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folding_kddcup-guess_passwd_vs_satan.log

folding_kddcup-guess_passwd_vs_satan.log 177 KB
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
  1. 

  2. 

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

  4. // Running convGAN-proximary-full on folding_kddcup-guess_passwd_vs_satan
    5. 

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

  6. 

  7. Load 'data_input/folding_kddcup-guess_passwd_vs_satan'
    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 1229 synthetic samples
    21. 

  21. -> retrain GAN for predict
    22. 

  22. Epoch 1/10
    23. 

  23. 
  1/128 [..............................] - ETA: 19s - loss: 7.0652e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.6435e-09 
 81/128 [=================>............] - ETA: 0s - loss: 1.2533e-09
121/128 [===========================>..] - ETA: 0s - loss: 1.2663e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.2313e-09
    24. 

  24. Epoch 2/10
    25. 

  25. 
  1/128 [..............................] - ETA: 0s - loss: 1.1774e-10
 38/128 [=======>......................] - ETA: 0s - loss: 7.0285e-10
 78/128 [=================>............] - ETA: 0s - loss: 7.2479e-10
115/128 [=========================>....] - ETA: 0s - loss: 7.9558e-10
128/128 [==============================] - 0s 1ms/step - loss: 7.4268e-10
    26. 

  26. Epoch 3/10
    27. 

  27. 
  1/128 [..............................] - ETA: 0s - loss: 2.1410e-10
 42/128 [========>.....................] - ETA: 0s - loss: 1.0717e-09
 82/128 [==================>...........] - ETA: 0s - loss: 6.7645e-10
122/128 [===========================>..] - ETA: 0s - loss: 5.6362e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.6138e-10
    28. 

  28. Epoch 4/10
    29. 

  29. 
  1/128 [..............................] - ETA: 0s - loss: 4.5857e-10
 41/128 [========>.....................] - ETA: 0s - loss: 6.7881e-10
 77/128 [=================>............] - ETA: 0s - loss: 5.3630e-10
114/128 [=========================>....] - ETA: 0s - loss: 5.1417e-10
128/128 [==============================] - 0s 1ms/step - loss: 4.7986e-10
    30. 

  30. Epoch 5/10
    31. 

  31. 
  1/128 [..............................] - ETA: 0s - loss: 2.9163e-10
 38/128 [=======>......................] - ETA: 0s - loss: 5.3152e-10
 79/128 [=================>............] - ETA: 0s - loss: 5.4913e-10
117/128 [==========================>...] - ETA: 0s - loss: 4.4493e-10
128/128 [==============================] - 0s 1ms/step - loss: 4.2106e-10
    32. 

  32. Epoch 6/10
    33. 

  33. 
  1/128 [..............................] - ETA: 0s - loss: 1.4203e-10
 41/128 [========>.....................] - ETA: 0s - loss: 3.6794e-10
 81/128 [=================>............] - ETA: 0s - loss: 4.3249e-10
119/128 [==========================>...] - ETA: 0s - loss: 3.7311e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.7901e-10
    34. 

  34. Epoch 7/10
    35. 

  35. 
  1/128 [..............................] - ETA: 0s - loss: 2.1066e-10
 41/128 [========>.....................] - ETA: 0s - loss: 2.0863e-10
 82/128 [==================>...........] - ETA: 0s - loss: 2.5179e-10
122/128 [===========================>..] - ETA: 0s - loss: 3.5137e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.5143e-10
    36. 

  36. Epoch 8/10
    37. 

  37. 
  1/128 [..............................] - ETA: 0s - loss: 9.8251e-11
 36/128 [=======>......................] - ETA: 0s - loss: 2.6981e-10
 76/128 [================>.............] - ETA: 0s - loss: 2.3972e-10
115/128 [=========================>....] - ETA: 0s - loss: 2.2029e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.2808e-10
    38. 

  38. Epoch 9/10
    39. 

  39. 
  1/128 [..............................] - ETA: 0s - loss: 6.6714e-11
 40/128 [========>.....................] - ETA: 0s - loss: 4.4518e-10
 80/128 [=================>............] - ETA: 0s - loss: 3.1828e-10
119/128 [==========================>...] - ETA: 0s - loss: 3.2178e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.1093e-10
    40. 

  40. Epoch 10/10
    41. 

  41. 
  1/128 [..............................] - ETA: 0s - loss: 6.7895e-11
 40/128 [========>.....................] - ETA: 0s - loss: 1.4793e-10
 79/128 [=================>............] - ETA: 0s - loss: 1.6612e-10
117/128 [==========================>...] - ETA: 0s - loss: 2.8204e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.9258e-10
    42. 

  42. -> test with GAN.predict
    43. 

  43. GAN tn, fp: 318, 0
    44. 

  44. GAN fn, tp: 0, 11
    45. 

  45. GAN f1 score: 1.000
    46. 

  46. GAN cohens kappa score: 1.000
    47. 

  47. 

  48. -> test with 'LR'
    49. 

  49. LR tn, fp: 318, 0
    50. 

  50. LR fn, tp: 0, 11
    51. 

  51. LR f1 score: 1.000
    52. 

  52. LR cohens kappa score: 1.000
    53. 

  53. LR average precision score: 1.000
    54. 

  54. 

  55. -> test with 'RF'
    56. 

  56. RF tn, fp: 318, 0
    57. 

  57. RF fn, tp: 0, 11
    58. 

  58. RF f1 score: 1.000
    59. 

  59. RF cohens kappa score: 1.000
    60. 

  60. 

  61. -> test with 'GB'
    62. 

  62. GB tn, fp: 318, 0
    63. 

  63. GB fn, tp: 0, 11
    64. 

  64. GB f1 score: 1.000
    65. 

  65. GB cohens kappa score: 1.000
    66. 

  66. 

  67. -> test with 'KNN'
    68. 

  68. KNN tn, fp: 318, 0
    69. 

  69. KNN fn, tp: 0, 11
    70. 

  70. KNN f1 score: 1.000
    71. 

  71. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    78. 

  78. -> retrain GAN for predict
    79. 

  79. Epoch 1/10
    80. 

  80. 
  1/128 [..............................] - ETA: 26s - loss: 1.1255e-10
 37/128 [=======>......................] - ETA: 0s - loss: 6.1950e-08 
 72/128 [===============>..............] - ETA: 0s - loss: 3.1966e-08
106/128 [=======================>......] - ETA: 0s - loss: 2.1760e-08
128/128 [==============================] - 0s 1ms/step - loss: 1.8181e-08
    81. 

  81. Epoch 2/10
    82. 

  82. 
  1/128 [..............................] - ETA: 0s - loss: 8.0143e-11
 39/128 [========>.....................] - ETA: 0s - loss: 1.5643e-09
 75/128 [================>.............] - ETA: 0s - loss: 8.8103e-10
115/128 [=========================>....] - ETA: 0s - loss: 6.4674e-10
128/128 [==============================] - 0s 1ms/step - loss: 6.1967e-10
    83. 

  83. Epoch 3/10
    84. 

  84. 
  1/128 [..............................] - ETA: 0s - loss: 5.7673e-11
 39/128 [========>.....................] - ETA: 0s - loss: 1.5731e-09
 73/128 [================>.............] - ETA: 0s - loss: 9.0072e-10
113/128 [=========================>....] - ETA: 0s - loss: 6.4246e-10
128/128 [==============================] - 0s 1ms/step - loss: 6.1193e-10
    85. 

  85. Epoch 4/10
    86. 

  86. 
  1/128 [..............................] - ETA: 0s - loss: 9.8568e-12
 35/128 [=======>......................] - ETA: 0s - loss: 2.7596e-10
 71/128 [===============>..............] - ETA: 0s - loss: 2.1110e-10
106/128 [=======================>......] - ETA: 0s - loss: 2.0782e-10
128/128 [==============================] - 0s 1ms/step - loss: 6.0445e-10
    87. 

  87. Epoch 5/10
    88. 

  88. 
  1/128 [..............................] - ETA: 0s - loss: 1.1186e-10
 38/128 [=======>......................] - ETA: 0s - loss: 1.4883e-10
 73/128 [================>.............] - ETA: 0s - loss: 1.9396e-10
108/128 [========================>.....] - ETA: 0s - loss: 6.6505e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.9715e-10
    89. 

  89. Epoch 6/10
    90. 

  90. 
  1/128 [..............................] - ETA: 0s - loss: 4.3534e-11
 40/128 [========>.....................] - ETA: 0s - loss: 2.1209e-10
 77/128 [=================>............] - ETA: 0s - loss: 1.5320e-10
112/128 [=========================>....] - ETA: 0s - loss: 6.4989e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.9024e-10
    91. 

  91. Epoch 7/10
    92. 

  92. 
  1/128 [..............................] - ETA: 0s - loss: 1.0161e-09
 36/128 [=======>......................] - ETA: 0s - loss: 1.6350e-09
 70/128 [===============>..............] - ETA: 0s - loss: 9.6737e-10
106/128 [=======================>......] - ETA: 0s - loss: 6.8127e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.8387e-10
    93. 

  93. Epoch 8/10
    94. 

  94. 
  1/128 [..............................] - ETA: 0s - loss: 2.5107e-10
 37/128 [=======>......................] - ETA: 0s - loss: 1.7496e-10
 74/128 [================>.............] - ETA: 0s - loss: 8.4623e-10
109/128 [========================>.....] - ETA: 0s - loss: 6.1641e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.7773e-10
    95. 

  95. Epoch 9/10
    96. 

  96. 
  1/128 [..............................] - ETA: 0s - loss: 1.7873e-10
 30/128 [======>.......................] - ETA: 0s - loss: 2.2560e-10
 59/128 [============>.................] - ETA: 0s - loss: 1.7597e-10
 98/128 [=====================>........] - ETA: 0s - loss: 1.5824e-10
128/128 [==============================] - 0s 2ms/step - loss: 5.7167e-10
    97. 

  97. Epoch 10/10
    98. 

  98. 
  1/128 [..............................] - ETA: 0s - loss: 2.4767e-10
 37/128 [=======>......................] - ETA: 0s - loss: 1.5199e-09
 73/128 [================>.............] - ETA: 0s - loss: 8.8163e-10
109/128 [========================>.....] - ETA: 0s - loss: 6.4546e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.6579e-10
    99. 

  99. -> test with GAN.predict
    100. 

  100. GAN tn, fp: 318, 0
    101. 

  101. GAN fn, tp: 0, 11
    102. 

  102. GAN f1 score: 1.000
    103. 

  103. GAN cohens kappa score: 1.000
    104. 

  104. 

  105. -> test with 'LR'
    106. 

  106. LR tn, fp: 318, 0
    107. 

  107. LR fn, tp: 0, 11
    108. 

  108. LR f1 score: 1.000
    109. 

  109. LR cohens kappa score: 1.000
    110. 

  110. LR average precision score: 1.000
    111. 

  111. 

  112. -> test with 'RF'
    113. 

  113. RF tn, fp: 318, 0
    114. 

  114. RF fn, tp: 0, 11
    115. 

  115. RF f1 score: 1.000
    116. 

  116. RF cohens kappa score: 1.000
    117. 

  117. 

  118. -> test with 'GB'
    119. 

  119. GB tn, fp: 318, 0
    120. 

  120. GB fn, tp: 0, 11
    121. 

  121. GB f1 score: 1.000
    122. 

  122. GB cohens kappa score: 1.000
    123. 

  123. 

  124. -> test with 'KNN'
    125. 

  125. KNN tn, fp: 318, 0
    126. 

  126. KNN fn, tp: 0, 11
    127. 

  127. KNN f1 score: 1.000
    128. 

  128. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    135. 

  135. -> retrain GAN for predict
    136. 

  136. Epoch 1/10
    137. 

  137. 
  1/128 [..............................] - ETA: 19s - loss: 1.8359e-09
 42/128 [========>.....................] - ETA: 0s - loss: 6.2573e-09 
 83/128 [==================>...........] - ETA: 0s - loss: 3.7198e-09
122/128 [===========================>..] - ETA: 0s - loss: 3.0157e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.9381e-09
    138. 

  138. Epoch 2/10
    139. 

  139. 
  1/128 [..............................] - ETA: 0s - loss: 1.0454e-09
 40/128 [========>.....................] - ETA: 0s - loss: 9.7892e-10
 80/128 [=================>............] - ETA: 0s - loss: 9.4472e-10
119/128 [==========================>...] - ETA: 0s - loss: 1.3160e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.2863e-09
    140. 

  140. Epoch 3/10
    141. 

  141. 
  1/128 [..............................] - ETA: 0s - loss: 8.4306e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.4209e-09
 81/128 [=================>............] - ETA: 0s - loss: 1.3349e-09
120/128 [===========================>..] - ETA: 0s - loss: 1.1849e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.1687e-09
    142. 

  142. Epoch 4/10
    143. 

  143. 
  1/128 [..............................] - ETA: 0s - loss: 6.8482e-10
 36/128 [=======>......................] - ETA: 0s - loss: 1.4192e-09
 72/128 [===============>..............] - ETA: 0s - loss: 1.1151e-09
108/128 [========================>.....] - ETA: 0s - loss: 1.0464e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.1172e-09
    144. 

  144. Epoch 5/10
    145. 

  145. 
  1/128 [..............................] - ETA: 0s - loss: 9.6030e-10
 40/128 [========>.....................] - ETA: 0s - loss: 7.8048e-10
 81/128 [=================>............] - ETA: 0s - loss: 1.2178e-09
122/128 [===========================>..] - ETA: 0s - loss: 1.0488e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.0360e-09
    146. 

  146. Epoch 6/10
    147. 

  147. 
  1/128 [..............................] - ETA: 0s - loss: 7.3791e-10
 40/128 [========>.....................] - ETA: 0s - loss: 1.2592e-09
 79/128 [=================>............] - ETA: 0s - loss: 1.0279e-09
119/128 [==========================>...] - ETA: 0s - loss: 1.0253e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.0014e-09
    148. 

  148. Epoch 7/10
    149. 

  149. 
  1/128 [..............................] - ETA: 0s - loss: 7.1867e-10
 43/128 [=========>....................] - ETA: 0s - loss: 7.4961e-10
 81/128 [=================>............] - ETA: 0s - loss: 7.2063e-10
122/128 [===========================>..] - ETA: 0s - loss: 7.2343e-10
128/128 [==============================] - 0s 1ms/step - loss: 9.6224e-10
    150. 

  150. Epoch 8/10
    151. 

  151. 
  1/128 [..............................] - ETA: 0s - loss: 7.9178e-10
 42/128 [========>.....................] - ETA: 0s - loss: 8.9481e-10
 83/128 [==================>...........] - ETA: 0s - loss: 1.0338e-09
120/128 [===========================>..] - ETA: 0s - loss: 9.2117e-10
128/128 [==============================] - 0s 1ms/step - loss: 9.0594e-10
    152. 

  152. Epoch 9/10
    153. 

  153. 
  1/128 [..............................] - ETA: 0s - loss: 9.0026e-10
 40/128 [========>.....................] - ETA: 0s - loss: 1.2148e-09
 80/128 [=================>............] - ETA: 0s - loss: 9.3558e-10
120/128 [===========================>..] - ETA: 0s - loss: 9.0207e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.8684e-10
    154. 

  154. Epoch 10/10
    155. 

  155. 
  1/128 [..............................] - ETA: 0s - loss: 8.9806e-10
 40/128 [========>.....................] - ETA: 0s - loss: 6.3795e-10
 81/128 [=================>............] - ETA: 0s - loss: 7.1418e-10
122/128 [===========================>..] - ETA: 0s - loss: 8.6271e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.5460e-10
    156. 

  156. -> test with GAN.predict
    157. 

  157. GAN tn, fp: 318, 0
    158. 

  158. GAN fn, tp: 0, 11
    159. 

  159. GAN f1 score: 1.000
    160. 

  160. GAN cohens kappa score: 1.000
    161. 

  161. 

  162. -> test with 'LR'
    163. 

  163. LR tn, fp: 318, 0
    164. 

  164. LR fn, tp: 0, 11
    165. 

  165. LR f1 score: 1.000
    166. 

  166. LR cohens kappa score: 1.000
    167. 

  167. LR average precision score: 1.000
    168. 

  168. 

  169. -> test with 'RF'
    170. 

  170. RF tn, fp: 318, 0
    171. 

  171. RF fn, tp: 0, 11
    172. 

  172. RF f1 score: 1.000
    173. 

  173. RF cohens kappa score: 1.000
    174. 

  174. 

  175. -> test with 'GB'
    176. 

  176. GB tn, fp: 318, 0
    177. 

  177. GB fn, tp: 0, 11
    178. 

  178. GB f1 score: 1.000
    179. 

  179. GB cohens kappa score: 1.000
    180. 

  180. 

  181. -> test with 'KNN'
    182. 

  182. KNN tn, fp: 317, 1
    183. 

  183. KNN fn, tp: 0, 11
    184. 

  184. KNN f1 score: 0.957
    185. 

  185. KNN cohens kappa score: 0.955
    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 1229 synthetic samples
    192. 

  192. -> retrain GAN for predict
    193. 

  193. Epoch 1/10
    194. 

  194. 
  1/128 [..............................] - ETA: 19s - loss: 2.3824e-09
 42/128 [========>.....................] - ETA: 0s - loss: 4.7044e-09 
 84/128 [==================>...........] - ETA: 0s - loss: 3.4384e-09
125/128 [============================>.] - ETA: 0s - loss: 3.0728e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.0561e-09
    195. 

  195. Epoch 2/10
    196. 

  196. 
  1/128 [..............................] - ETA: 0s - loss: 2.3399e-09
 36/128 [=======>......................] - ETA: 0s - loss: 3.7245e-09
 72/128 [===============>..............] - ETA: 0s - loss: 3.0089e-09
107/128 [========================>.....] - ETA: 0s - loss: 2.8772e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.7369e-09
    197. 

  197. Epoch 3/10
    198. 

  198. 
  1/128 [..............................] - ETA: 0s - loss: 1.2800e-09
 40/128 [========>.....................] - ETA: 0s - loss: 2.1140e-09
 80/128 [=================>............] - ETA: 0s - loss: 2.7690e-09
119/128 [==========================>...] - ETA: 0s - loss: 2.5924e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.5265e-09
    199. 

  199. Epoch 4/10
    200. 

  200. 
  1/128 [..............................] - ETA: 0s - loss: 4.2231e-09
 40/128 [========>.....................] - ETA: 0s - loss: 3.7242e-09
 80/128 [=================>............] - ETA: 0s - loss: 2.7228e-09
120/128 [===========================>..] - ETA: 0s - loss: 2.3946e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.3627e-09
    201. 

  201. Epoch 5/10
    202. 

  202. 
  1/128 [..............................] - ETA: 0s - loss: 1.8716e-09
 41/128 [========>.....................] - ETA: 0s - loss: 2.1079e-09
 79/128 [=================>............] - ETA: 0s - loss: 1.8560e-09
121/128 [===========================>..] - ETA: 0s - loss: 1.7654e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.2483e-09
    203. 

  203. Epoch 6/10
    204. 

  204. 
  1/128 [..............................] - ETA: 0s - loss: 1.7309e-09
 40/128 [========>.....................] - ETA: 0s - loss: 1.5170e-09
 80/128 [=================>............] - ETA: 0s - loss: 1.5942e-09
119/128 [==========================>...] - ETA: 0s - loss: 2.1202e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.1559e-09
    205. 

  205. Epoch 7/10
    206. 

  206. 
  1/128 [..............................] - ETA: 0s - loss: 1.2211e-09
 42/128 [========>.....................] - ETA: 0s - loss: 1.6087e-09
 83/128 [==================>...........] - ETA: 0s - loss: 2.4092e-09
123/128 [===========================>..] - ETA: 0s - loss: 2.0908e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.0579e-09
    207. 

  207. Epoch 8/10
    208. 

  208. 
  1/128 [..............................] - ETA: 0s - loss: 2.1012e-09
 40/128 [========>.....................] - ETA: 0s - loss: 1.3383e-09
 80/128 [=================>............] - ETA: 0s - loss: 2.1692e-09
120/128 [===========================>..] - ETA: 0s - loss: 2.0205e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.9886e-09
    209. 

  209. Epoch 9/10
    210. 

  210. 
  1/128 [..............................] - ETA: 0s - loss: 9.9728e-10
 42/128 [========>.....................] - ETA: 0s - loss: 1.5521e-09
 82/128 [==================>...........] - ETA: 0s - loss: 2.1981e-09
123/128 [===========================>..] - ETA: 0s - loss: 1.9322e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.9106e-09
    211. 

  211. Epoch 10/10
    212. 

  212. 
  1/128 [..............................] - ETA: 0s - loss: 1.1330e-09
 42/128 [========>.....................] - ETA: 0s - loss: 2.7922e-09
 83/128 [==================>...........] - ETA: 0s - loss: 2.0962e-09
124/128 [============================>.] - ETA: 0s - loss: 1.8570e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.8448e-09
    213. 

  213. -> test with GAN.predict
    214. 

  214. GAN tn, fp: 318, 0
    215. 

  215. GAN fn, tp: 0, 11
    216. 

  216. GAN f1 score: 1.000
    217. 

  217. GAN cohens kappa score: 1.000
    218. 

  218. 

  219. -> test with 'LR'
    220. 

  220. LR tn, fp: 318, 0
    221. 

  221. LR fn, tp: 0, 11
    222. 

  222. LR f1 score: 1.000
    223. 

  223. LR cohens kappa score: 1.000
    224. 

  224. LR average precision score: 1.000
    225. 

  225. 

  226. -> test with 'RF'
    227. 

  227. RF tn, fp: 318, 0
    228. 

  228. RF fn, tp: 0, 11
    229. 

  229. RF f1 score: 1.000
    230. 

  230. RF cohens kappa score: 1.000
    231. 

  231. 

  232. -> test with 'GB'
    233. 

  233. GB tn, fp: 318, 0
    234. 

  234. GB fn, tp: 0, 11
    235. 

  235. GB f1 score: 1.000
    236. 

  236. GB cohens kappa score: 1.000
    237. 

  237. 

  238. -> test with 'KNN'
    239. 

  239. KNN tn, fp: 318, 0
    240. 

  240. KNN fn, tp: 0, 11
    241. 

  241. KNN f1 score: 1.000
    242. 

  242. KNN cohens kappa score: 1.000
    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 1228 synthetic samples
    249. 

  249. -> retrain GAN for predict
    250. 

  250. Epoch 1/10
    251. 

  251. 
  1/128 [..............................] - ETA: 20s - loss: 4.0775e-09
 40/128 [========>.....................] - ETA: 0s - loss: 4.3599e-09 
 81/128 [=================>............] - ETA: 0s - loss: 8.9048e-09
122/128 [===========================>..] - ETA: 0s - loss: 7.5389e-09
128/128 [==============================] - 0s 1ms/step - loss: 7.4208e-09
    252. 

  252. Epoch 2/10
    253. 

  253. 
  1/128 [..............................] - ETA: 0s - loss: 5.8716e-09
 41/128 [========>.....................] - ETA: 0s - loss: 3.7496e-09
 81/128 [=================>............] - ETA: 0s - loss: 3.7067e-09
122/128 [===========================>..] - ETA: 0s - loss: 4.0096e-09
128/128 [==============================] - 0s 1ms/step - loss: 4.0500e-09
    254. 

  254. Epoch 3/10
    255. 

  255. 
  1/128 [..............................] - ETA: 0s - loss: 3.7828e-09
 41/128 [========>.....................] - ETA: 0s - loss: 4.0490e-09
 82/128 [==================>...........] - ETA: 0s - loss: 4.0131e-09
122/128 [===========================>..] - ETA: 0s - loss: 3.8101e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.8148e-09
    256. 

  256. Epoch 4/10
    257. 

  257. 
  1/128 [..............................] - ETA: 0s - loss: 3.1511e-09
 40/128 [========>.....................] - ETA: 0s - loss: 3.7698e-09
 78/128 [=================>............] - ETA: 0s - loss: 3.6883e-09
119/128 [==========================>...] - ETA: 0s - loss: 3.6728e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.6322e-09
    258. 

  258. Epoch 5/10
    259. 

  259. 
  1/128 [..............................] - ETA: 0s - loss: 2.4225e-09
 42/128 [========>.....................] - ETA: 0s - loss: 3.1484e-09
 81/128 [=================>............] - ETA: 0s - loss: 3.7109e-09
121/128 [===========================>..] - ETA: 0s - loss: 3.5283e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.5037e-09
    260. 

  260. Epoch 6/10
    261. 

  261. 
  1/128 [..............................] - ETA: 0s - loss: 6.3797e-09
 41/128 [========>.....................] - ETA: 0s - loss: 3.3123e-09
 81/128 [=================>............] - ETA: 0s - loss: 3.4523e-09
121/128 [===========================>..] - ETA: 0s - loss: 3.4304e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.3696e-09
    262. 

  262. Epoch 7/10
    263. 

  263. 
  1/128 [..............................] - ETA: 0s - loss: 6.5691e-09
 42/128 [========>.....................] - ETA: 0s - loss: 3.5943e-09
 80/128 [=================>............] - ETA: 0s - loss: 3.3526e-09
117/128 [==========================>...] - ETA: 0s - loss: 3.2607e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.2507e-09
    264. 

  264. Epoch 8/10
    265. 

  265. 
  1/128 [..............................] - ETA: 0s - loss: 1.4527e-09
 42/128 [========>.....................] - ETA: 0s - loss: 2.9768e-09
 82/128 [==================>...........] - ETA: 0s - loss: 3.1636e-09
116/128 [==========================>...] - ETA: 0s - loss: 3.1673e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.1613e-09
    266. 

  266. Epoch 9/10
    267. 

  267. 
  1/128 [..............................] - ETA: 0s - loss: 3.3838e-09
 32/128 [======>.......................] - ETA: 0s - loss: 2.8206e-09
 70/128 [===============>..............] - ETA: 0s - loss: 3.0762e-09
110/128 [========================>.....] - ETA: 0s - loss: 3.0816e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.0503e-09
    268. 

  268. Epoch 10/10
    269. 

  269. 
  1/128 [..............................] - ETA: 0s - loss: 2.8851e-09
 40/128 [========>.....................] - ETA: 0s - loss: 3.1905e-09
 82/128 [==================>...........] - ETA: 0s - loss: 3.1326e-09
124/128 [============================>.] - ETA: 0s - loss: 2.9653e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.9550e-09
    270. 

  270. -> test with GAN.predict
    271. 

  271. GAN tn, fp: 317, 0
    272. 

  272. GAN fn, tp: 0, 9
    273. 

  273. GAN f1 score: 1.000
    274. 

  274. GAN cohens kappa score: 1.000
    275. 

  275. 

  276. -> test with 'LR'
    277. 

  277. LR tn, fp: 317, 0
    278. 

  278. LR fn, tp: 0, 9
    279. 

  279. LR f1 score: 1.000
    280. 

  280. LR cohens kappa score: 1.000
    281. 

  281. LR average precision score: 1.000
    282. 

  282. 

  283. -> test with 'RF'
    284. 

  284. RF tn, fp: 317, 0
    285. 

  285. RF fn, tp: 0, 9
    286. 

  286. RF f1 score: 1.000
    287. 

  287. RF cohens kappa score: 1.000
    288. 

  288. 

  289. -> test with 'GB'
    290. 

  290. GB tn, fp: 317, 0
    291. 

  291. GB fn, tp: 0, 9
    292. 

  292. GB f1 score: 1.000
    293. 

  293. GB cohens kappa score: 1.000
    294. 

  294. 

  295. -> test with 'KNN'
    296. 

  296. KNN tn, fp: 317, 0
    297. 

  297. KNN fn, tp: 0, 9
    298. 

  298. KNN f1 score: 1.000
    299. 

  299. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    310. 

  310. -> retrain GAN for predict
    311. 

  311. Epoch 1/10
    312. 

  312. 
  1/128 [..............................] - ETA: 21s - loss: 1.0512e-09
 42/128 [========>.....................] - ETA: 0s - loss: 1.0739e-09 
 83/128 [==================>...........] - ETA: 0s - loss: 1.2339e-09
123/128 [===========================>..] - ETA: 0s - loss: 1.0943e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.0863e-09
    313. 

  313. Epoch 2/10
    314. 

  314. 
  1/128 [..............................] - ETA: 0s - loss: 1.8160e-09
 42/128 [========>.....................] - ETA: 0s - loss: 7.7659e-10
 82/128 [==================>...........] - ETA: 0s - loss: 7.6377e-10
123/128 [===========================>..] - ETA: 0s - loss: 1.0274e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.0165e-09
    315. 

  315. Epoch 3/10
    316. 

  316. 
  1/128 [..............................] - ETA: 0s - loss: 7.4252e-10
 41/128 [========>.....................] - ETA: 0s - loss: 7.0837e-10
 79/128 [=================>............] - ETA: 0s - loss: 7.1757e-10
118/128 [==========================>...] - ETA: 0s - loss: 8.9642e-10
128/128 [==============================] - 0s 1ms/step - loss: 9.4923e-10
    317. 

  317. Epoch 4/10
    318. 

  318. 
  1/128 [..............................] - ETA: 0s - loss: 7.2671e-10
 39/128 [========>.....................] - ETA: 0s - loss: 1.2465e-09
 77/128 [=================>............] - ETA: 0s - loss: 8.9100e-10
117/128 [==========================>...] - ETA: 0s - loss: 7.9202e-10
128/128 [==============================] - 0s 1ms/step - loss: 7.7724e-10
    319. 

  319. Epoch 5/10
    320. 

  320. 
  1/128 [..............................] - ETA: 0s - loss: 7.8384e-10
 37/128 [=======>......................] - ETA: 0s - loss: 1.1164e-09
 77/128 [=================>............] - ETA: 0s - loss: 8.5107e-10
117/128 [==========================>...] - ETA: 0s - loss: 7.3486e-10
128/128 [==============================] - 0s 1ms/step - loss: 7.4738e-10
    321. 

  321. Epoch 6/10
    322. 

  322. 
  1/128 [..............................] - ETA: 0s - loss: 6.4737e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.1377e-09
 82/128 [==================>...........] - ETA: 0s - loss: 8.4215e-10
120/128 [===========================>..] - ETA: 0s - loss: 7.3621e-10
128/128 [==============================] - 0s 1ms/step - loss: 7.2249e-10
    323. 

  323. Epoch 7/10
    324. 

  324. 
  1/128 [..............................] - ETA: 0s - loss: 3.7562e-10
 36/128 [=======>......................] - ETA: 0s - loss: 1.1054e-09
 68/128 [==============>...............] - ETA: 0s - loss: 8.7140e-10
101/128 [======================>.......] - ETA: 0s - loss: 7.5473e-10
128/128 [==============================] - 0s 1ms/step - loss: 7.0405e-10
    325. 

  325. Epoch 8/10
    326. 

  326. 
  1/128 [..............................] - ETA: 0s - loss: 2.0298e-10
 42/128 [========>.....................] - ETA: 0s - loss: 1.0807e-09
 83/128 [==================>...........] - ETA: 0s - loss: 8.0376e-10
124/128 [============================>.] - ETA: 0s - loss: 6.8863e-10
128/128 [==============================] - 0s 1ms/step - loss: 6.8448e-10
    327. 

  327. Epoch 9/10
    328. 

  328. 
  1/128 [..............................] - ETA: 0s - loss: 7.0610e-10
 41/128 [========>.....................] - ETA: 0s - loss: 5.4836e-10
 80/128 [=================>............] - ETA: 0s - loss: 7.8203e-10
120/128 [===========================>..] - ETA: 0s - loss: 6.7758e-10
128/128 [==============================] - 0s 1ms/step - loss: 6.6534e-10
    329. 

  329. Epoch 10/10
    330. 

  330. 
  1/128 [..............................] - ETA: 0s - loss: 3.5385e-10
 42/128 [========>.....................] - ETA: 0s - loss: 4.9278e-10
 83/128 [==================>...........] - ETA: 0s - loss: 4.8260e-10
125/128 [============================>.] - ETA: 0s - loss: 6.5703e-10
128/128 [==============================] - 0s 1ms/step - loss: 6.5188e-10
    331. 

  331. -> test with GAN.predict
    332. 

  332. GAN tn, fp: 318, 0
    333. 

  333. GAN fn, tp: 0, 11
    334. 

  334. GAN f1 score: 1.000
    335. 

  335. GAN cohens kappa score: 1.000
    336. 

  336. 

  337. -> test with 'LR'
    338. 

  338. LR tn, fp: 318, 0
    339. 

  339. LR fn, tp: 0, 11
    340. 

  340. LR f1 score: 1.000
    341. 

  341. LR cohens kappa score: 1.000
    342. 

  342. LR average precision score: 1.000
    343. 

  343. 

  344. -> test with 'RF'
    345. 

  345. RF tn, fp: 318, 0
    346. 

  346. RF fn, tp: 0, 11
    347. 

  347. RF f1 score: 1.000
    348. 

  348. RF cohens kappa score: 1.000
    349. 

  349. 

  350. -> test with 'GB'
    351. 

  351. GB tn, fp: 318, 0
    352. 

  352. GB fn, tp: 0, 11
    353. 

  353. GB f1 score: 1.000
    354. 

  354. GB cohens kappa score: 1.000
    355. 

  355. 

  356. -> test with 'KNN'
    357. 

  357. KNN tn, fp: 318, 0
    358. 

  358. KNN fn, tp: 0, 11
    359. 

  359. KNN f1 score: 1.000
    360. 

  360. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    367. 

  367. -> retrain GAN for predict
    368. 

  368. Epoch 1/10
    369. 

  369. 
  1/128 [..............................] - ETA: 21s - loss: 1.5280e-09
 42/128 [========>.....................] - ETA: 0s - loss: 2.0646e-09 
 83/128 [==================>...........] - ETA: 0s - loss: 1.3698e-08
122/128 [===========================>..] - ETA: 0s - loss: 9.7648e-09
128/128 [==============================] - 0s 1ms/step - loss: 9.4255e-09
    370. 

  370. Epoch 2/10
    371. 

  371. 
  1/128 [..............................] - ETA: 0s - loss: 1.3955e-09
 40/128 [========>.....................] - ETA: 0s - loss: 1.6909e-09
 81/128 [=================>............] - ETA: 0s - loss: 1.5715e-09
122/128 [===========================>..] - ETA: 0s - loss: 1.5211e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4999e-09
    372. 

  372. Epoch 3/10
    373. 

  373. 
  1/128 [..............................] - ETA: 0s - loss: 1.3747e-09
 42/128 [========>.....................] - ETA: 0s - loss: 1.3904e-09
 81/128 [=================>............] - ETA: 0s - loss: 1.3737e-09
121/128 [===========================>..] - ETA: 0s - loss: 1.4874e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4905e-09
    374. 

  374. Epoch 4/10
    375. 

  375. 
  1/128 [..............................] - ETA: 0s - loss: 1.7683e-09
 41/128 [========>.....................] - ETA: 0s - loss: 1.4088e-09
 79/128 [=================>............] - ETA: 0s - loss: 1.5940e-09
112/128 [=========================>....] - ETA: 0s - loss: 1.4963e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4816e-09
    376. 

  376. Epoch 5/10
    377. 

  377. 
  1/128 [..............................] - ETA: 0s - loss: 1.3180e-09
 35/128 [=======>......................] - ETA: 0s - loss: 1.4411e-09
 75/128 [================>.............] - ETA: 0s - loss: 1.3255e-09
112/128 [=========================>....] - ETA: 0s - loss: 1.4928e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4721e-09
    378. 

  378. Epoch 6/10
    379. 

  379. 
  1/128 [..............................] - ETA: 0s - loss: 6.7075e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.2350e-09
 81/128 [=================>............] - ETA: 0s - loss: 1.3071e-09
122/128 [===========================>..] - ETA: 0s - loss: 1.4690e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4632e-09
    380. 

  380. Epoch 7/10
    381. 

  381. 
  1/128 [..............................] - ETA: 0s - loss: 1.0679e-09
 41/128 [========>.....................] - ETA: 0s - loss: 1.2271e-09
 81/128 [=================>............] - ETA: 0s - loss: 1.5194e-09
121/128 [===========================>..] - ETA: 0s - loss: 1.4513e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4543e-09
    382. 

  382. Epoch 8/10
    383. 

  383. 
  1/128 [..............................] - ETA: 0s - loss: 1.8802e-09
 42/128 [========>.....................] - ETA: 0s - loss: 1.2883e-09
 82/128 [==================>...........] - ETA: 0s - loss: 1.3167e-09
122/128 [===========================>..] - ETA: 0s - loss: 1.4119e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4436e-09
    384. 

  384. Epoch 9/10
    385. 

  385. 
  1/128 [..............................] - ETA: 0s - loss: 1.1600e-09
 41/128 [========>.....................] - ETA: 0s - loss: 1.2759e-09
 80/128 [=================>............] - ETA: 0s - loss: 1.5170e-09
120/128 [===========================>..] - ETA: 0s - loss: 1.4395e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4339e-09
    386. 

  386. Epoch 10/10
    387. 

  387. 
  1/128 [..............................] - ETA: 0s - loss: 8.5465e-10
 40/128 [========>.....................] - ETA: 0s - loss: 1.6435e-09
 80/128 [=================>............] - ETA: 0s - loss: 1.4972e-09
115/128 [=========================>....] - ETA: 0s - loss: 1.4388e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.4243e-09
    388. 

  388. -> test with GAN.predict
    389. 

  389. GAN tn, fp: 318, 0
    390. 

  390. GAN fn, tp: 0, 11
    391. 

  391. GAN f1 score: 1.000
    392. 

  392. GAN cohens kappa score: 1.000
    393. 

  393. 

  394. -> test with 'LR'
    395. 

  395. LR tn, fp: 318, 0
    396. 

  396. LR fn, tp: 0, 11
    397. 

  397. LR f1 score: 1.000
    398. 

  398. LR cohens kappa score: 1.000
    399. 

  399. LR average precision score: 1.000
    400. 

  400. 

  401. -> test with 'RF'
    402. 

  402. RF tn, fp: 318, 0
    403. 

  403. RF fn, tp: 0, 11
    404. 

  404. RF f1 score: 1.000
    405. 

  405. RF cohens kappa score: 1.000
    406. 

  406. 

  407. -> test with 'GB'
    408. 

  408. GB tn, fp: 318, 0
    409. 

  409. GB fn, tp: 0, 11
    410. 

  410. GB f1 score: 1.000
    411. 

  411. GB cohens kappa score: 1.000
    412. 

  412. 

  413. -> test with 'KNN'
    414. 

  414. KNN tn, fp: 317, 1
    415. 

  415. KNN fn, tp: 0, 11
    416. 

  416. KNN f1 score: 0.957
    417. 

  417. KNN cohens kappa score: 0.955
    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 1229 synthetic samples
    424. 

  424. -> retrain GAN for predict
    425. 

  425. Epoch 1/10
    426. 

  426. 
  1/128 [..............................] - ETA: 23s - loss: 1.4440e-09
 40/128 [========>.....................] - ETA: 0s - loss: 2.3862e-09 
 81/128 [=================>............] - ETA: 0s - loss: 3.7894e-09
122/128 [===========================>..] - ETA: 0s - loss: 2.9761e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.9042e-09
    427. 

  427. Epoch 2/10
    428. 

  428. 
  1/128 [..............................] - ETA: 0s - loss: 1.7418e-09
 40/128 [========>.....................] - ETA: 0s - loss: 8.0377e-10
 81/128 [=================>............] - ETA: 0s - loss: 9.1136e-10
121/128 [===========================>..] - ETA: 0s - loss: 1.2299e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.2057e-09
    429. 

  429. Epoch 3/10
    430. 

  430. 
  1/128 [..............................] - ETA: 0s - loss: 6.7400e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.1454e-09
 76/128 [================>.............] - ETA: 0s - loss: 1.0012e-09
109/128 [========================>.....] - ETA: 0s - loss: 1.1310e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.0646e-09
    431. 

  431. Epoch 4/10
    432. 

  432. 
  1/128 [..............................] - ETA: 0s - loss: 3.4555e-10
 39/128 [========>.....................] - ETA: 0s - loss: 6.8073e-10
 78/128 [=================>............] - ETA: 0s - loss: 7.7507e-10
118/128 [==========================>...] - ETA: 0s - loss: 9.5469e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.0297e-09
    433. 

  433. Epoch 5/10
    434. 

  434. 
  1/128 [..............................] - ETA: 0s - loss: 7.2851e-10
 42/128 [========>.....................] - ETA: 0s - loss: 9.2890e-10
 82/128 [==================>...........] - ETA: 0s - loss: 1.0544e-09
121/128 [===========================>..] - ETA: 0s - loss: 9.5848e-10
128/128 [==============================] - 0s 1ms/step - loss: 9.4165e-10
    435. 

  435. Epoch 6/10
    436. 

  436. 
  1/128 [..............................] - ETA: 0s - loss: 6.8515e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.2043e-09
 82/128 [==================>...........] - ETA: 0s - loss: 9.9784e-10
123/128 [===========================>..] - ETA: 0s - loss: 9.2092e-10
128/128 [==============================] - 0s 1ms/step - loss: 9.0759e-10
    437. 

  437. Epoch 7/10
    438. 

  438. 
  1/128 [..............................] - ETA: 0s - loss: 4.5829e-10
 41/128 [========>.....................] - ETA: 0s - loss: 6.6684e-10
 80/128 [=================>............] - ETA: 0s - loss: 7.7083e-10
121/128 [===========================>..] - ETA: 0s - loss: 8.9448e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.8432e-10
    439. 

  439. Epoch 8/10
    440. 

  440. 
  1/128 [..............................] - ETA: 0s - loss: 6.7309e-10
 42/128 [========>.....................] - ETA: 0s - loss: 7.4895e-10
 80/128 [=================>............] - ETA: 0s - loss: 1.0405e-09
120/128 [===========================>..] - ETA: 0s - loss: 8.7976e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.6336e-10
    441. 

  441. Epoch 9/10
    442. 

  442. 
  1/128 [..............................] - ETA: 0s - loss: 5.4358e-10
 40/128 [========>.....................] - ETA: 0s - loss: 5.5526e-10
 80/128 [=================>............] - ETA: 0s - loss: 7.0694e-10
119/128 [==========================>...] - ETA: 0s - loss: 8.5254e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3075e-10
    443. 

  443. Epoch 10/10
    444. 

  444. 
  1/128 [..............................] - ETA: 0s - loss: 5.5745e-10
 38/128 [=======>......................] - ETA: 0s - loss: 1.1665e-09
 79/128 [=================>............] - ETA: 0s - loss: 9.3943e-10
117/128 [==========================>...] - ETA: 0s - loss: 8.0203e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.2380e-10
    445. 

  445. -> test with GAN.predict
    446. 

  446. GAN tn, fp: 318, 0
    447. 

  447. GAN fn, tp: 0, 11
    448. 

  448. GAN f1 score: 1.000
    449. 

  449. GAN cohens kappa score: 1.000
    450. 

  450. 

  451. -> test with 'LR'
    452. 

  452. LR tn, fp: 318, 0
    453. 

  453. LR fn, tp: 0, 11
    454. 

  454. LR f1 score: 1.000
    455. 

  455. LR cohens kappa score: 1.000
    456. 

  456. LR average precision score: 1.000
    457. 

  457. 

  458. -> test with 'RF'
    459. 

  459. RF tn, fp: 318, 0
    460. 

  460. RF fn, tp: 0, 11
    461. 

  461. RF f1 score: 1.000
    462. 

  462. RF cohens kappa score: 1.000
    463. 

  463. 

  464. -> test with 'GB'
    465. 

  465. GB tn, fp: 318, 0
    466. 

  466. GB fn, tp: 0, 11
    467. 

  467. GB f1 score: 1.000
    468. 

  468. GB cohens kappa score: 1.000
    469. 

  469. 

  470. -> test with 'KNN'
    471. 

  471. KNN tn, fp: 318, 0
    472. 

  472. KNN fn, tp: 0, 11
    473. 

  473. KNN f1 score: 1.000
    474. 

  474. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    481. 

  481. -> retrain GAN for predict
    482. 

  482. Epoch 1/10
    483. 

  483. 
  1/128 [..............................] - ETA: 23s - loss: 1.1086e-10
 42/128 [========>.....................] - ETA: 0s - loss: 3.7055e-10 
 83/128 [==================>...........] - ETA: 0s - loss: 0.0130    
121/128 [===========================>..] - ETA: 0s - loss: 0.0089
128/128 [==============================] - 0s 1ms/step - loss: 0.0085
    484. 

  484. Epoch 2/10
    485. 

  485. 
  1/128 [..............................] - ETA: 0s - loss: 7.4247e-12
 42/128 [========>.....................] - ETA: 0s - loss: 2.8608e-11
 80/128 [=================>............] - ETA: 0s - loss: 3.5425e-10
119/128 [==========================>...] - ETA: 0s - loss: 2.4445e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3342e-10
    486. 

  486. Epoch 3/10
    487. 

  487. 
  1/128 [..............................] - ETA: 0s - loss: 6.0788e-12
 33/128 [======>.......................] - ETA: 0s - loss: 3.9154e-11
 71/128 [===============>..............] - ETA: 0s - loss: 4.0217e-10
107/128 [========================>.....] - ETA: 0s - loss: 2.7308e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3323e-10
    488. 

  488. Epoch 4/10
    489. 

  489. 
  1/128 [..............................] - ETA: 0s - loss: 4.2676e-11
 42/128 [========>.....................] - ETA: 0s - loss: 2.3632e-11
 83/128 [==================>...........] - ETA: 0s - loss: 3.4218e-10
121/128 [===========================>..] - ETA: 0s - loss: 2.4378e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3306e-10
    490. 

  490. Epoch 5/10
    491. 

  491. 
  1/128 [..............................] - ETA: 0s - loss: 3.2273e-12
 42/128 [========>.....................] - ETA: 0s - loss: 6.4702e-10
 84/128 [==================>...........] - ETA: 0s - loss: 3.3514e-10
125/128 [============================>.] - ETA: 0s - loss: 2.3626e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3287e-10
    492. 

  492. Epoch 6/10
    493. 

  493. 
  1/128 [..............................] - ETA: 0s - loss: 4.1858e-11
 39/128 [========>.....................] - ETA: 0s - loss: 2.1549e-11
 79/128 [=================>............] - ETA: 0s - loss: 2.3646e-11
120/128 [===========================>..] - ETA: 0s - loss: 2.4500e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3269e-10
    494. 

  494. Epoch 7/10
    495. 

  495. 
  1/128 [..............................] - ETA: 0s - loss: 1.6698e-11
 43/128 [=========>....................] - ETA: 0s - loss: 6.3610e-10
 85/128 [==================>...........] - ETA: 0s - loss: 3.3609e-10
123/128 [===========================>..] - ETA: 0s - loss: 2.3993e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3249e-10
    496. 

  496. Epoch 8/10
    497. 

  497. 
  1/128 [..............................] - ETA: 0s - loss: 1.4630e-11
 41/128 [========>.....................] - ETA: 0s - loss: 2.1227e-11
 81/128 [=================>............] - ETA: 0s - loss: 3.4810e-10
122/128 [===========================>..] - ETA: 0s - loss: 2.4087e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3229e-10
    498. 

  498. Epoch 9/10
    499. 

  499. 
  1/128 [..............................] - ETA: 0s - loss: 1.1556e-11
 41/128 [========>.....................] - ETA: 0s - loss: 2.7147e-11
 83/128 [==================>...........] - ETA: 0s - loss: 3.4242e-10
123/128 [===========================>..] - ETA: 0s - loss: 2.3914e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3209e-10
    500. 

  500. Epoch 10/10
    501. 

  501. 
  1/128 [..............................] - ETA: 0s - loss: 3.6549e-12
 42/128 [========>.....................] - ETA: 0s - loss: 2.2954e-11
 83/128 [==================>...........] - ETA: 0s - loss: 2.2857e-11
124/128 [============================>.] - ETA: 0s - loss: 2.3687e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.3187e-10
    502. 

  502. -> test with GAN.predict
    503. 

  503. GAN tn, fp: 318, 0
    504. 

  504. GAN fn, tp: 0, 11
    505. 

  505. GAN f1 score: 1.000
    506. 

  506. GAN cohens kappa score: 1.000
    507. 

  507. 

  508. -> test with 'LR'
    509. 

  509. LR tn, fp: 318, 0
    510. 

  510. LR fn, tp: 0, 11
    511. 

  511. LR f1 score: 1.000
    512. 

  512. LR cohens kappa score: 1.000
    513. 

  513. LR average precision score: 1.000
    514. 

  514. 

  515. -> test with 'RF'
    516. 

  516. RF tn, fp: 318, 0
    517. 

  517. RF fn, tp: 0, 11
    518. 

  518. RF f1 score: 1.000
    519. 

  519. RF cohens kappa score: 1.000
    520. 

  520. 

  521. -> test with 'GB'
    522. 

  522. GB tn, fp: 318, 0
    523. 

  523. GB fn, tp: 0, 11
    524. 

  524. GB f1 score: 1.000
    525. 

  525. GB cohens kappa score: 1.000
    526. 

  526. 

  527. -> test with 'KNN'
    528. 

  528. KNN tn, fp: 318, 0
    529. 

  529. KNN fn, tp: 0, 11
    530. 

  530. KNN f1 score: 1.000
    531. 

  531. KNN cohens kappa score: 1.000
    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 1228 synthetic samples
    538. 

  538. -> retrain GAN for predict
    539. 

  539. Epoch 1/10
    540. 

  540. 
  1/128 [..............................] - ETA: 24s - loss: 2.8385e-09
 37/128 [=======>......................] - ETA: 0s - loss: 7.2612e-06 
 67/128 [==============>...............] - ETA: 0s - loss: 4.0168e-06
 97/128 [=====================>........] - ETA: 0s - loss: 2.7779e-06
126/128 [============================>.] - ETA: 0s - loss: 2.1406e-06
128/128 [==============================] - 0s 2ms/step - loss: 2.1205e-06
    541. 

  541. Epoch 2/10
    542. 

  542. 
  1/128 [..............................] - ETA: 0s - loss: 5.3842e-09
 33/128 [======>.......................] - ETA: 0s - loss: 5.9206e-09
 67/128 [==============>...............] - ETA: 0s - loss: 7.0521e-09
104/128 [=======================>......] - ETA: 0s - loss: 3.6036e-08
128/128 [==============================] - 0s 1ms/step - loss: 3.0207e-08
    543. 

  543. Epoch 3/10
    544. 

  544. 
  1/128 [..............................] - ETA: 0s - loss: 5.0486e-09
 36/128 [=======>......................] - ETA: 0s - loss: 5.9200e-09
 70/128 [===============>..............] - ETA: 0s - loss: 4.9794e-09
104/128 [=======================>......] - ETA: 0s - loss: 4.7677e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.6488e-08
    545. 

  545. Epoch 4/10
    546. 

  546. 
  1/128 [..............................] - ETA: 0s - loss: 2.0720e-09
 36/128 [=======>......................] - ETA: 0s - loss: 5.9250e-09
 71/128 [===============>..............] - ETA: 0s - loss: 4.7595e-09
107/128 [========================>.....] - ETA: 0s - loss: 2.8117e-08
128/128 [==============================] - 0s 1ms/step - loss: 2.4246e-08
    547. 

  547. Epoch 5/10
    548. 

  548. 
  1/128 [..............................] - ETA: 0s - loss: 2.6001e-09
 36/128 [=======>......................] - ETA: 0s - loss: 3.3578e-09
 71/128 [===============>..............] - ETA: 0s - loss: 3.1713e-09
105/128 [=======================>......] - ETA: 0s - loss: 2.5691e-08
128/128 [==============================] - 0s 1ms/step - loss: 2.2271e-08
    549. 

  549. Epoch 6/10
    550. 

  550. 
  1/128 [..............................] - ETA: 0s - loss: 4.1158e-09
 39/128 [========>.....................] - ETA: 0s - loss: 2.9772e-09
 75/128 [================>.............] - ETA: 0s - loss: 2.8783e-09
114/128 [=========================>....] - ETA: 0s - loss: 3.0377e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.0723e-08
    551. 

  551. Epoch 7/10
    552. 

  552. 
  1/128 [..............................] - ETA: 0s - loss: 3.3838e-09
 37/128 [=======>......................] - ETA: 0s - loss: 3.3892e-09
 73/128 [================>.............] - ETA: 0s - loss: 4.0195e-09
109/128 [========================>.....] - ETA: 0s - loss: 2.2242e-08
128/128 [==============================] - 0s 1ms/step - loss: 1.9490e-08
    553. 

  553. Epoch 8/10
    554. 

  554. 
  1/128 [..............................] - ETA: 0s - loss: 2.6261e-09
 37/128 [=======>......................] - ETA: 0s - loss: 3.0477e-09
 72/128 [===============>..............] - ETA: 0s - loss: 3.0882e-09
107/128 [========================>.....] - ETA: 0s - loss: 2.0571e-08
128/128 [==============================] - 0s 1ms/step - loss: 1.8155e-08
    555. 

  555. Epoch 9/10
    556. 

  556. 
  1/128 [..............................] - ETA: 0s - loss: 8.7774e-10
 35/128 [=======>......................] - ETA: 0s - loss: 5.4914e-08
 70/128 [===============>..............] - ETA: 0s - loss: 2.8819e-08
105/128 [=======================>......] - ETA: 0s - loss: 2.0161e-08
128/128 [==============================] - 0s 1ms/step - loss: 1.7222e-08
    557. 

  557. Epoch 10/10
    558. 

  558. 
  1/128 [..............................] - ETA: 0s - loss: 2.0105e-09
 35/128 [=======>......................] - ETA: 0s - loss: 2.8101e-09
 69/128 [===============>..............] - ETA: 0s - loss: 2.6771e-08
100/128 [======================>.......] - ETA: 0s - loss: 1.9238e-08
128/128 [==============================] - 0s 2ms/step - loss: 1.6206e-08
    559. 

  559. -> test with GAN.predict
    560. 

  560. GAN tn, fp: 317, 0
    561. 

  561. GAN fn, tp: 0, 9
    562. 

  562. GAN f1 score: 1.000
    563. 

  563. GAN cohens kappa score: 1.000
    564. 

  564. 

  565. -> test with 'LR'
    566. 

  566. LR tn, fp: 317, 0
    567. 

  567. LR fn, tp: 0, 9
    568. 

  568. LR f1 score: 1.000
    569. 

  569. LR cohens kappa score: 1.000
    570. 

  570. LR average precision score: 1.000
    571. 

  571. 

  572. -> test with 'RF'
    573. 

  573. RF tn, fp: 317, 0
    574. 

  574. RF fn, tp: 0, 9
    575. 

  575. RF f1 score: 1.000
    576. 

  576. RF cohens kappa score: 1.000
    577. 

  577. 

  578. -> test with 'GB'
    579. 

  579. GB tn, fp: 317, 0
    580. 

  580. GB fn, tp: 0, 9
    581. 

  581. GB f1 score: 1.000
    582. 

  582. GB cohens kappa score: 1.000
    583. 

  583. 

  584. -> test with 'KNN'
    585. 

  585. KNN tn, fp: 317, 0
    586. 

  586. KNN fn, tp: 0, 9
    587. 

  587. KNN f1 score: 1.000
    588. 

  588. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    599. 

  599. -> retrain GAN for predict
    600. 

  600. Epoch 1/10
    601. 

  601. 
  1/128 [..............................] - ETA: 19s - loss: 1.1125e-09
 41/128 [========>.....................] - ETA: 0s - loss: 3.4027e-09 
 82/128 [==================>...........] - ETA: 0s - loss: 9.5507e-09
123/128 [===========================>..] - ETA: 0s - loss: 6.7993e-09
128/128 [==============================] - 0s 1ms/step - loss: 6.6350e-09
    602. 

  602. Epoch 2/10
    603. 

  603. 
  1/128 [..............................] - ETA: 0s - loss: 5.9582e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.0250e-08
 80/128 [=================>............] - ETA: 0s - loss: 5.8022e-09
119/128 [==========================>...] - ETA: 0s - loss: 4.7212e-09
128/128 [==============================] - 0s 1ms/step - loss: 4.4802e-09
    604. 

  604. Epoch 3/10
    605. 

  605. 
  1/128 [..............................] - ETA: 0s - loss: 2.2635e-08
 36/128 [=======>......................] - ETA: 0s - loss: 1.4712e-09
 68/128 [==============>...............] - ETA: 0s - loss: 1.5191e-09
107/128 [========================>.....] - ETA: 0s - loss: 4.1179e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.6049e-09
    606. 

  606. Epoch 4/10
    607. 

  607. 
  1/128 [..............................] - ETA: 0s - loss: 4.5685e-10
 42/128 [========>.....................] - ETA: 0s - loss: 2.0213e-09
 82/128 [==================>...........] - ETA: 0s - loss: 1.9897e-09
122/128 [===========================>..] - ETA: 0s - loss: 3.2069e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.1055e-09
    608. 

  608. Epoch 5/10
    609. 

  609. 
  1/128 [..............................] - ETA: 0s - loss: 8.2621e-10
 38/128 [=======>......................] - ETA: 0s - loss: 1.7171e-09
 74/128 [================>.............] - ETA: 0s - loss: 1.5891e-09
110/128 [========================>.....] - ETA: 0s - loss: 3.0147e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.7285e-09
    610. 

  610. Epoch 6/10
    611. 

  611. 
  1/128 [..............................] - ETA: 0s - loss: 2.3073e-10
 42/128 [========>.....................] - ETA: 0s - loss: 4.2406e-09
 82/128 [==================>...........] - ETA: 0s - loss: 2.9205e-09
122/128 [===========================>..] - ETA: 0s - loss: 2.5183e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.4548e-09
    612. 

  612. Epoch 7/10
    613. 

  613. 
  1/128 [..............................] - ETA: 0s - loss: 4.6584e-10
 41/128 [========>.....................] - ETA: 0s - loss: 3.8503e-09
 79/128 [=================>............] - ETA: 0s - loss: 2.4220e-09
120/128 [===========================>..] - ETA: 0s - loss: 2.3148e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.2319e-09
    614. 

  614. Epoch 8/10
    615. 

  615. 
  1/128 [..............................] - ETA: 0s - loss: 3.5218e-10
 42/128 [========>.....................] - ETA: 0s - loss: 3.9153e-09
 83/128 [==================>...........] - ETA: 0s - loss: 2.6416e-09
124/128 [============================>.] - ETA: 0s - loss: 2.0711e-09
128/128 [==============================] - 0s 1ms/step - loss: 2.0531e-09
    616. 

  616. Epoch 9/10
    617. 

  617. 
  1/128 [..............................] - ETA: 0s - loss: 5.4544e-10
 36/128 [=======>......................] - ETA: 0s - loss: 1.0923e-09
 76/128 [================>.............] - ETA: 0s - loss: 1.0667e-09
116/128 [==========================>...] - ETA: 0s - loss: 1.8216e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.9059e-09
    618. 

  618. Epoch 10/10
    619. 

  619. 
  1/128 [..............................] - ETA: 0s - loss: 7.0788e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.2138e-09
 82/128 [==================>...........] - ETA: 0s - loss: 2.1301e-09
122/128 [===========================>..] - ETA: 0s - loss: 1.8108e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.7772e-09
    620. 

  620. -> test with GAN.predict
    621. 

  621. GAN tn, fp: 318, 0
    622. 

  622. GAN fn, tp: 0, 11
    623. 

  623. GAN f1 score: 1.000
    624. 

  624. GAN cohens kappa score: 1.000
    625. 

  625. 

  626. -> test with 'LR'
    627. 

  627. LR tn, fp: 318, 0
    628. 

  628. LR fn, tp: 0, 11
    629. 

  629. LR f1 score: 1.000
    630. 

  630. LR cohens kappa score: 1.000
    631. 

  631. LR average precision score: 1.000
    632. 

  632. 

  633. -> test with 'RF'
    634. 

  634. RF tn, fp: 318, 0
    635. 

  635. RF fn, tp: 0, 11
    636. 

  636. RF f1 score: 1.000
    637. 

  637. RF cohens kappa score: 1.000
    638. 

  638. 

  639. -> test with 'GB'
    640. 

  640. GB tn, fp: 318, 0
    641. 

  641. GB fn, tp: 0, 11
    642. 

  642. GB f1 score: 1.000
    643. 

  643. GB cohens kappa score: 1.000
    644. 

  644. 

  645. -> test with 'KNN'
    646. 

  646. KNN tn, fp: 317, 1
    647. 

  647. KNN fn, tp: 0, 11
    648. 

  648. KNN f1 score: 0.957
    649. 

  649. KNN cohens kappa score: 0.955
    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 1229 synthetic samples
    656. 

  656. -> retrain GAN for predict
    657. 

  657. Epoch 1/10
    658. 

  658. 
  1/128 [..............................] - ETA: 21s - loss: 4.1303e-09
 41/128 [========>.....................] - ETA: 0s - loss: 0.0669     
 81/128 [=================>............] - ETA: 0s - loss: 0.0343
122/128 [===========================>..] - ETA: 0s - loss: 0.0227
128/128 [==============================] - 0s 1ms/step - loss: 0.0218
    659. 

  659. Epoch 2/10
    660. 

  660. 
  1/128 [..............................] - ETA: 0s - loss: 4.2182e-07
 42/128 [========>.....................] - ETA: 0s - loss: 3.0616e-07
 78/128 [=================>............] - ETA: 0s - loss: 3.0284e-07
110/128 [========================>.....] - ETA: 0s - loss: 2.8913e-07
128/128 [==============================] - 0s 1ms/step - loss: 2.8601e-07
    661. 

  661. Epoch 3/10
    662. 

  662. 
  1/128 [..............................] - ETA: 0s - loss: 2.1788e-07
 33/128 [======>.......................] - ETA: 0s - loss: 2.5635e-07
 73/128 [================>.............] - ETA: 0s - loss: 2.4998e-07
112/128 [=========================>....] - ETA: 0s - loss: 2.4402e-07
128/128 [==============================] - 0s 1ms/step - loss: 2.3693e-07
    663. 

  663. Epoch 4/10
    664. 

  664. 
  1/128 [..............................] - ETA: 0s - loss: 8.9473e-08
 42/128 [========>.....................] - ETA: 0s - loss: 1.9200e-07
 81/128 [=================>............] - ETA: 0s - loss: 2.0658e-07
119/128 [==========================>...] - ETA: 0s - loss: 2.0021e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.9819e-07
    665. 

  665. Epoch 5/10
    666. 

  666. 
  1/128 [..............................] - ETA: 0s - loss: 2.0109e-07
 41/128 [========>.....................] - ETA: 0s - loss: 1.7302e-07
 80/128 [=================>............] - ETA: 0s - loss: 1.6720e-07
119/128 [==========================>...] - ETA: 0s - loss: 1.6737e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.6651e-07
    667. 

  667. Epoch 6/10
    668. 

  668. 
  1/128 [..............................] - ETA: 0s - loss: 3.0140e-07
 42/128 [========>.....................] - ETA: 0s - loss: 1.5593e-07
 81/128 [=================>............] - ETA: 0s - loss: 1.4975e-07
121/128 [===========================>..] - ETA: 0s - loss: 1.4313e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.4107e-07
    669. 

  669. Epoch 7/10
    670. 

  670. 
  1/128 [..............................] - ETA: 0s - loss: 1.3679e-07
 43/128 [=========>....................] - ETA: 0s - loss: 1.3744e-07
 85/128 [==================>...........] - ETA: 0s - loss: 1.2835e-07
124/128 [============================>.] - ETA: 0s - loss: 1.2054e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.2005e-07
    671. 

  671. Epoch 8/10
    672. 

  672. 
  1/128 [..............................] - ETA: 0s - loss: 4.9952e-08
 37/128 [=======>......................] - ETA: 0s - loss: 1.0576e-07
 77/128 [=================>............] - ETA: 0s - loss: 1.0436e-07
114/128 [=========================>....] - ETA: 0s - loss: 1.0248e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.0261e-07
    673. 

  673. Epoch 9/10
    674. 

  674. 
  1/128 [..............................] - ETA: 0s - loss: 1.7293e-07
 41/128 [========>.....................] - ETA: 0s - loss: 9.2644e-08
 81/128 [=================>............] - ETA: 0s - loss: 9.4115e-08
122/128 [===========================>..] - ETA: 0s - loss: 8.8266e-08
128/128 [==============================] - 0s 1ms/step - loss: 8.8122e-08
    675. 

  675. Epoch 10/10
    676. 

  676. 
  1/128 [..............................] - ETA: 0s - loss: 8.9407e-08
 41/128 [========>.....................] - ETA: 0s - loss: 8.0545e-08
 82/128 [==================>...........] - ETA: 0s - loss: 7.6692e-08
121/128 [===========================>..] - ETA: 0s - loss: 7.4742e-08
128/128 [==============================] - 0s 1ms/step - loss: 7.5897e-08
    677. 

  677. -> test with GAN.predict
    678. 

  678. GAN tn, fp: 318, 0
    679. 

  679. GAN fn, tp: 0, 11
    680. 

  680. GAN f1 score: 1.000
    681. 

  681. GAN cohens kappa score: 1.000
    682. 

  682. 

  683. -> test with 'LR'
    684. 

  684. LR tn, fp: 318, 0
    685. 

  685. LR fn, tp: 0, 11
    686. 

  686. LR f1 score: 1.000
    687. 

  687. LR cohens kappa score: 1.000
    688. 

  688. LR average precision score: 1.000
    689. 

  689. 

  690. -> test with 'RF'
    691. 

  691. RF tn, fp: 318, 0
    692. 

  692. RF fn, tp: 0, 11
    693. 

  693. RF f1 score: 1.000
    694. 

  694. RF cohens kappa score: 1.000
    695. 

  695. 

  696. -> test with 'GB'
    697. 

  697. GB tn, fp: 318, 0
    698. 

  698. GB fn, tp: 0, 11
    699. 

  699. GB f1 score: 1.000
    700. 

  700. GB cohens kappa score: 1.000
    701. 

  701. 

  702. -> test with 'KNN'
    703. 

  703. KNN tn, fp: 318, 0
    704. 

  704. KNN fn, tp: 0, 11
    705. 

  705. KNN f1 score: 1.000
    706. 

  706. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    713. 

  713. -> retrain GAN for predict
    714. 

  714. Epoch 1/10
    715. 

  715. 
  1/128 [..............................] - ETA: 23s - loss: 1.1647e-09
 41/128 [========>.....................] - ETA: 0s - loss: 3.7304e-09 
 81/128 [=================>............] - ETA: 0s - loss: 3.2490e-09
121/128 [===========================>..] - ETA: 0s - loss: 1.7517e-05
128/128 [==============================] - 0s 1ms/step - loss: 1.6680e-05
    716. 

  716. Epoch 2/10
    717. 

  717. 
  1/128 [..............................] - ETA: 0s - loss: 6.6315e-08
 41/128 [========>.....................] - ETA: 0s - loss: 8.8047e-06
 77/128 [=================>............] - ETA: 0s - loss: 4.6986e-06
115/128 [=========================>....] - ETA: 0s - loss: 3.1496e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.8510e-06
    718. 

  718. Epoch 3/10
    719. 

  719. 
  1/128 [..............................] - ETA: 0s - loss: 5.9090e-09
 41/128 [========>.....................] - ETA: 0s - loss: 6.2021e-06
 82/128 [==================>...........] - ETA: 0s - loss: 3.1075e-06
123/128 [===========================>..] - ETA: 0s - loss: 2.0739e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.0073e-06
    720. 

  720. Epoch 4/10
    721. 

  721. 
  1/128 [..............................] - ETA: 0s - loss: 1.1616e-08
 41/128 [========>.....................] - ETA: 0s - loss: 6.0252e-09
 79/128 [=================>............] - ETA: 0s - loss: 2.4777e-06
113/128 [=========================>....] - ETA: 0s - loss: 1.7363e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.5446e-06
    722. 

  722. Epoch 5/10
    723. 

  723. 
  1/128 [..............................] - ETA: 0s - loss: 1.1434e-08
 35/128 [=======>......................] - ETA: 0s - loss: 7.8181e-09
 73/128 [================>.............] - ETA: 0s - loss: 2.1371e-06
114/128 [=========================>....] - ETA: 0s - loss: 1.3710e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.2304e-06
    724. 

  724. Epoch 6/10
    725. 

  725. 
  1/128 [..............................] - ETA: 0s - loss: 1.1662e-08
 42/128 [========>.....................] - ETA: 0s - loss: 6.6640e-09
 81/128 [=================>............] - ETA: 0s - loss: 6.0866e-09
119/128 [==========================>...] - ETA: 0s - loss: 1.0824e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.0140e-06
    726. 

  726. Epoch 7/10
    727. 

  727. 
  1/128 [..............................] - ETA: 0s - loss: 1.1169e-08
 39/128 [========>.....................] - ETA: 0s - loss: 6.8291e-09
 79/128 [=================>............] - ETA: 0s - loss: 5.8823e-09
118/128 [==========================>...] - ETA: 0s - loss: 9.1572e-07
128/128 [==============================] - 0s 1ms/step - loss: 8.5078e-07
    728. 

  728. Epoch 8/10
    729. 

  729. 
  1/128 [..............................] - ETA: 0s - loss: 1.1007e-08
 39/128 [========>.....................] - ETA: 0s - loss: 2.3497e-06
 78/128 [=================>............] - ETA: 0s - loss: 1.1779e-06
118/128 [==========================>...] - ETA: 0s - loss: 7.8075e-07
128/128 [==============================] - 0s 1ms/step - loss: 7.2517e-07
    730. 

  730. Epoch 9/10
    731. 

  731. 
  1/128 [..............................] - ETA: 0s - loss: 5.0428e-09
 41/128 [========>.....................] - ETA: 0s - loss: 1.9316e-06
 81/128 [=================>............] - ETA: 0s - loss: 9.8146e-07
122/128 [===========================>..] - ETA: 0s - loss: 6.5345e-07
128/128 [==============================] - 0s 1ms/step - loss: 6.2739e-07
    732. 

  732. Epoch 10/10
    733. 

  733. 
  1/128 [..............................] - ETA: 0s - loss: 3.2929e-09
 41/128 [========>.....................] - ETA: 0s - loss: 1.6877e-06
 81/128 [=================>............] - ETA: 0s - loss: 8.5678e-07
122/128 [===========================>..] - ETA: 0s - loss: 5.7086e-07
128/128 [==============================] - 0s 1ms/step - loss: 5.4813e-07
    734. 

  734. -> test with GAN.predict
    735. 

  735. GAN tn, fp: 318, 0
    736. 

  736. GAN fn, tp: 0, 11
    737. 

  737. GAN f1 score: 1.000
    738. 

  738. GAN cohens kappa score: 1.000
    739. 

  739. 

  740. -> test with 'LR'
    741. 

  741. LR tn, fp: 318, 0
    742. 

  742. LR fn, tp: 0, 11
    743. 

  743. LR f1 score: 1.000
    744. 

  744. LR cohens kappa score: 1.000
    745. 

  745. LR average precision score: 1.000
    746. 

  746. 

  747. -> test with 'RF'
    748. 

  748. RF tn, fp: 318, 0
    749. 

  749. RF fn, tp: 0, 11
    750. 

  750. RF f1 score: 1.000
    751. 

  751. RF cohens kappa score: 1.000
    752. 

  752. 

  753. -> test with 'GB'
    754. 

  754. GB tn, fp: 318, 0
    755. 

  755. GB fn, tp: 0, 11
    756. 

  756. GB f1 score: 1.000
    757. 

  757. GB cohens kappa score: 1.000
    758. 

  758. 

  759. -> test with 'KNN'
    760. 

  760. KNN tn, fp: 318, 0
    761. 

  761. KNN fn, tp: 0, 11
    762. 

  762. KNN f1 score: 1.000
    763. 

  763. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    770. 

  770. -> retrain GAN for predict
    771. 

  771. Epoch 1/10
    772. 

  772. 
  1/128 [..............................] - ETA: 20s - loss: 2.6636e-09
 42/128 [========>.....................] - ETA: 0s - loss: 1.9389e-09 
 82/128 [==================>...........] - ETA: 0s - loss: 1.4544e-05
121/128 [===========================>..] - ETA: 0s - loss: 9.8636e-06
128/128 [==============================] - 0s 1ms/step - loss: 9.3904e-06
    773. 

  773. Epoch 2/10
    774. 

  774. 
  1/128 [..............................] - ETA: 0s - loss: 2.8135e-09
 39/128 [========>.....................] - ETA: 0s - loss: 5.5031e-09
 79/128 [=================>............] - ETA: 0s - loss: 1.1975e-06
120/128 [===========================>..] - ETA: 0s - loss: 7.8986e-07
128/128 [==============================] - 0s 1ms/step - loss: 7.4594e-07
    775. 

  775. Epoch 3/10
    776. 

  776. 
  1/128 [..............................] - ETA: 0s - loss: 2.8323e-09
 42/128 [========>.....................] - ETA: 0s - loss: 3.1609e-09
 83/128 [==================>...........] - ETA: 0s - loss: 4.0258e-09
121/128 [===========================>..] - ETA: 0s - loss: 5.7737e-07
128/128 [==============================] - 0s 1ms/step - loss: 5.4989e-07
    777. 

  777. Epoch 4/10
    778. 

  778. 
  1/128 [..............................] - ETA: 0s - loss: 3.5581e-09
 40/128 [========>.....................] - ETA: 0s - loss: 1.3877e-06
 81/128 [=================>............] - ETA: 0s - loss: 6.8987e-07
117/128 [==========================>...] - ETA: 0s - loss: 4.7878e-07
128/128 [==============================] - 0s 1ms/step - loss: 4.4121e-07
    779. 

  779. Epoch 5/10
    780. 

  780. 
  1/128 [..............................] - ETA: 0s - loss: 2.2147e-09
 40/128 [========>.....................] - ETA: 0s - loss: 4.5247e-09
 79/128 [=================>............] - ETA: 0s - loss: 3.6212e-09
116/128 [==========================>...] - ETA: 0s - loss: 3.9382e-07
128/128 [==============================] - 0s 1ms/step - loss: 3.5972e-07
    781. 

  781. Epoch 6/10
    782. 

  782. 
  1/128 [..............................] - ETA: 0s - loss: 2.2887e-09
 41/128 [========>.....................] - ETA: 0s - loss: 3.2252e-09
 80/128 [=================>............] - ETA: 0s - loss: 4.7361e-07
120/128 [===========================>..] - ETA: 0s - loss: 3.1677e-07
128/128 [==============================] - 0s 1ms/step - loss: 2.9976e-07
    783. 

  783. Epoch 7/10
    784. 

  784. 
  1/128 [..............................] - ETA: 0s - loss: 2.3356e-09
 41/128 [========>.....................] - ETA: 0s - loss: 3.2427e-09
 81/128 [=================>............] - ETA: 0s - loss: 3.4685e-09
121/128 [===========================>..] - ETA: 0s - loss: 2.6748e-07
128/128 [==============================] - 0s 1ms/step - loss: 2.5476e-07
    785. 

  785. Epoch 8/10
    786. 

  786. 
  1/128 [..............................] - ETA: 0s - loss: 3.3947e-09
 40/128 [========>.....................] - ETA: 0s - loss: 2.8938e-09
 79/128 [=================>............] - ETA: 0s - loss: 3.5037e-07
111/128 [=========================>....] - ETA: 0s - loss: 2.5056e-07
128/128 [==============================] - 0s 1ms/step - loss: 2.1922e-07
    787. 

  787. Epoch 9/10
    788. 

  788. 
  1/128 [..............................] - ETA: 0s - loss: 1.2073e-09
 40/128 [========>.....................] - ETA: 0s - loss: 2.3438e-09
 78/128 [=================>............] - ETA: 0s - loss: 3.0999e-07
117/128 [==========================>...] - ETA: 0s - loss: 2.0760e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.9129e-07
    789. 

  789. Epoch 10/10
    790. 

  790. 
  1/128 [..............................] - ETA: 0s - loss: 9.8104e-10
 41/128 [========>.....................] - ETA: 0s - loss: 5.1777e-07
 82/128 [==================>...........] - ETA: 0s - loss: 2.6027e-07
123/128 [===========================>..] - ETA: 0s - loss: 1.7435e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.6880e-07
    791. 

  791. -> test with GAN.predict
    792. 

  792. GAN tn, fp: 318, 0
    793. 

  793. GAN fn, tp: 0, 11
    794. 

  794. GAN f1 score: 1.000
    795. 

  795. GAN cohens kappa score: 1.000
    796. 

  796. 

  797. -> test with 'LR'
    798. 

  798. LR tn, fp: 318, 0
    799. 

  799. LR fn, tp: 0, 11
    800. 

  800. LR f1 score: 1.000
    801. 

  801. LR cohens kappa score: 1.000
    802. 

  802. LR average precision score: 1.000
    803. 

  803. 

  804. -> test with 'RF'
    805. 

  805. RF tn, fp: 318, 0
    806. 

  806. RF fn, tp: 0, 11
    807. 

  807. RF f1 score: 1.000
    808. 

  808. RF cohens kappa score: 1.000
    809. 

  809. 

  810. -> test with 'GB'
    811. 

  811. GB tn, fp: 318, 0
    812. 

  812. GB fn, tp: 0, 11
    813. 

  813. GB f1 score: 1.000
    814. 

  814. GB cohens kappa score: 1.000
    815. 

  815. 

  816. -> test with 'KNN'
    817. 

  817. KNN tn, fp: 318, 0
    818. 

  818. KNN fn, tp: 0, 11
    819. 

  819. KNN f1 score: 1.000
    820. 

  820. KNN cohens kappa score: 1.000
    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 1228 synthetic samples
    827. 

  827. -> retrain GAN for predict
    828. 

  828. Epoch 1/10
    829. 

  829. 
  1/128 [..............................] - ETA: 22s - loss: 1.6707e-10
 42/128 [========>.....................] - ETA: 0s - loss: 7.6904e-10 
 81/128 [=================>............] - ETA: 0s - loss: 5.5079e-10
121/128 [===========================>..] - ETA: 0s - loss: 4.6977e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.8579e-09
    830. 

  830. Epoch 2/10
    831. 

  831. 
  1/128 [..............................] - ETA: 0s - loss: 1.7190e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.2920e-10
 82/128 [==================>...........] - ETA: 0s - loss: 3.1384e-10
118/128 [==========================>...] - ETA: 0s - loss: 2.2949e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.1613e-10
    832. 

  832. Epoch 3/10
    833. 

  833. 
  1/128 [..............................] - ETA: 0s - loss: 1.8776e-11
 38/128 [=======>......................] - ETA: 0s - loss: 1.0601e-10
 78/128 [=================>............] - ETA: 0s - loss: 7.1239e-11
118/128 [==========================>...] - ETA: 0s - loss: 5.8619e-11
128/128 [==============================] - 0s 1ms/step - loss: 2.0691e-10
    834. 

  834. Epoch 4/10
    835. 

  835. 
  1/128 [..............................] - ETA: 0s - loss: 4.4184e-11
 41/128 [========>.....................] - ETA: 0s - loss: 3.1417e-11
 81/128 [=================>............] - ETA: 0s - loss: 6.6787e-11
114/128 [=========================>....] - ETA: 0s - loss: 2.2538e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.0512e-10
    836. 

  836. Epoch 5/10
    837. 

  837. 
  1/128 [..............................] - ETA: 0s - loss: 9.9657e-12
 40/128 [========>.....................] - ETA: 0s - loss: 3.2652e-11
 81/128 [=================>............] - ETA: 0s - loss: 2.9774e-10
120/128 [===========================>..] - ETA: 0s - loss: 2.1333e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.0314e-10
    838. 

  838. Epoch 6/10
    839. 

  839. 
  1/128 [..............................] - ETA: 0s - loss: 3.4222e-11
 39/128 [========>.....................] - ETA: 0s - loss: 3.3197e-11
 80/128 [=================>............] - ETA: 0s - loss: 3.4980e-11
120/128 [===========================>..] - ETA: 0s - loss: 2.1132e-10
128/128 [==============================] - 0s 1ms/step - loss: 2.0115e-10
    840. 

  840. Epoch 7/10
    841. 

  841. 
  1/128 [..............................] - ETA: 0s - loss: 2.6490e-11
 41/128 [========>.....................] - ETA: 0s - loss: 3.1142e-11
 82/128 [==================>...........] - ETA: 0s - loss: 3.4197e-11
119/128 [==========================>...] - ETA: 0s - loss: 1.9194e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.9929e-10
    842. 

  842. Epoch 8/10
    843. 

  843. 
  1/128 [..............................] - ETA: 0s - loss: 1.6170e-11
 36/128 [=======>......................] - ETA: 0s - loss: 3.5801e-11
 72/128 [===============>..............] - ETA: 0s - loss: 2.9537e-10
108/128 [========================>.....] - ETA: 0s - loss: 2.2677e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.9727e-10
    844. 

  844. Epoch 9/10
    845. 

  845. 
  1/128 [..............................] - ETA: 0s - loss: 3.0396e-11
 34/128 [======>.......................] - ETA: 0s - loss: 9.3405e-11
 68/128 [==============>...............] - ETA: 0s - loss: 3.3849e-10
107/128 [========================>.....] - ETA: 0s - loss: 2.2654e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.9495e-10
    846. 

  846. Epoch 10/10
    847. 

  847. 
  1/128 [..............................] - ETA: 0s - loss: 2.5381e-11
 39/128 [========>.....................] - ETA: 0s - loss: 3.0162e-11
 78/128 [=================>............] - ETA: 0s - loss: 2.9698e-10
117/128 [==========================>...] - ETA: 0s - loss: 2.0777e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.9326e-10
    848. 

  848. -> test with GAN.predict
    849. 

  849. GAN tn, fp: 317, 0
    850. 

  850. GAN fn, tp: 0, 9
    851. 

  851. GAN f1 score: 1.000
    852. 

  852. GAN cohens kappa score: 1.000
    853. 

  853. 

  854. -> test with 'LR'
    855. 

  855. LR tn, fp: 317, 0
    856. 

  856. LR fn, tp: 0, 9
    857. 

  857. LR f1 score: 1.000
    858. 

  858. LR cohens kappa score: 1.000
    859. 

  859. LR average precision score: 1.000
    860. 

  860. 

  861. -> test with 'RF'
    862. 

  862. RF tn, fp: 317, 0
    863. 

  863. RF fn, tp: 0, 9
    864. 

  864. RF f1 score: 1.000
    865. 

  865. RF cohens kappa score: 1.000
    866. 

  866. 

  867. -> test with 'GB'
    868. 

  868. GB tn, fp: 317, 0
    869. 

  869. GB fn, tp: 0, 9
    870. 

  870. GB f1 score: 1.000
    871. 

  871. GB cohens kappa score: 1.000
    872. 

  872. 

  873. -> test with 'KNN'
    874. 

  874. KNN tn, fp: 317, 0
    875. 

  875. KNN fn, tp: 0, 9
    876. 

  876. KNN f1 score: 1.000
    877. 

  877. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    888. 

  888. -> retrain GAN for predict
    889. 

  889. Epoch 1/10
    890. 

  890. 
  1/128 [..............................] - ETA: 22s - loss: 7.9322e-10
 33/128 [======>.......................] - ETA: 0s - loss: 7.1365e-04 
 64/128 [==============>...............] - ETA: 0s - loss: 3.6799e-04
 99/128 [======================>.......] - ETA: 0s - loss: 2.3790e-04
128/128 [==============================] - 0s 2ms/step - loss: 1.8531e-04
    891. 

  891. Epoch 2/10
    892. 

  892. 
  1/128 [..............................] - ETA: 0s - loss: 4.9383e-09
 39/128 [========>.....................] - ETA: 0s - loss: 1.2522e-05
 79/128 [=================>............] - ETA: 0s - loss: 6.2000e-06
113/128 [=========================>....] - ETA: 0s - loss: 4.3411e-06
128/128 [==============================] - 0s 1ms/step - loss: 3.8613e-06
    893. 

  893. Epoch 3/10
    894. 

  894. 
  1/128 [..............................] - ETA: 0s - loss: 2.9327e-08
 30/128 [======>.......................] - ETA: 0s - loss: 1.6332e-08
 62/128 [=============>................] - ETA: 0s - loss: 4.5455e-06
 94/128 [=====================>........] - ETA: 0s - loss: 3.0055e-06
128/128 [==============================] - ETA: 0s - loss: 2.2314e-06
128/128 [==============================] - 0s 2ms/step - loss: 2.2314e-06
    895. 

  895. Epoch 4/10
    896. 

  896. 
  1/128 [..............................] - ETA: 0s - loss: 1.1871e-08
 34/128 [======>.......................] - ETA: 0s - loss: 1.4784e-08
 69/128 [===============>..............] - ETA: 0s - loss: 3.1521e-06
103/128 [=======================>......] - ETA: 0s - loss: 2.1183e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.7232e-06
    897. 

  897. Epoch 5/10
    898. 

  898. 
  1/128 [..............................] - ETA: 0s - loss: 2.5873e-08
 35/128 [=======>......................] - ETA: 0s - loss: 3.1165e-08
 71/128 [===============>..............] - ETA: 0s - loss: 2.5519e-06
103/128 [=======================>......] - ETA: 0s - loss: 1.7637e-06
128/128 [==============================] - 0s 2ms/step - loss: 1.4321e-06
    899. 

  899. Epoch 6/10
    900. 

  900. 
  1/128 [..............................] - ETA: 0s - loss: 1.0858e-08
 32/128 [======>.......................] - ETA: 0s - loss: 4.8719e-06
 62/128 [=============>................] - ETA: 0s - loss: 2.5223e-06
 98/128 [=====================>........] - ETA: 0s - loss: 1.5996e-06
128/128 [==============================] - 0s 2ms/step - loss: 1.2358e-06
    901. 

  901. Epoch 7/10
    902. 

  902. 
  1/128 [..............................] - ETA: 0s - loss: 3.1806e-09
 37/128 [=======>......................] - ETA: 0s - loss: 1.4131e-08
 76/128 [================>.............] - ETA: 0s - loss: 1.8192e-06
111/128 [=========================>....] - ETA: 0s - loss: 1.2495e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.0953e-06
    903. 

  903. Epoch 8/10
    904. 

  904. 
  1/128 [..............................] - ETA: 0s - loss: 9.1155e-09
 39/128 [========>.....................] - ETA: 0s - loss: 3.1584e-06
 75/128 [================>.............] - ETA: 0s - loss: 1.6464e-06
111/128 [=========================>....] - ETA: 0s - loss: 1.1199e-06
128/128 [==============================] - 0s 1ms/step - loss: 9.7964e-07
    905. 

  905. Epoch 9/10
    906. 

  906. 
  1/128 [..............................] - ETA: 0s - loss: 3.3164e-08
 38/128 [=======>......................] - ETA: 0s - loss: 8.7137e-09
 74/128 [================>.............] - ETA: 0s - loss: 1.5081e-06
108/128 [========================>.....] - ETA: 0s - loss: 1.0367e-06
128/128 [==============================] - 0s 1ms/step - loss: 8.8463e-07
    907. 

  907. Epoch 10/10
    908. 

  908. 
  1/128 [..............................] - ETA: 0s - loss: 2.8104e-09
 31/128 [======>.......................] - ETA: 0s - loss: 9.1427e-09
 67/128 [==============>...............] - ETA: 0s - loss: 8.0250e-09
103/128 [=======================>......] - ETA: 0s - loss: 9.9589e-07
128/128 [==============================] - 0s 2ms/step - loss: 8.0834e-07
    909. 

  909. -> test with GAN.predict
    910. 

  910. GAN tn, fp: 318, 0
    911. 

  911. GAN fn, tp: 0, 11
    912. 

  912. GAN f1 score: 1.000
    913. 

  913. GAN cohens kappa score: 1.000
    914. 

  914. 

  915. -> test with 'LR'
    916. 

  916. LR tn, fp: 318, 0
    917. 

  917. LR fn, tp: 0, 11
    918. 

  918. LR f1 score: 1.000
    919. 

  919. LR cohens kappa score: 1.000
    920. 

  920. LR average precision score: 1.000
    921. 

  921. 

  922. -> test with 'RF'
    923. 

  923. RF tn, fp: 318, 0
    924. 

  924. RF fn, tp: 0, 11
    925. 

  925. RF f1 score: 1.000
    926. 

  926. RF cohens kappa score: 1.000
    927. 

  927. 

  928. -> test with 'GB'
    929. 

  929. GB tn, fp: 318, 0
    930. 

  930. GB fn, tp: 0, 11
    931. 

  931. GB f1 score: 1.000
    932. 

  932. GB cohens kappa score: 1.000
    933. 

  933. 

  934. -> test with 'KNN'
    935. 

  935. KNN tn, fp: 318, 0
    936. 

  936. KNN fn, tp: 0, 11
    937. 

  937. KNN f1 score: 1.000
    938. 

  938. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    945. 

  945. -> retrain GAN for predict
    946. 

  946. Epoch 1/10
    947. 

  947. 
  1/128 [..............................] - ETA: 50s - loss: 5.3069e-11
 42/128 [========>.....................] - ETA: 0s - loss: 0.1202     
 82/128 [==================>...........] - ETA: 0s - loss: 0.0618
123/128 [===========================>..] - ETA: 0s - loss: 0.0425
128/128 [==============================] - 1s 1ms/step - loss: 0.0411
    948. 

  948. Epoch 2/10
    949. 

  949. 
  1/128 [..............................] - ETA: 0s - loss: 1.3768e-10
 41/128 [========>.....................] - ETA: 0s - loss: 2.0119e-09
 81/128 [=================>............] - ETA: 0s - loss: 1.2846e-09
122/128 [===========================>..] - ETA: 0s - loss: 8.6550e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3225e-10
    950. 

  950. Epoch 3/10
    951. 

  951. 
  1/128 [..............................] - ETA: 0s - loss: 5.1327e-11
 42/128 [========>.....................] - ETA: 0s - loss: 3.3071e-11
 77/128 [=================>............] - ETA: 0s - loss: 1.1031e-09
110/128 [========================>.....] - ETA: 0s - loss: 9.6507e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3988e-10
    952. 

  952. Epoch 4/10
    953. 

  953. 
  1/128 [..............................] - ETA: 0s - loss: 3.7000e-11
 39/128 [========>.....................] - ETA: 0s - loss: 4.0272e-11
 80/128 [=================>............] - ETA: 0s - loss: 1.3103e-09
120/128 [===========================>..] - ETA: 0s - loss: 8.8626e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3876e-10
    954. 

  954. Epoch 5/10
    955. 

  955. 
  1/128 [..............................] - ETA: 0s - loss: 4.2331e-11
 41/128 [========>.....................] - ETA: 0s - loss: 2.0347e-09
 81/128 [=================>............] - ETA: 0s - loss: 1.2949e-09
120/128 [===========================>..] - ETA: 0s - loss: 8.8486e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3759e-10
    956. 

  956. Epoch 6/10
    957. 

  957. 
  1/128 [..............................] - ETA: 0s - loss: 6.9406e-11
 41/128 [========>.....................] - ETA: 0s - loss: 3.3327e-11
 80/128 [=================>............] - ETA: 0s - loss: 3.5278e-11
119/128 [==========================>...] - ETA: 0s - loss: 8.9060e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3637e-10
    958. 

  958. Epoch 7/10
    959. 

  959. 
  1/128 [..............................] - ETA: 0s - loss: 6.3635e-11
 39/128 [========>.....................] - ETA: 0s - loss: 4.0259e-11
 79/128 [=================>............] - ETA: 0s - loss: 3.6400e-11
119/128 [==========================>...] - ETA: 0s - loss: 8.8953e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3508e-10
    960. 

  960. Epoch 8/10
    961. 

  961. 
  1/128 [..............................] - ETA: 0s - loss: 2.4958e-11
 40/128 [========>.....................] - ETA: 0s - loss: 3.9519e-11
 80/128 [=================>............] - ETA: 0s - loss: 1.3038e-09
119/128 [==========================>...] - ETA: 0s - loss: 8.8773e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3374e-10
    962. 

  962. Epoch 9/10
    963. 

  963. 
  1/128 [..............................] - ETA: 0s - loss: 2.6633e-11
 42/128 [========>.....................] - ETA: 0s - loss: 4.0171e-11
 82/128 [==================>...........] - ETA: 0s - loss: 1.2722e-09
123/128 [===========================>..] - ETA: 0s - loss: 8.5876e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3236e-10
    964. 

  964. Epoch 10/10
    965. 

  965. 
  1/128 [..............................] - ETA: 0s - loss: 4.8744e-11
 42/128 [========>.....................] - ETA: 0s - loss: 3.7706e-11
 82/128 [==================>...........] - ETA: 0s - loss: 1.0247e-09
118/128 [==========================>...] - ETA: 0s - loss: 8.9245e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3096e-10
    966. 

  966. -> test with GAN.predict
    967. 

  967. GAN tn, fp: 318, 0
    968. 

  968. GAN fn, tp: 0, 11
    969. 

  969. GAN f1 score: 1.000
    970. 

  970. GAN cohens kappa score: 1.000
    971. 

  971. 

  972. -> test with 'LR'
    973. 

  973. LR tn, fp: 318, 0
    974. 

  974. LR fn, tp: 0, 11
    975. 

  975. LR f1 score: 1.000
    976. 

  976. LR cohens kappa score: 1.000
    977. 

  977. LR average precision score: 1.000
    978. 

  978. 

  979. -> test with 'RF'
    980. 

  980. RF tn, fp: 318, 0
    981. 

  981. RF fn, tp: 0, 11
    982. 

  982. RF f1 score: 1.000
    983. 

  983. RF cohens kappa score: 1.000
    984. 

  984. 

  985. -> test with 'GB'
    986. 

  986. GB tn, fp: 318, 0
    987. 

  987. GB fn, tp: 0, 11
    988. 

  988. GB f1 score: 1.000
    989. 

  989. GB cohens kappa score: 1.000
    990. 

  990. 

  991. -> test with 'KNN'
    992. 

  992. KNN tn, fp: 318, 0
    993. 

  993. KNN fn, tp: 0, 11
    994. 

  994. KNN f1 score: 1.000
    995. 

  995. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    1002. 

  1002. -> retrain GAN for predict
    1003. 

  1003. Epoch 1/10
    1004. 

  1004. 
  1/128 [..............................] - ETA: 24s - loss: 3.2350e-11
 41/128 [========>.....................] - ETA: 0s - loss: 5.2977e-10 
 82/128 [==================>...........] - ETA: 0s - loss: 8.5991e-10
123/128 [===========================>..] - ETA: 0s - loss: 9.5074e-10
128/128 [==============================] - 0s 1ms/step - loss: 9.2014e-10
    1005. 

  1005. Epoch 2/10
    1006. 

  1006. 
  1/128 [..............................] - ETA: 0s - loss: 1.0581e-12
 40/128 [========>.....................] - ETA: 0s - loss: 8.9032e-11
 80/128 [=================>............] - ETA: 0s - loss: 4.7096e-11
120/128 [===========================>..] - ETA: 0s - loss: 1.3312e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.2583e-10
    1007. 

  1007. Epoch 3/10
    1008. 

  1008. 
  1/128 [..............................] - ETA: 0s - loss: 1.1135e-12
 41/128 [========>.....................] - ETA: 0s - loss: 2.8885e-10
 80/128 [=================>............] - ETA: 0s - loss: 1.5535e-10
121/128 [===========================>..] - ETA: 0s - loss: 1.2587e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.1990e-10
    1009. 

  1009. Epoch 4/10
    1010. 

  1010. 
  1/128 [..............................] - ETA: 0s - loss: 2.5610e-12
 41/128 [========>.....................] - ETA: 0s - loss: 3.5287e-10
 81/128 [=================>............] - ETA: 0s - loss: 1.7963e-10
121/128 [===========================>..] - ETA: 0s - loss: 1.2091e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.1531e-10
    1011. 

  1011. Epoch 5/10
    1012. 

  1012. 
  1/128 [..............................] - ETA: 0s - loss: 3.4296e-11
 42/128 [========>.....................] - ETA: 0s - loss: 2.8046e-12
 80/128 [=================>............] - ETA: 0s - loss: 2.8373e-12
121/128 [===========================>..] - ETA: 0s - loss: 2.0310e-11
128/128 [==============================] - 0s 1ms/step - loss: 1.1174e-10
    1013. 

  1013. Epoch 6/10
    1014. 

  1014. 
  1/128 [..............................] - ETA: 0s - loss: 2.1238e-12
 32/128 [======>.......................] - ETA: 0s - loss: 3.4592e-12
 65/128 [==============>...............] - ETA: 0s - loss: 3.0351e-11
 97/128 [=====================>........] - ETA: 0s - loss: 1.4440e-10
128/128 [==============================] - 0s 2ms/step - loss: 1.1055e-10
    1015. 

  1015. Epoch 7/10
    1016. 

  1016. 
  1/128 [..............................] - ETA: 0s - loss: 7.5082e-13
 41/128 [========>.....................] - ETA: 0s - loss: 3.2875e-10
 81/128 [=================>............] - ETA: 0s - loss: 1.6754e-10
121/128 [===========================>..] - ETA: 0s - loss: 1.1495e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.0948e-10
    1017. 

  1017. Epoch 8/10
    1018. 

  1018. 
  1/128 [..............................] - ETA: 0s - loss: 7.6667e-13
 39/128 [========>.....................] - ETA: 0s - loss: 3.4688e-10
 78/128 [=================>............] - ETA: 0s - loss: 1.7511e-10
118/128 [==========================>...] - ETA: 0s - loss: 1.1677e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.0857e-10
    1019. 

  1019. Epoch 9/10
    1020. 

  1020. 
  1/128 [..............................] - ETA: 0s - loss: 1.3942e-12
 37/128 [=======>......................] - ETA: 0s - loss: 4.4166e-11
 72/128 [===============>..............] - ETA: 0s - loss: 1.8617e-10
106/128 [=======================>......] - ETA: 0s - loss: 1.2880e-10
128/128 [==============================] - 0s 1ms/step - loss: 1.0769e-10
    1021. 

  1021. Epoch 10/10
    1022. 

  1022. 
  1/128 [..............................] - ETA: 0s - loss: 6.7932e-13
 41/128 [========>.....................] - ETA: 0s - loss: 7.5227e-12
 81/128 [=================>............] - ETA: 0s - loss: 2.2736e-11
121/128 [===========================>..] - ETA: 0s - loss: 1.5989e-11
128/128 [==============================] - 0s 1ms/step - loss: 1.0685e-10
    1023. 

  1023. -> test with GAN.predict
    1024. 

  1024. GAN tn, fp: 318, 0
    1025. 

  1025. GAN fn, tp: 0, 11
    1026. 

  1026. GAN f1 score: 1.000
    1027. 

  1027. GAN cohens kappa score: 1.000
    1028. 

  1028. 

  1029. -> test with 'LR'
    1030. 

  1030. LR tn, fp: 318, 0
    1031. 

  1031. LR fn, tp: 0, 11
    1032. 

  1032. LR f1 score: 1.000
    1033. 

  1033. LR cohens kappa score: 1.000
    1034. 

  1034. LR average precision score: 1.000
    1035. 

  1035. 

  1036. -> test with 'RF'
    1037. 

  1037. RF tn, fp: 318, 0
    1038. 

  1038. RF fn, tp: 0, 11
    1039. 

  1039. RF f1 score: 1.000
    1040. 

  1040. RF cohens kappa score: 1.000
    1041. 

  1041. 

  1042. -> test with 'GB'
    1043. 

  1043. GB tn, fp: 318, 0
    1044. 

  1044. GB fn, tp: 0, 11
    1045. 

  1045. GB f1 score: 1.000
    1046. 

  1046. GB cohens kappa score: 1.000
    1047. 

  1047. 

  1048. -> test with 'KNN'
    1049. 

  1049. KNN tn, fp: 318, 0
    1050. 

  1050. KNN fn, tp: 0, 11
    1051. 

  1051. KNN f1 score: 1.000
    1052. 

  1052. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    1059. 

  1059. -> retrain GAN for predict
    1060. 

  1060. Epoch 1/10
    1061. 

  1061. 
  1/128 [..............................] - ETA: 27s - loss: 3.9529e-09
 38/128 [=======>......................] - ETA: 0s - loss: 5.6325e-09 
 76/128 [================>.............] - ETA: 0s - loss: 5.4210e-09
113/128 [=========================>....] - ETA: 0s - loss: 5.4664e-09
128/128 [==============================] - 0s 1ms/step - loss: 5.4786e-09
    1062. 

  1062. Epoch 2/10
    1063. 

  1063. 
  1/128 [..............................] - ETA: 0s - loss: 6.2202e-09
 39/128 [========>.....................] - ETA: 0s - loss: 4.3330e-09
 75/128 [================>.............] - ETA: 0s - loss: 4.9906e-09
112/128 [=========================>....] - ETA: 0s - loss: 4.8509e-09
128/128 [==============================] - 0s 1ms/step - loss: 4.9583e-09
    1064. 

  1064. Epoch 3/10
    1065. 

  1065. 
  1/128 [..............................] - ETA: 0s - loss: 4.5780e-09
 37/128 [=======>......................] - ETA: 0s - loss: 4.2812e-09
 73/128 [================>.............] - ETA: 0s - loss: 4.6193e-09
108/128 [========================>.....] - ETA: 0s - loss: 4.5411e-09
128/128 [==============================] - 0s 1ms/step - loss: 4.5869e-09
    1066. 

  1066. Epoch 4/10
    1067. 

  1067. 
  1/128 [..............................] - ETA: 0s - loss: 2.3595e-08
 38/128 [=======>......................] - ETA: 0s - loss: 4.4644e-09
 74/128 [================>.............] - ETA: 0s - loss: 4.2527e-09
110/128 [========================>.....] - ETA: 0s - loss: 4.3124e-09
128/128 [==============================] - 0s 1ms/step - loss: 4.2959e-09
    1068. 

  1068. Epoch 5/10
    1069. 

  1069. 
  1/128 [..............................] - ETA: 0s - loss: 3.6091e-09
 36/128 [=======>......................] - ETA: 0s - loss: 3.9330e-09
 73/128 [================>.............] - ETA: 0s - loss: 4.0449e-09
109/128 [========================>.....] - ETA: 0s - loss: 4.1451e-09
128/128 [==============================] - 0s 1ms/step - loss: 4.0664e-09
    1070. 

  1070. Epoch 6/10
    1071. 

  1071. 
  1/128 [..............................] - ETA: 0s - loss: 3.1537e-09
 34/128 [======>.......................] - ETA: 0s - loss: 3.5259e-09
 71/128 [===============>..............] - ETA: 0s - loss: 4.2467e-09
106/128 [=======================>......] - ETA: 0s - loss: 4.0462e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.9117e-09
    1072. 

  1072. Epoch 7/10
    1073. 

  1073. 
  1/128 [..............................] - ETA: 0s - loss: 3.9421e-09
 37/128 [=======>......................] - ETA: 0s - loss: 3.5829e-09
 73/128 [================>.............] - ETA: 0s - loss: 3.5661e-09
109/128 [========================>.....] - ETA: 0s - loss: 3.8529e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.7604e-09
    1074. 

  1074. Epoch 8/10
    1075. 

  1075. 
  1/128 [..............................] - ETA: 0s - loss: 3.9964e-09
 37/128 [=======>......................] - ETA: 0s - loss: 3.4748e-09
 73/128 [================>.............] - ETA: 0s - loss: 3.6912e-09
109/128 [========================>.....] - ETA: 0s - loss: 3.6162e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.6290e-09
    1076. 

  1076. Epoch 9/10
    1077. 

  1077. 
  1/128 [..............................] - ETA: 0s - loss: 3.3420e-09
 42/128 [========>.....................] - ETA: 0s - loss: 3.4038e-09
 84/128 [==================>...........] - ETA: 0s - loss: 3.3771e-09
128/128 [==============================] - ETA: 0s - loss: 3.5189e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.5189e-09
    1078. 

  1078. Epoch 10/10
    1079. 

  1079. 
  1/128 [..............................] - ETA: 0s - loss: 3.7662e-09
 39/128 [========>.....................] - ETA: 0s - loss: 3.2784e-09
 76/128 [================>.............] - ETA: 0s - loss: 3.6241e-09
 93/128 [====================>.........] - ETA: 0s - loss: 3.5566e-09
128/128 [==============================] - 0s 2ms/step - loss: 3.4140e-09
    1080. 

  1080. -> test with GAN.predict
    1081. 

  1081. GAN tn, fp: 318, 0
    1082. 

  1082. GAN fn, tp: 0, 11
    1083. 

  1083. GAN f1 score: 1.000
    1084. 

  1084. GAN cohens kappa score: 1.000
    1085. 

  1085. 

  1086. -> test with 'LR'
    1087. 

  1087. LR tn, fp: 318, 0
    1088. 

  1088. LR fn, tp: 0, 11
    1089. 

  1089. LR f1 score: 1.000
    1090. 

  1090. LR cohens kappa score: 1.000
    1091. 

  1091. LR average precision score: 1.000
    1092. 

  1092. 

  1093. -> test with 'RF'
    1094. 

  1094. RF tn, fp: 318, 0
    1095. 

  1095. RF fn, tp: 0, 11
    1096. 

  1096. RF f1 score: 1.000
    1097. 

  1097. RF cohens kappa score: 1.000
    1098. 

  1098. 

  1099. -> test with 'GB'
    1100. 

  1100. GB tn, fp: 318, 0
    1101. 

  1101. GB fn, tp: 0, 11
    1102. 

  1102. GB f1 score: 1.000
    1103. 

  1103. GB cohens kappa score: 1.000
    1104. 

  1104. 

  1105. -> test with 'KNN'
    1106. 

  1106. KNN tn, fp: 318, 0
    1107. 

  1107. KNN fn, tp: 0, 11
    1108. 

  1108. KNN f1 score: 1.000
    1109. 

  1109. KNN cohens kappa score: 1.000
    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 1228 synthetic samples
    1116. 

  1116. -> retrain GAN for predict
    1117. 

  1117. Epoch 1/10
    1118. 

  1118. 
  1/128 [..............................] - ETA: 21s - loss: 2.8392e-10
 34/128 [======>.......................] - ETA: 0s - loss: 9.8471e-10 
 73/128 [================>.............] - ETA: 0s - loss: 5.8505e-09
112/128 [=========================>....] - ETA: 0s - loss: 3.8625e-09
128/128 [==============================] - 0s 1ms/step - loss: 3.4223e-09
    1119. 

  1119. Epoch 2/10
    1120. 

  1120. 
  1/128 [..............................] - ETA: 0s - loss: 1.0726e-10
 38/128 [=======>......................] - ETA: 0s - loss: 8.4221e-10
 78/128 [=================>............] - ETA: 0s - loss: 7.3790e-10
118/128 [==========================>...] - ETA: 0s - loss: 6.1135e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.7249e-10
    1121. 

  1121. Epoch 3/10
    1122. 

  1122. 
  1/128 [..............................] - ETA: 0s - loss: 6.9183e-11
 35/128 [=======>......................] - ETA: 0s - loss: 3.6903e-10
 69/128 [===============>..............] - ETA: 0s - loss: 2.4423e-10
103/128 [=======================>......] - ETA: 0s - loss: 5.9458e-10
128/128 [==============================] - 0s 1ms/step - loss: 5.0884e-10
    1123. 

  1123. Epoch 4/10
    1124. 

  1124. 
  1/128 [..............................] - ETA: 0s - loss: 3.1753e-10
 39/128 [========>.....................] - ETA: 0s - loss: 1.4106e-10
 77/128 [=================>............] - ETA: 0s - loss: 2.7157e-10
114/128 [=========================>....] - ETA: 0s - loss: 2.9672e-10
128/128 [==============================] - 0s 1ms/step - loss: 4.6569e-10
    1125. 

  1125. Epoch 5/10
    1126. 

  1126. 
  1/128 [..............................] - ETA: 0s - loss: 1.4712e-10
 40/128 [========>.....................] - ETA: 0s - loss: 9.9011e-11
 78/128 [=================>............] - ETA: 0s - loss: 5.3491e-10
117/128 [==========================>...] - ETA: 0s - loss: 4.4508e-10
128/128 [==============================] - 0s 1ms/step - loss: 4.3160e-10
    1127. 

  1127. Epoch 6/10
    1128. 

  1128. 
  1/128 [..............................] - ETA: 0s - loss: 3.2018e-10
 37/128 [=======>......................] - ETA: 0s - loss: 1.4119e-10
 76/128 [================>.............] - ETA: 0s - loss: 2.9985e-10
112/128 [=========================>....] - ETA: 0s - loss: 4.5587e-10
128/128 [==============================] - 0s 1ms/step - loss: 4.1048e-10
    1129. 

  1129. Epoch 7/10
    1130. 

  1130. 
  1/128 [..............................] - ETA: 0s - loss: 4.1774e-11
 39/128 [========>.....................] - ETA: 0s - loss: 3.3577e-10
 78/128 [=================>............] - ETA: 0s - loss: 2.7028e-10
116/128 [==========================>...] - ETA: 0s - loss: 2.0143e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.8841e-10
    1131. 

  1131. Epoch 8/10
    1132. 

  1132. 
  1/128 [..............................] - ETA: 0s - loss: 8.3003e-11
 41/128 [========>.....................] - ETA: 0s - loss: 2.0289e-10
 80/128 [=================>............] - ETA: 0s - loss: 4.6084e-10
119/128 [==========================>...] - ETA: 0s - loss: 3.9214e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.7283e-10
    1133. 

  1133. Epoch 9/10
    1134. 

  1134. 
  1/128 [..............................] - ETA: 0s - loss: 2.5384e-11
 39/128 [========>.....................] - ETA: 0s - loss: 1.8332e-10
 78/128 [=================>............] - ETA: 0s - loss: 4.6413e-10
118/128 [==========================>...] - ETA: 0s - loss: 3.3117e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.5887e-10
    1135. 

  1135. Epoch 10/10
    1136. 

  1136. 
  1/128 [..............................] - ETA: 0s - loss: 2.6712e-11
 41/128 [========>.....................] - ETA: 0s - loss: 2.8591e-10
 80/128 [=================>............] - ETA: 0s - loss: 5.0017e-10
119/128 [==========================>...] - ETA: 0s - loss: 3.6636e-10
128/128 [==============================] - 0s 1ms/step - loss: 3.4677e-10
    1137. 

  1137. -> test with GAN.predict
    1138. 

  1138. GAN tn, fp: 317, 0
    1139. 

  1139. GAN fn, tp: 0, 9
    1140. 

  1140. GAN f1 score: 1.000
    1141. 

  1141. GAN cohens kappa score: 1.000
    1142. 

  1142. 

  1143. -> test with 'LR'
    1144. 

  1144. LR tn, fp: 316, 1
    1145. 

  1145. LR fn, tp: 0, 9
    1146. 

  1146. LR f1 score: 0.947
    1147. 

  1147. LR cohens kappa score: 0.946
    1148. 

  1148. LR average precision score: 1.000
    1149. 

  1149. 

  1150. -> test with 'RF'
    1151. 

  1151. RF tn, fp: 317, 0
    1152. 

  1152. RF fn, tp: 0, 9
    1153. 

  1153. RF f1 score: 1.000
    1154. 

  1154. RF cohens kappa score: 1.000
    1155. 

  1155. 

  1156. -> test with 'GB'
    1157. 

  1157. GB tn, fp: 317, 0
    1158. 

  1158. GB fn, tp: 0, 9
    1159. 

  1159. GB f1 score: 1.000
    1160. 

  1160. GB cohens kappa score: 1.000
    1161. 

  1161. 

  1162. -> test with 'KNN'
    1163. 

  1163. KNN tn, fp: 316, 1
    1164. 

  1164. KNN fn, tp: 0, 9
    1165. 

  1165. KNN f1 score: 0.947
    1166. 

  1166. KNN cohens kappa score: 0.946
    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 1229 synthetic samples
    1177. 

  1177. -> retrain GAN for predict
    1178. 

  1178. Epoch 1/10
    1179. 

  1179. 
  1/128 [..............................] - ETA: 22s - loss: 4.0080e-10
 41/128 [========>.....................] - ETA: 0s - loss: 1.8264e-09 
 81/128 [=================>............] - ETA: 0s - loss: 4.8390e-09
120/128 [===========================>..] - ETA: 0s - loss: 9.7124e-09
128/128 [==============================] - 0s 1ms/step - loss: 9.1791e-09
    1180. 

  1180. Epoch 2/10
    1181. 

  1181. 
  1/128 [..............................] - ETA: 0s - loss: 6.4131e-10
 41/128 [========>.....................] - ETA: 0s - loss: 4.8787e-10
 80/128 [=================>............] - ETA: 0s - loss: 1.2522e-09
119/128 [==========================>...] - ETA: 0s - loss: 1.3875e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.3077e-09
    1182. 

  1182. Epoch 3/10
    1183. 

  1183. 
  1/128 [..............................] - ETA: 0s - loss: 8.1106e-11
 39/128 [========>.....................] - ETA: 0s - loss: 1.4337e-10
 78/128 [=================>............] - ETA: 0s - loss: 9.5434e-10
117/128 [==========================>...] - ETA: 0s - loss: 1.2678e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.1758e-09
    1184. 

  1184. Epoch 4/10
    1185. 

  1185. 
  1/128 [..............................] - ETA: 0s - loss: 1.0416e-10
 40/128 [========>.....................] - ETA: 0s - loss: 1.5282e-10
 78/128 [=================>............] - ETA: 0s - loss: 8.0497e-10
114/128 [=========================>....] - ETA: 0s - loss: 1.1848e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.0740e-09
    1186. 

  1186. Epoch 5/10
    1187. 

  1187. 
  1/128 [..............................] - ETA: 0s - loss: 1.5984e-10
 30/128 [======>.......................] - ETA: 0s - loss: 3.8958e-10
 62/128 [=============>................] - ETA: 0s - loss: 1.2021e-09
101/128 [======================>.......] - ETA: 0s - loss: 1.2183e-09
128/128 [==============================] - 0s 1ms/step - loss: 1.0013e-09
    1188. 

  1188. Epoch 6/10
    1189. 

  1189. 
  1/128 [..............................] - ETA: 0s - loss: 6.0779e-11
 41/128 [========>.....................] - ETA: 0s - loss: 1.2479e-09
 80/128 [=================>............] - ETA: 0s - loss: 6.8531e-10
119/128 [==========================>...] - ETA: 0s - loss: 1.0037e-09
128/128 [==============================] - 0s 1ms/step - loss: 9.4580e-10
    1190. 

  1190. Epoch 7/10
    1191. 

  1191. 
  1/128 [..............................] - ETA: 0s - loss: 5.3565e-11
 40/128 [========>.....................] - ETA: 0s - loss: 9.8268e-10
 78/128 [=================>............] - ETA: 0s - loss: 5.5624e-10
117/128 [==========================>...] - ETA: 0s - loss: 9.6704e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.9768e-10
    1192. 

  1192. Epoch 8/10
    1193. 

  1193. 
  1/128 [..............................] - ETA: 0s - loss: 6.2998e-11
 40/128 [========>.....................] - ETA: 0s - loss: 8.8508e-10
 80/128 [=================>............] - ETA: 0s - loss: 1.2440e-09
119/128 [==========================>...] - ETA: 0s - loss: 9.1430e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.6164e-10
    1194. 

  1194. Epoch 9/10
    1195. 

  1195. 
  1/128 [..............................] - ETA: 0s - loss: 4.6482e-11
 42/128 [========>.....................] - ETA: 0s - loss: 1.0730e-10
 85/128 [==================>...........] - ETA: 0s - loss: 7.9754e-10
126/128 [============================>.] - ETA: 0s - loss: 8.3881e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.3221e-10
    1196. 

  1196. Epoch 10/10
    1197. 

  1197. 
  1/128 [..............................] - ETA: 0s - loss: 8.5124e-11
 41/128 [========>.....................] - ETA: 0s - loss: 8.5290e-10
 80/128 [=================>............] - ETA: 0s - loss: 4.8675e-10
119/128 [==========================>...] - ETA: 0s - loss: 8.5410e-10
128/128 [==============================] - 0s 1ms/step - loss: 8.0473e-10
    1198. 

  1198. -> test with GAN.predict
    1199. 

  1199. GAN tn, fp: 318, 0
    1200. 

  1200. GAN fn, tp: 0, 11
    1201. 

  1201. GAN f1 score: 1.000
    1202. 

  1202. GAN cohens kappa score: 1.000
    1203. 

  1203. 

  1204. -> test with 'LR'
    1205. 

  1205. LR tn, fp: 318, 0
    1206. 

  1206. LR fn, tp: 0, 11
    1207. 

  1207. LR f1 score: 1.000
    1208. 

  1208. LR cohens kappa score: 1.000
    1209. 

  1209. LR average precision score: 1.000
    1210. 

  1210. 

  1211. -> test with 'RF'
    1212. 

  1212. RF tn, fp: 318, 0
    1213. 

  1213. RF fn, tp: 0, 11
    1214. 

  1214. RF f1 score: 1.000
    1215. 

  1215. RF cohens kappa score: 1.000
    1216. 

  1216. 

  1217. -> test with 'GB'
    1218. 

  1218. GB tn, fp: 318, 0
    1219. 

  1219. GB fn, tp: 0, 11
    1220. 

  1220. GB f1 score: 1.000
    1221. 

  1221. GB cohens kappa score: 1.000
    1222. 

  1222. 

  1223. -> test with 'KNN'
    1224. 

  1224. KNN tn, fp: 317, 1
    1225. 

  1225. KNN fn, tp: 0, 11
    1226. 

  1226. KNN f1 score: 0.957
    1227. 

  1227. KNN cohens kappa score: 0.955
    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 1229 synthetic samples
    1234. 

  1234. -> retrain GAN for predict
    1235. 

  1235. Epoch 1/10
    1236. 

  1236. 
  1/128 [..............................] - ETA: 21s - loss: 1.6411e-09
 34/128 [======>.......................] - ETA: 0s - loss: 8.7902e-10 
 65/128 [==============>...............] - ETA: 0s - loss: 1.5067e-04
 98/128 [=====================>........] - ETA: 0s - loss: 9.9937e-05
128/128 [==============================] - 0s 2ms/step - loss: 8.3984e-05
    1237. 

  1237. Epoch 2/10
    1238. 

  1238. 
  1/128 [..............................] - ETA: 0s - loss: 2.1529e-08
 35/128 [=======>......................] - ETA: 0s - loss: 1.1837e-05
 71/128 [===============>..............] - ETA: 0s - loss: 5.8453e-06
106/128 [=======================>......] - ETA: 0s - loss: 3.9587e-06
128/128 [==============================] - 0s 1ms/step - loss: 3.3026e-06
    1239. 

  1239. Epoch 3/10
    1240. 

  1240. 
  1/128 [..............................] - ETA: 0s - loss: 1.2219e-08
 39/128 [========>.....................] - ETA: 0s - loss: 9.7135e-08
 70/128 [===============>..............] - ETA: 0s - loss: 5.7898e-08
101/128 [======================>.......] - ETA: 0s - loss: 4.2481e-08
128/128 [==============================] - 0s 1ms/step - loss: 2.5155e-06
    1241. 

  1241. Epoch 4/10
    1242. 

  1242. 
  1/128 [..............................] - ETA: 0s - loss: 2.0929e-08
 34/128 [======>.......................] - ETA: 0s - loss: 7.7107e-06
 66/128 [==============>...............] - ETA: 0s - loss: 3.9755e-06
103/128 [=======================>......] - ETA: 0s - loss: 2.5490e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.0665e-06
    1243. 

  1243. Epoch 5/10
    1244. 

  1244. 
  1/128 [..............................] - ETA: 0s - loss: 4.4965e-09
 35/128 [=======>......................] - ETA: 0s - loss: 5.9240e-08
 65/128 [==============>...............] - ETA: 0s - loss: 3.3268e-06
 99/128 [======================>.......] - ETA: 0s - loss: 2.1868e-06
128/128 [==============================] - 0s 2ms/step - loss: 1.7039e-06
    1245. 

  1245. Epoch 6/10
    1246. 

  1246. 
  1/128 [..............................] - ETA: 0s - loss: 3.7834e-09
 40/128 [========>.....................] - ETA: 0s - loss: 4.5539e-06
 79/128 [=================>............] - ETA: 0s - loss: 2.3072e-06
117/128 [==========================>...] - ETA: 0s - loss: 1.5591e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.4452e-06
    1247. 

  1247. Epoch 7/10
    1248. 

  1248. 
  1/128 [..............................] - ETA: 0s - loss: 1.5330e-09
 39/128 [========>.....................] - ETA: 0s - loss: 3.5080e-08
 78/128 [=================>............] - ETA: 0s - loss: 1.8543e-08
117/128 [==========================>...] - ETA: 0s - loss: 1.3491e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.2421e-06
    1249. 

  1249. Epoch 8/10
    1250. 

  1250. 
  1/128 [..............................] - ETA: 0s - loss: 1.5154e-09
 40/128 [========>.....................] - ETA: 0s - loss: 2.7587e-08
 79/128 [=================>............] - ETA: 0s - loss: 1.5720e-08
117/128 [==========================>...] - ETA: 0s - loss: 1.1589e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.0671e-06
    1251. 

  1251. Epoch 9/10
    1252. 

  1252. 
  1/128 [..............................] - ETA: 0s - loss: 1.5924e-09
 38/128 [=======>......................] - ETA: 0s - loss: 2.8276e-09
 76/128 [================>.............] - ETA: 0s - loss: 1.5488e-06
114/128 [=========================>....] - ETA: 0s - loss: 1.0339e-06
128/128 [==============================] - 0s 1ms/step - loss: 9.2752e-07
    1253. 

  1253. Epoch 10/10
    1254. 

  1254. 
  1/128 [..............................] - ETA: 0s - loss: 8.0993e-10
 39/128 [========>.....................] - ETA: 0s - loss: 1.9121e-09
 78/128 [=================>............] - ETA: 0s - loss: 1.3091e-06
117/128 [==========================>...] - ETA: 0s - loss: 8.7942e-07
128/128 [==============================] - 0s 1ms/step - loss: 8.0971e-07
    1255. 

  1255. -> test with GAN.predict
    1256. 

  1256. GAN tn, fp: 318, 0
    1257. 

  1257. GAN fn, tp: 0, 11
    1258. 

  1258. GAN f1 score: 1.000
    1259. 

  1259. GAN cohens kappa score: 1.000
    1260. 

  1260. 

  1261. -> test with 'LR'
    1262. 

  1262. LR tn, fp: 318, 0
    1263. 

  1263. LR fn, tp: 0, 11
    1264. 

  1264. LR f1 score: 1.000
    1265. 

  1265. LR cohens kappa score: 1.000
    1266. 

  1266. LR average precision score: 1.000
    1267. 

  1267. 

  1268. -> test with 'RF'
    1269. 

  1269. RF tn, fp: 318, 0
    1270. 

  1270. RF fn, tp: 0, 11
    1271. 

  1271. RF f1 score: 1.000
    1272. 

  1272. RF cohens kappa score: 1.000
    1273. 

  1273. 

  1274. -> test with 'GB'
    1275. 

  1275. GB tn, fp: 318, 0
    1276. 

  1276. GB fn, tp: 0, 11
    1277. 

  1277. GB f1 score: 1.000
    1278. 

  1278. GB cohens kappa score: 1.000
    1279. 

  1279. 

  1280. -> test with 'KNN'
    1281. 

  1281. KNN tn, fp: 318, 0
    1282. 

  1282. KNN fn, tp: 0, 11
    1283. 

  1283. KNN f1 score: 1.000
    1284. 

  1284. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    1291. 

  1291. -> retrain GAN for predict
    1292. 

  1292. Epoch 1/10
    1293. 

  1293. 
  1/128 [..............................] - ETA: 20s - loss: 3.1947e-10
 40/128 [========>.....................] - ETA: 0s - loss: 4.5475e-09 
 78/128 [=================>............] - ETA: 0s - loss: 1.9018e-04
116/128 [==========================>...] - ETA: 0s - loss: 1.2915e-04
128/128 [==============================] - 0s 1ms/step - loss: 1.1787e-04
    1294. 

  1294. Epoch 2/10
    1295. 

  1295. 
  1/128 [..............................] - ETA: 0s - loss: 3.7418e-10
 34/128 [======>.......................] - ETA: 0s - loss: 4.3054e-10
 72/128 [===============>..............] - ETA: 0s - loss: 7.4109e-06
111/128 [=========================>....] - ETA: 0s - loss: 4.8561e-06
128/128 [==============================] - 0s 1ms/step - loss: 4.2425e-06
    1296. 

  1296. Epoch 3/10
    1297. 

  1297. 
  1/128 [..............................] - ETA: 0s - loss: 8.8633e-08
 41/128 [========>.....................] - ETA: 0s - loss: 4.0523e-09
 79/128 [=================>............] - ETA: 0s - loss: 2.7412e-09
119/128 [==========================>...] - ETA: 0s - loss: 3.0255e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.8327e-06
    1298. 

  1298. Epoch 4/10
    1299. 

  1299. 
  1/128 [..............................] - ETA: 0s - loss: 1.6961e-09
 41/128 [========>.....................] - ETA: 0s - loss: 8.9487e-08
 81/128 [=================>............] - ETA: 0s - loss: 3.4289e-06
120/128 [===========================>..] - ETA: 0s - loss: 2.3159e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.1871e-06
    1300. 

  1300. Epoch 5/10
    1301. 

  1301. 
  1/128 [..............................] - ETA: 0s - loss: 2.2798e-09
 40/128 [========>.....................] - ETA: 0s - loss: 5.6448e-06
 79/128 [=================>............] - ETA: 0s - loss: 2.8604e-06
118/128 [==========================>...] - ETA: 0s - loss: 1.9176e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.7821e-06
    1302. 

  1302. Epoch 6/10
    1303. 

  1303. 
  1/128 [..............................] - ETA: 0s - loss: 1.3521e-09
 40/128 [========>.....................] - ETA: 0s - loss: 4.7024e-06
 70/128 [===============>..............] - ETA: 0s - loss: 2.6898e-06
103/128 [=======================>......] - ETA: 0s - loss: 1.8327e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.5006e-06
    1304. 

  1304. Epoch 7/10
    1305. 

  1305. 
  1/128 [..............................] - ETA: 0s - loss: 2.3765e-09
 39/128 [========>.....................] - ETA: 0s - loss: 5.3600e-09
 79/128 [=================>............] - ETA: 0s - loss: 2.0504e-06
118/128 [==========================>...] - ETA: 0s - loss: 1.3883e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.2894e-06
    1306. 

  1306. Epoch 8/10
    1307. 

  1307. 
  1/128 [..............................] - ETA: 0s - loss: 2.2524e-09
 40/128 [========>.....................] - ETA: 0s - loss: 3.9079e-08
 79/128 [=================>............] - ETA: 0s - loss: 2.1669e-08
118/128 [==========================>...] - ETA: 0s - loss: 1.6300e-08
128/128 [==============================] - 0s 1ms/step - loss: 1.1113e-06
    1308. 

  1308. Epoch 9/10
    1309. 

  1309. 
  1/128 [..............................] - ETA: 0s - loss: 3.7317e-10
 40/128 [========>.....................] - ETA: 0s - loss: 3.0849e-06
 78/128 [=================>............] - ETA: 0s - loss: 1.5989e-06
117/128 [==========================>...] - ETA: 0s - loss: 1.0695e-06
128/128 [==============================] - 0s 1ms/step - loss: 9.8465e-07
    1310. 

  1310. Epoch 10/10
    1311. 

  1311. 
  1/128 [..............................] - ETA: 0s - loss: 3.2008e-10
 41/128 [========>.....................] - ETA: 0s - loss: 5.4109e-09
 80/128 [=================>............] - ETA: 0s - loss: 1.3040e-06
120/128 [===========================>..] - ETA: 0s - loss: 8.7901e-07
128/128 [==============================] - 0s 1ms/step - loss: 8.3001e-07
    1312. 

  1312. -> test with GAN.predict
    1313. 

  1313. GAN tn, fp: 318, 0
    1314. 

  1314. GAN fn, tp: 0, 11
    1315. 

  1315. GAN f1 score: 1.000
    1316. 

  1316. GAN cohens kappa score: 1.000
    1317. 

  1317. 

  1318. -> test with 'LR'
    1319. 

  1319. LR tn, fp: 318, 0
    1320. 

  1320. LR fn, tp: 0, 11
    1321. 

  1321. LR f1 score: 1.000
    1322. 

  1322. LR cohens kappa score: 1.000
    1323. 

  1323. LR average precision score: 1.000
    1324. 

  1324. 

  1325. -> test with 'RF'
    1326. 

  1326. RF tn, fp: 318, 0
    1327. 

  1327. RF fn, tp: 0, 11
    1328. 

  1328. RF f1 score: 1.000
    1329. 

  1329. RF cohens kappa score: 1.000
    1330. 

  1330. 

  1331. -> test with 'GB'
    1332. 

  1332. GB tn, fp: 318, 0
    1333. 

  1333. GB fn, tp: 0, 11
    1334. 

  1334. GB f1 score: 1.000
    1335. 

  1335. GB cohens kappa score: 1.000
    1336. 

  1336. 

  1337. -> test with 'KNN'
    1338. 

  1338. KNN tn, fp: 318, 0
    1339. 

  1339. KNN fn, tp: 0, 11
    1340. 

  1340. KNN f1 score: 1.000
    1341. 

  1341. KNN cohens kappa score: 1.000
    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 1229 synthetic samples
    1348. 

  1348. -> retrain GAN for predict
    1349. 

  1349. Epoch 1/10
    1350. 

  1350. 
  1/128 [..............................] - ETA: 21s - loss: 1.4707e-09
 41/128 [========>.....................] - ETA: 0s - loss: 3.5333e-04 
 79/128 [=================>............] - ETA: 0s - loss: 1.8340e-04
119/128 [==========================>...] - ETA: 0s - loss: 1.4413e-04
128/128 [==============================] - 0s 1ms/step - loss: 1.3494e-04
    1351. 

  1351. Epoch 2/10
    1352. 

  1352. 
  1/128 [..............................] - ETA: 0s - loss: 3.0647e-09
 41/128 [========>.....................] - ETA: 0s - loss: 2.5371e-09
 79/128 [=================>............] - ETA: 0s - loss: 1.0121e-05
117/128 [==========================>...] - ETA: 0s - loss: 7.3757e-06
128/128 [==============================] - 0s 1ms/step - loss: 6.7898e-06
    1353. 

  1353. Epoch 3/10
    1354. 

  1354. 
  1/128 [..............................] - ETA: 0s - loss: 2.8150e-09
 40/128 [========>.....................] - ETA: 0s - loss: 2.4452e-09
 77/128 [=================>............] - ETA: 0s - loss: 6.6043e-06
115/128 [=========================>....] - ETA: 0s - loss: 4.4229e-06
128/128 [==============================] - 0s 1ms/step - loss: 4.0021e-06
    1355. 

  1355. Epoch 4/10
    1356. 

  1356. 
  1/128 [..............................] - ETA: 0s - loss: 3.8317e-09
 39/128 [========>.....................] - ETA: 0s - loss: 2.6589e-09
 75/128 [================>.............] - ETA: 0s - loss: 4.1072e-07
113/128 [=========================>....] - ETA: 0s - loss: 3.2011e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.8463e-06
    1357. 

  1357. Epoch 5/10
    1358. 

  1358. 
  1/128 [..............................] - ETA: 0s - loss: 2.8884e-09
 40/128 [========>.....................] - ETA: 0s - loss: 3.2848e-09
 79/128 [=================>............] - ETA: 0s - loss: 3.2097e-06
120/128 [===========================>..] - ETA: 0s - loss: 2.3030e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.1747e-06
    1359. 

  1359. Epoch 6/10
    1360. 

  1360. 
  1/128 [..............................] - ETA: 0s - loss: 5.1654e-09
 42/128 [========>.....................] - ETA: 0s - loss: 5.0876e-07
 88/128 [===================>..........] - ETA: 0s - loss: 2.5113e-06
111/128 [=========================>....] - ETA: 0s - loss: 1.9922e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.7406e-06
    1361. 

  1361. Epoch 7/10
    1362. 

  1362. 
  1/128 [..............................] - ETA: 0s - loss: 6.8942e-09
 44/128 [=========>....................] - ETA: 0s - loss: 5.7513e-09
 89/128 [===================>..........] - ETA: 0s - loss: 1.8562e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.4364e-06
    1363. 

  1363. Epoch 8/10
    1364. 

  1364. 
  1/128 [..............................] - ETA: 0s - loss: 6.4276e-09
 41/128 [========>.....................] - ETA: 0s - loss: 6.7222e-09
 79/128 [=================>............] - ETA: 0s - loss: 2.1330e-07
125/128 [============================>.] - ETA: 0s - loss: 1.2377e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.2174e-06
    1365. 

  1365. Epoch 9/10
    1366. 

  1366. 
  1/128 [..............................] - ETA: 0s - loss: 6.3452e-09
 43/128 [=========>....................] - ETA: 0s - loss: 3.5350e-07
 84/128 [==================>...........] - ETA: 0s - loss: 1.8407e-07
120/128 [===========================>..] - ETA: 0s - loss: 1.1184e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.0563e-06
    1367. 

  1367. Epoch 10/10
    1368. 

  1368. 
  1/128 [..............................] - ETA: 0s - loss: 6.0887e-09
 39/128 [========>.....................] - ETA: 0s - loss: 5.9798e-09
 78/128 [=================>............] - ETA: 0s - loss: 1.3305e-06
119/128 [==========================>...] - ETA: 0s - loss: 9.8294e-07
128/128 [==============================] - 0s 1ms/step - loss: 9.2066e-07
    1369. 

  1369. -> test with GAN.predict
    1370. 

  1370. GAN tn, fp: 318, 0
    1371. 

  1371. GAN fn, tp: 0, 11
    1372. 

  1372. GAN f1 score: 1.000
    1373. 

  1373. GAN cohens kappa score: 1.000
    1374. 

  1374. 

  1375. -> test with 'LR'
    1376. 

  1376. LR tn, fp: 318, 0
    1377. 

  1377. LR fn, tp: 0, 11
    1378. 

  1378. LR f1 score: 1.000
    1379. 

  1379. LR cohens kappa score: 1.000
    1380. 

  1380. LR average precision score: 1.000
    1381. 

  1381. 

  1382. -> test with 'RF'
    1383. 

  1383. RF tn, fp: 318, 0
    1384. 

  1384. RF fn, tp: 0, 11
    1385. 

  1385. RF f1 score: 1.000
    1386. 

  1386. RF cohens kappa score: 1.000
    1387. 

  1387. 

  1388. -> test with 'GB'
    1389. 

  1389. GB tn, fp: 318, 0
    1390. 

  1390. GB fn, tp: 0, 11
    1391. 

  1391. GB f1 score: 1.000
    1392. 

  1392. GB cohens kappa score: 1.000
    1393. 

  1393. 

  1394. -> test with 'KNN'
    1395. 

  1395. KNN tn, fp: 318, 0
    1396. 

  1396. KNN fn, tp: 0, 11
    1397. 

  1397. KNN f1 score: 1.000
    1398. 

  1398. KNN cohens kappa score: 1.000
    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 1228 synthetic samples
    1405. 

  1405. -> retrain GAN for predict
    1406. 

  1406. Epoch 1/10
    1407. 

  1407. 
  1/128 [..............................] - ETA: 22s - loss: 2.6818e-10
 36/128 [=======>......................] - ETA: 0s - loss: 9.0138e-10 
 67/128 [==============>...............] - ETA: 0s - loss: 0.0762    
102/128 [======================>.......] - ETA: 0s - loss: 0.0501
128/128 [==============================] - 0s 1ms/step - loss: 0.0401
    1408. 

  1408. Epoch 2/10
    1409. 

  1409. 
  1/128 [..............................] - ETA: 0s - loss: 6.2488e-06
 38/128 [=======>......................] - ETA: 0s - loss: 4.3737e-06
 73/128 [================>.............] - ETA: 0s - loss: 4.9266e-06
108/128 [========================>.....] - ETA: 0s - loss: 4.3684e-06
128/128 [==============================] - 0s 1ms/step - loss: 4.0713e-06
    1410. 

  1410. Epoch 3/10
    1411. 

  1411. 
  1/128 [..............................] - ETA: 0s - loss: 1.1505e-06
 38/128 [=======>......................] - ETA: 0s - loss: 3.8408e-06
 76/128 [================>.............] - ETA: 0s - loss: 2.9102e-06
109/128 [========================>.....] - ETA: 0s - loss: 2.6641e-06
128/128 [==============================] - 0s 1ms/step - loss: 2.5313e-06
    1412. 

  1412. Epoch 4/10
    1413. 

  1413. 
  1/128 [..............................] - ETA: 0s - loss: 1.1570e-06
 35/128 [=======>......................] - ETA: 0s - loss: 2.3247e-06
 69/128 [===============>..............] - ETA: 0s - loss: 1.8012e-06
104/128 [=======================>......] - ETA: 0s - loss: 1.9093e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.9138e-06
    1414. 

  1414. Epoch 5/10
    1415. 

  1415. 
  1/128 [..............................] - ETA: 0s - loss: 9.7444e-07
 34/128 [======>.......................] - ETA: 0s - loss: 2.2908e-06
 69/128 [===============>..............] - ETA: 0s - loss: 1.7457e-06
103/128 [=======================>......] - ETA: 0s - loss: 1.4618e-06
128/128 [==============================] - 0s 2ms/step - loss: 1.5287e-06
    1416. 

  1416. Epoch 6/10
    1417. 

  1417. 
  1/128 [..............................] - ETA: 0s - loss: 8.9771e-06
 35/128 [=======>......................] - ETA: 0s - loss: 2.0051e-06
 72/128 [===============>..............] - ETA: 0s - loss: 1.3553e-06
105/128 [=======================>......] - ETA: 0s - loss: 1.1576e-06
128/128 [==============================] - 0s 1ms/step - loss: 1.0503e-06
    1418. 

  1418. Epoch 7/10
    1419. 

  1419. 
  1/128 [..............................] - ETA: 0s - loss: 2.3777e-07
 36/128 [=======>......................] - ETA: 0s - loss: 6.0123e-07
 72/128 [===============>..............] - ETA: 0s - loss: 4.9694e-07
107/128 [========================>.....] - ETA: 0s - loss: 3.9743e-07
128/128 [==============================] - 0s 1ms/step - loss: 3.7616e-07
    1420. 

  1420. Epoch 8/10
    1421. 

  1421. 
  1/128 [..............................] - ETA: 0s - loss: 1.5518e-07
 34/128 [======>.......................] - ETA: 0s - loss: 1.6705e-07
 66/128 [==============>...............] - ETA: 0s - loss: 1.7814e-07
103/128 [=======================>......] - ETA: 0s - loss: 1.7018e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.8248e-07
    1422. 

  1422. Epoch 9/10
    1423. 

  1423. 
  1/128 [..............................] - ETA: 0s - loss: 6.4778e-08
 36/128 [=======>......................] - ETA: 0s - loss: 1.0797e-07
 72/128 [===============>..............] - ETA: 0s - loss: 1.2320e-07
108/128 [========================>.....] - ETA: 0s - loss: 1.2322e-07
128/128 [==============================] - 0s 1ms/step - loss: 1.1478e-07
    1424. 

  1424. Epoch 10/10
    1425. 

  1425. 
  1/128 [..............................] - ETA: 0s - loss: 5.3125e-08
 35/128 [=======>......................] - ETA: 0s - loss: 9.2837e-08
 59/128 [============>.................] - ETA: 0s - loss: 8.8362e-08
 85/128 [==================>...........] - ETA: 0s - loss: 7.7919e-08
113/128 [=========================>....] - ETA: 0s - loss: 7.5708e-08
128/128 [==============================] - 0s 2ms/step - loss: 8.0943e-08
    1426. 

  1426. -> test with GAN.predict
    1427. 

  1427. GAN tn, fp: 317, 0
    1428. 

  1428. GAN fn, tp: 0, 9
    1429. 

  1429. GAN f1 score: 1.000
    1430. 

  1430. GAN cohens kappa score: 1.000
    1431. 

  1431. 

  1432. -> test with 'LR'
    1433. 

  1433. LR tn, fp: 317, 0
    1434. 

  1434. LR fn, tp: 0, 9
    1435. 

  1435. LR f1 score: 1.000
    1436. 

  1436. LR cohens kappa score: 1.000
    1437. 

  1437. LR average precision score: 1.000
    1438. 

  1438. 

  1439. -> test with 'RF'
    1440. 

  1440. RF tn, fp: 317, 0
    1441. 

  1441. RF fn, tp: 0, 9
    1442. 

  1442. RF f1 score: 1.000
    1443. 

  1443. RF cohens kappa score: 1.000
    1444. 

  1444. 

  1445. -> test with 'GB'
    1446. 

  1446. GB tn, fp: 317, 0
    1447. 

  1447. GB fn, tp: 0, 9
    1448. 

  1448. GB f1 score: 1.000
    1449. 

  1449. GB cohens kappa score: 1.000
    1450. 

  1450. 

  1451. -> test with 'KNN'
    1452. 

  1452. KNN tn, fp: 317, 0
    1453. 

  1453. KNN fn, tp: 0, 9
    1454. 

  1454. KNN f1 score: 1.000
    1455. 

  1455. KNN cohens kappa score: 1.000
    1456. 

  1456. 

  1457. ### Exercise is done.
    1458. 

  1458. 

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

  1460. maximum:
    1461. 

  1461. LR tn, fp: 318, 1
    1462. 

  1462. LR fn, tp: 0, 11
    1463. 

  1463. LR f1 score: 1.000
    1464. 

  1464. LR cohens kappa score: 1.000
    1465. 

  1465. LR average precision score: 1.000
    1466. 

  1466. 

  1467. 

  1468. average:
    1469. 

  1469. LR tn, fp: 317.76, 0.04
    1470. 

  1470. LR fn, tp: 0.0, 10.6
    1471. 

  1471. LR f1 score: 0.998
    1472. 

  1472. LR cohens kappa score: 0.998
    1473. 

  1473. LR average precision score: 1.000
    1474. 

  1474. 

  1475. 

  1476. minimum:
    1477. 

  1477. LR tn, fp: 316, 0
    1478. 

  1478. LR fn, tp: 0, 9
    1479. 

  1479. LR f1 score: 0.947
    1480. 

  1480. LR cohens kappa score: 0.946
    1481. 

  1481. LR average precision score: 1.000
    1482. 

  1482. 

  1483. 

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

  1485. maximum:
    1486. 

  1486. RF tn, fp: 318, 0
    1487. 

  1487. RF fn, tp: 0, 11
    1488. 

  1488. RF f1 score: 1.000
    1489. 

  1489. RF cohens kappa score: 1.000
    1490. 

  1490. 

  1491. 

  1492. average:
    1493. 

  1493. RF tn, fp: 317.8, 0.0
    1494. 

  1494. RF fn, tp: 0.0, 10.6
    1495. 

  1495. RF f1 score: 1.000
    1496. 

  1496. RF cohens kappa score: 1.000
    1497. 

  1497. 

  1498. 

  1499. minimum:
    1500. 

  1500. RF tn, fp: 317, 0
    1501. 

  1501. RF fn, tp: 0, 9
    1502. 

  1502. RF f1 score: 1.000
    1503. 

  1503. RF cohens kappa score: 1.000
    1504. 

  1504. 

  1505. 

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

  1507. maximum:
    1508. 

  1508. GB tn, fp: 318, 0
    1509. 

  1509. GB fn, tp: 0, 11
    1510. 

  1510. GB f1 score: 1.000
    1511. 

  1511. GB cohens kappa score: 1.000
    1512. 

  1512. 

  1513. 

  1514. average:
    1515. 

  1515. GB tn, fp: 317.8, 0.0
    1516. 

  1516. GB fn, tp: 0.0, 10.6
    1517. 

  1517. GB f1 score: 1.000
    1518. 

  1518. GB cohens kappa score: 1.000
    1519. 

  1519. 

  1520. 

  1521. minimum:
    1522. 

  1522. GB tn, fp: 317, 0
    1523. 

  1523. GB fn, tp: 0, 9
    1524. 

  1524. GB f1 score: 1.000
    1525. 

  1525. GB cohens kappa score: 1.000
    1526. 

  1526. 

  1527. 

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

  1529. maximum:
    1530. 

  1530. KNN tn, fp: 318, 1
    1531. 

  1531. KNN fn, tp: 0, 11
    1532. 

  1532. KNN f1 score: 1.000
    1533. 

  1533. KNN cohens kappa score: 1.000
    1534. 

  1534. 

  1535. 

  1536. average:
    1537. 

  1537. KNN tn, fp: 317.6, 0.2
    1538. 

  1538. KNN fn, tp: 0.0, 10.6
    1539. 

  1539. KNN f1 score: 0.991
    1540. 

  1540. KNN cohens kappa score: 0.991
    1541. 

  1541. 

  1542. 

  1543. minimum:
    1544. 

  1544. KNN tn, fp: 316, 0
    1545. 

  1545. KNN fn, tp: 0, 9
    1546. 

  1546. KNN f1 score: 0.947
    1547. 

  1547. KNN cohens kappa score: 0.946
    1548. 

  1548. 

  1549. 

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

  1551. maximum:
    1552. 

  1552. GAN tn, fp: 318, 0
    1553. 

  1553. GAN fn, tp: 0, 11
    1554. 

  1554. GAN f1 score: 1.000
    1555. 

  1555. GAN cohens kappa score: 1.000
    1556. 

  1556. 

  1557. 

  1558. average:
    1559. 

  1559. GAN tn, fp: 317.8, 0.0
    1560. 

  1560. GAN fn, tp: 0.0, 10.6
    1561. 

  1561. GAN f1 score: 1.000
    1562. 

  1562. GAN cohens kappa score: 1.000
    1563. 

  1563. 

  1564. 

  1565. minimum:
    1566. 

  1566. GAN tn, fp: 317, 0
    1567. 

  1567. GAN fn, tp: 0, 9
    1568. 

  1568. GAN f1 score: 1.000
    1569. 

  1569. GAN cohens kappa score: 1.000
    1570. 

  1570.