 

  1401. ------ Step 5/5: Slice 5/5 -------
    1402. 

  1402. -> Reset the GAN
    1403. 

  1403. -> Train generator for synthetic samples
    1404. 

  1404. -> create 1272 synthetic samples
    1405. 

  1405. -> retrain GAN for predict
    1406. 

  1406. Epoch 1/10
    1407. 

  1407. 
  1/133 [..............................] - ETA: 18s - loss: 0.0088
 44/133 [========>.....................] - ETA: 0s - loss: 0.0445 
 90/133 [===================>..........] - ETA: 0s - loss: 0.0396
133/133 [==============================] - 0s 1ms/step - loss: 0.0373
    1408. 

  1408. Epoch 2/10
    1409. 

  1409. 
  1/133 [..............................] - ETA: 0s - loss: 0.0197
 49/133 [==========>...................] - ETA: 0s - loss: 0.0382
 98/133 [=====================>........] - ETA: 0s - loss: 0.0366
133/133 [==============================] - 0s 1ms/step - loss: 0.0351
    1410. 

  1410. Epoch 3/10
    1411. 

  1411. 
  1/133 [..............................] - ETA: 0s - loss: 0.0228
 49/133 [==========>...................] - ETA: 0s - loss: 0.0361
 96/133 [====================>.........] - ETA: 0s - loss: 0.0358
133/133 [==============================] - 0s 1ms/step - loss: 0.0329
    1412. 

  1412. Epoch 4/10
    1413. 

  1413. 
  1/133 [..............................] - ETA: 0s - loss: 0.0190
 49/133 [==========>...................] - ETA: 0s - loss: 0.0358
 97/133 [====================>.........] - ETA: 0s - loss: 0.0358
133/133 [==============================] - 0s 1ms/step - loss: 0.0323
    1414. 

  1414. Epoch 5/10
    1415. 

  1415. 
  1/133 [..............................] - ETA: 0s - loss: 0.0993
 50/133 [==========>...................] - ETA: 0s - loss: 0.0340
 98/133 [=====================>........] - ETA: 0s - loss: 0.0344
133/133 [==============================] - 0s 1ms/step - loss: 0.0304
    1416. 

  1416. Epoch 6/10
    1417. 

  1417. 
  1/133 [..............................] - ETA: 0s - loss: 0.0459
 49/133 [==========>...................] - ETA: 0s - loss: 0.0268
 98/133 [=====================>........] - ETA: 0s - loss: 0.0280
133/133 [==============================] - 0s 1ms/step - loss: 0.0288
    1418. 

  1418. Epoch 7/10
    1419. 

  1419. 
  1/133 [..............................] - ETA: 0s - loss: 0.0157
 50/133 [==========>...................] - ETA: 0s - loss: 0.0238
 96/133 [====================>.........] - ETA: 0s - loss: 0.0276
133/133 [==============================] - 0s 1ms/step - loss: 0.0274
    1420. 

  1420. Epoch 8/10
    1421. 

  1421. 
  1/133 [..............................] - ETA: 0s - loss: 0.0053
 50/133 [==========>...................] - ETA: 0s - loss: 0.0309
 99/133 [=====================>........] - ETA: 0s - loss: 0.0266
133/133 [==============================] - 0s 1ms/step - loss: 0.0259
    1422. 

  1422. Epoch 9/10
    1423. 

  1423. 
  1/133 [..............................] - ETA: 0s - loss: 0.0596
 50/133 [==========>...................] - ETA: 0s - loss: 0.0296
 99/133 [=====================>........] - ETA: 0s - loss: 0.0278
133/133 [==============================] - 0s 1ms/step - loss: 0.0246
    1424. 

  1424. Epoch 10/10
    1425. 

  1425. 
  1/133 [..............................] - ETA: 0s - loss: 0.0408
 50/133 [==========>...................] - ETA: 0s - loss: 0.0212
 99/133 [=====================>........] - ETA: 0s - loss: 0.0230
133/133 [==============================] - 0s 1ms/step - loss: 0.0243
    1426. 

  1426. -> test with GAN.predict
    1427. 

  1427. GAN tn, fp: 325, 6
    1428. 

  1428. GAN fn, tp: 0, 13
    1429. 

  1429. GAN f1 score: 0.813
    1430. 

  1430. GAN cohens kappa score: 0.804
    1431. 

  1431. 

  1432. -> test with 'LR'
    1433. 

  1433. LR tn, fp: 179, 152
    1434. 

  1434. LR fn, tp: 4, 9
    1435. 

  1435. LR f1 score: 0.103
    1436. 

  1436. LR cohens kappa score: 0.036
    1437. 

  1437. LR average precision score: 0.063
    1438. 

  1438. 

  1439. -> test with 'RF'
    1440. 

  1440. RF tn, fp: 330, 1
    1441. 

  1441. RF fn, tp: 6, 7
    1442. 

  1442. RF f1 score: 0.667
    1443. 

  1443. RF cohens kappa score: 0.657
    1444. 

  1444. 

  1445. -> test with 'GB'
    1446. 

  1446. GB tn, fp: 331, 0
    1447. 

  1447. GB fn, tp: 3, 10
    1448. 

  1448. GB f1 score: 0.870
    1449. 

  1449. GB cohens kappa score: 0.865
    1450. 

  1450. 

  1451. -> test with 'KNN'
    1452. 

  1452. KNN tn, fp: 321, 10
    1453. 

  1453. KNN fn, tp: 0, 13
    1454. 

  1454. KNN f1 score: 0.722
    1455. 

  1455. KNN cohens kappa score: 0.708
    1456. 

  1456. 

  1457. ### Exercise is done.
    1458. 

  1458. 

  1459. -----[ LR ]-----
    1460. 

  1460. maximum:
    1461. 

  1461. LR tn, fp: 194, 169
    1462. 

  1462. LR fn, tp: 8, 12
    1463. 

  1463. LR f1 score: 0.138
    1464. 

  1464. LR cohens kappa score: 0.069
    1465. 

  1465. LR average precision score: 0.083
    1466. 

  1466. 

  1467. 

  1468. average:
    1469. 

  1469. LR tn, fp: 180.28, 151.52
    1470. 

  1470. LR fn, tp: 4.32, 9.48
    1471. 

  1471. LR f1 score: 0.108
    1472. 

  1472. LR cohens kappa score: 0.038
    1473. 

  1473. LR average precision score: 0.065
    1474. 

  1474. 

  1475. 

  1476. minimum:
    1477. 

  1477. LR tn, fp: 163, 138
    1478. 

  1478. LR fn, tp: 2, 6
    1479. 

  1479. LR f1 score: 0.071
    1480. 

  1480. LR cohens kappa score: -0.003
    1481. 

  1481. LR average precision score: 0.049
    1482. 

  1482. 

  1483. 

  1484. -----[ RF ]-----
    1485. 

  1485. maximum:
    1486. 

  1486. RF tn, fp: 332, 3
    1487. 

  1487. RF fn, tp: 11, 10
    1488. 

  1488. RF f1 score: 0.833
    1489. 

  1489. RF cohens kappa score: 0.828
    1490. 

  1490. 

  1491. 

  1492. average:
    1493. 

  1493. RF tn, fp: 331.12, 0.68
    1494. 

  1494. RF fn, tp: 6.92, 6.88
    1495. 

  1495. RF f1 score: 0.634
    1496. 

  1496. RF cohens kappa score: 0.624
    1497. 

  1497. 

  1498. 

  1499. minimum:
    1500. 

  1500. RF tn, fp: 329, 0
    1501. 

  1501. RF fn, tp: 4, 3
    1502. 

  1502. RF f1 score: 0.353
    1503. 

  1503. RF cohens kappa score: 0.344
    1504. 

  1504. 

  1505. 

  1506. -----[ GB ]-----
    1507. 

  1507. maximum:
    1508. 

  1508. GB tn, fp: 332, 4
    1509. 

  1509. GB fn, tp: 9, 14
    1510. 

  1510. GB f1 score: 0.933
    1511. 

  1511. GB cohens kappa score: 0.930
    1512. 

  1512. 

  1513. 

  1514. average:
    1515. 

  1515. GB tn, fp: 330.04, 1.76
    1516. 

  1516. GB fn, tp: 5.0, 8.8
    1517. 

  1517. GB f1 score: 0.714
    1518. 

  1518. GB cohens kappa score: 0.705
    1519. 

  1519. 

  1520. 

  1521. minimum:
    1522. 

  1522. GB tn, fp: 328, 0
    1523. 

  1523. GB fn, tp: 0, 5
    1524. 

  1524. GB f1 score: 0.476
    1525. 

  1525. GB cohens kappa score: 0.462
    1526. 

  1526. 

  1527. 

  1528. -----[ KNN ]-----
    1529. 

  1529. maximum:
    1530. 

  1530. KNN tn, fp: 328, 54
    1531. 

  1531. KNN fn, tp: 3, 14
    1532. 

  1532. KNN f1 score: 0.875
    1533. 

  1533. KNN cohens kappa score: 0.869
    1534. 

  1534. 

  1535. 

  1536. average:
    1537. 

  1537. KNN tn, fp: 310.44, 21.36
    1538. 

  1538. KNN fn, tp: 0.92, 12.88
    1539. 

  1539. KNN f1 score: 0.558
    1540. 

  1540. KNN cohens kappa score: 0.531
    1541. 

  1541. 

  1542. 

  1543. minimum:
    1544. 

  1544. KNN tn, fp: 277, 4
    1545. 

  1545. KNN fn, tp: 0, 11
    1546. 

  1546. KNN f1 score: 0.304
    1547. 

  1547. KNN cohens kappa score: 0.257
    1548. 

  1548. 

  1549. 

  1550. -----[ GAN ]-----
    1551. 

  1551. maximum:
    1552. 

  1552. GAN tn, fp: 331, 9
    1553. 

  1553. GAN fn, tp: 6, 14
    1554. 

  1554. GAN f1 score: 0.875
    1555. 

  1555. GAN cohens kappa score: 0.869
    1556. 

  1556. 

  1557. 

  1558. average:
    1559. 

  1559. GAN tn, fp: 326.96, 4.84
    1560. 

  1560. GAN fn, tp: 2.8, 11.0
    1561. 

  1561. GAN f1 score: 0.741
    1562. 

  1562. GAN cohens kappa score: 0.729
    1563. 

  1563. 

  1564. 

  1565. minimum:
    1566. 

  1566. GAN tn, fp: 323, 1
    1567. 

  1567. GAN fn, tp: 0, 8
    1568. 

  1568. GAN f1 score: 0.593
    1569. 

  1569. GAN cohens kappa score: 0.576
    1570. 

  1570.