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CTAB-GAN
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folding_kddcup-guess_passwd_vs_satan.log

folding_kddcup-guess_passwd_vs_satan.log 30 KB
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  1. 

  2. 

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

  4. // Running CTAB-GAN 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. 
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    21. 

  21. -> create 1229 synthetic samples
    22. 

  22. -> test with 'LR'
    23. 

  23. LR tn, fp: 318, 0
    24. 

  24. LR fn, tp: 0, 11
    25. 

  25. LR f1 score: 1.000
    26. 

  26. LR cohens kappa score: 1.000
    27. 

  27. LR average precision score: 1.000
    28. 

  28. 

  29. -> test with 'GB'
    30. 

  30. GB tn, fp: 318, 0
    31. 

  31. GB fn, tp: 0, 11
    32. 

  32. GB f1 score: 1.000
    33. 

  33. GB cohens kappa score: 1.000
    34. 

  34. 

  35. -> test with 'KNN'
    36. 

  36. KNN tn, fp: 318, 0
    37. 

  37. KNN fn, tp: 0, 11
    38. 

  38. KNN f1 score: 1.000
    39. 

  39. KNN cohens kappa score: 1.000
    40. 

  40. 

  41. 

  42. ------ Step 1/5: Slice 2/5 -------
    43. 

  43. -> Reset the GAN
    44. 

  44. -> Train generator for synthetic samples
    45. 

  45. 
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    46. 

  46. -> create 1229 synthetic samples
    47. 

  47. -> test with 'LR'
    48. 

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

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

  50. LR f1 score: 1.000
    51. 

  51. LR cohens kappa score: 1.000
    52. 

  52. LR average precision score: 1.000
    53. 

  53. 

  54. -> test with 'GB'
    55. 

  55. GB tn, fp: 318, 0
    56. 

  56. GB fn, tp: 0, 11
    57. 

  57. GB f1 score: 1.000
    58. 

  58. GB cohens kappa score: 1.000
    59. 

  59. 

  60. -> test with 'KNN'
    61. 

  61. KNN tn, fp: 318, 0
    62. 

  62. KNN fn, tp: 0, 11
    63. 

  63. KNN f1 score: 1.000
    64. 

  64. KNN cohens kappa score: 1.000
    65. 

  65. 

  66. 

  67. ------ Step 1/5: Slice 3/5 -------
    68. 

  68. -> Reset the GAN
    69. 

  69. -> Train generator for synthetic samples
    70. 

  70. 
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    71. 

  71. -> create 1229 synthetic samples
    72. 

  72. -> test with 'LR'
    73. 

  73. LR tn, fp: 318, 0
    74. 

  74. LR fn, tp: 0, 11
    75. 

  75. LR f1 score: 1.000
    76. 

  76. LR cohens kappa score: 1.000
    77. 

  77. LR average precision score: 1.000
    78. 

  78. 

  79. -> test with 'GB'
    80. 

  80. GB tn, fp: 318, 0
    81. 

  81. GB fn, tp: 0, 11
    82. 

  82. GB f1 score: 1.000
    83. 

  83. GB cohens kappa score: 1.000
    84. 

  84. 

  85. -> test with 'KNN'
    86. 

  86. KNN tn, fp: 317, 1
    87. 

  87. KNN fn, tp: 0, 11
    88. 

  88. KNN f1 score: 0.957
    89. 

  89. KNN cohens kappa score: 0.955
    90. 

  90. 

  91. 

  92. ------ Step 1/5: Slice 4/5 -------
    93. 

  93. -> Reset the GAN
    94. 

  94. -> Train generator for synthetic samples
    95. 

  95. 
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    96. 

  96. -> create 1229 synthetic samples
    97. 

  97. -> test with 'LR'
    98. 

  98. LR tn, fp: 318, 0
    99. 

  99. LR fn, tp: 0, 11
    100. 

  100. LR f1 score: 1.000
    101. 

  101. LR cohens kappa score: 1.000
    102. 

  102. LR average precision score: 1.000
    103. 

  103. 

  104. -> test with 'GB'
    105. 

  105. GB tn, fp: 318, 0
    106. 

  106. GB fn, tp: 0, 11
    107. 

  107. GB f1 score: 1.000
    108. 

  108. GB cohens kappa score: 1.000
    109. 

  109. 

  110. -> test with 'KNN'
    111. 

  111. KNN tn, fp: 318, 0
    112. 

  112. KNN fn, tp: 0, 11
    113. 

  113. KNN f1 score: 1.000
    114. 

  114. KNN cohens kappa score: 1.000
    115. 

  115. 

  116. 

  117. ------ Step 1/5: Slice 5/5 -------
    118. 

  118. -> Reset the GAN
    119. 

  119. -> Train generator for synthetic samples
    120. 

  120. 
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    121. 

  121. -> create 1228 synthetic samples
    122. 

  122. -> test with 'LR'
    123. 

  123. LR tn, fp: 317, 0
    124. 

  124. LR fn, tp: 0, 9
    125. 

  125. LR f1 score: 1.000
    126. 

  126. LR cohens kappa score: 1.000
    127. 

  127. LR average precision score: 1.000
    128. 

  128. 

  129. -> test with 'GB'
    130. 

  130. GB tn, fp: 317, 0
    131. 

  131. GB fn, tp: 0, 9
    132. 

  132. GB f1 score: 1.000
    133. 

  133. GB cohens kappa score: 1.000
    134. 

  134. 

  135. -> test with 'KNN'
    136. 

  136. KNN tn, fp: 317, 0
    137. 

  137. KNN fn, tp: 0, 9
    138. 

  138. KNN f1 score: 1.000
    139. 

  139. KNN cohens kappa score: 1.000
    140. 

  140. 

  141. 

  142. ====== Step 2/5 =======
    143. 

  143. -> Shuffling data
    144. 

  144. -> Spliting data to slices
    145. 

  145. 

  146. ------ Step 2/5: Slice 1/5 -------
    147. 

  147. -> Reset the GAN
    148. 

  148. -> Train generator for synthetic samples
    149. 

  149. 
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    150. 

  150. -> create 1229 synthetic samples
    151. 

  151. -> test with 'LR'
    152. 

  152. LR tn, fp: 318, 0
    153. 

  153. LR fn, tp: 0, 11
    154. 

  154. LR f1 score: 1.000
    155. 

  155. LR cohens kappa score: 1.000
    156. 

  156. LR average precision score: 1.000
    157. 

  157. 

  158. -> test with 'GB'
    159. 

  159. GB tn, fp: 318, 0
    160. 

  160. GB fn, tp: 0, 11
    161. 

  161. GB f1 score: 1.000
    162. 

  162. GB cohens kappa score: 1.000
    163. 

  163. 

  164. -> test with 'KNN'
    165. 

  165. KNN tn, fp: 318, 0
    166. 

  166. KNN fn, tp: 0, 11
    167. 

  167. KNN f1 score: 1.000
    168. 

  168. KNN cohens kappa score: 1.000
    169. 

  169. 

  170. 

  171. ------ Step 2/5: Slice 2/5 -------
    172. 

  172. -> Reset the GAN
    173. 

  173. -> Train generator for synthetic samples
    174. 

  174. 
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    175. 

  175. -> create 1229 synthetic samples
    176. 

  176. -> test with 'LR'
    177. 

  177. LR tn, fp: 318, 0
    178. 

  178. LR fn, tp: 0, 11
    179. 

  179. LR f1 score: 1.000
    180. 

  180. LR cohens kappa score: 1.000
    181. 

  181. LR average precision score: 1.000
    182. 

  182. 

  183. -> test with 'GB'
    184. 

  184. GB tn, fp: 318, 0
    185. 

  185. GB fn, tp: 0, 11
    186. 

  186. GB f1 score: 1.000
    187. 

  187. GB cohens kappa score: 1.000
    188. 

  188. 

  189. -> test with 'KNN'
    190. 

  190. KNN tn, fp: 317, 1
    191. 

  191. KNN fn, tp: 0, 11
    192. 

  192. KNN f1 score: 0.957
    193. 

  193. KNN cohens kappa score: 0.955
    194. 

  194. 

  195. 

  196. ------ Step 2/5: Slice 3/5 -------
    197. 

  197. -> Reset the GAN
    198. 

  198. -> Train generator for synthetic samples
    199. 

  199. 
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    200. 

  200. -> create 1229 synthetic samples
    201. 

  201. -> test with 'LR'
    202. 

  202. LR tn, fp: 318, 0
    203. 

  203. LR fn, tp: 0, 11
    204. 

  204. LR f1 score: 1.000
    205. 

  205. LR cohens kappa score: 1.000
    206. 

  206. LR average precision score: 1.000
    207. 

  207. 

  208. -> test with 'GB'
    209. 

  209. GB tn, fp: 318, 0
    210. 

  210. GB fn, tp: 0, 11
    211. 

  211. GB f1 score: 1.000
    212. 

  212. GB cohens kappa score: 1.000
    213. 

  213. 

  214. -> test with 'KNN'
    215. 

  215. KNN tn, fp: 318, 0
    216. 

  216. KNN fn, tp: 0, 11
    217. 

  217. KNN f1 score: 1.000
    218. 

  218. KNN cohens kappa score: 1.000
    219. 

  219. 

  220. 

  221. ------ Step 2/5: Slice 4/5 -------
    222. 

  222. -> Reset the GAN
    223. 

  223. -> Train generator for synthetic samples
    224. 

  224. 
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    225. 

  225. -> create 1229 synthetic samples
    226. 

  226. -> test with 'LR'
    227. 

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

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

  229. LR f1 score: 1.000
    230. 

  230. LR cohens kappa score: 1.000
    231. 

  231. LR average precision score: 1.000
    232. 

  232. 

  233. -> test with 'GB'
    234. 

  234. GB tn, fp: 318, 0
    235. 

  235. GB fn, tp: 0, 11
    236. 

  236. GB f1 score: 1.000
    237. 

  237. GB cohens kappa score: 1.000
    238. 

  238. 

  239. -> test with 'KNN'
    240. 

  240. KNN tn, fp: 318, 0
    241. 

  241. KNN fn, tp: 0, 11
    242. 

  242. KNN f1 score: 1.000
    243. 

  243. KNN cohens kappa score: 1.000
    244. 

  244. 

  245. 

  246. ------ Step 2/5: Slice 5/5 -------
    247. 

  247. -> Reset the GAN
    248. 

  248. -> Train generator for synthetic samples
    249. 

  249. 
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 40%|████      | 4/10 [00:08<00:12,  2.08s/it]
 50%|█████     | 5/10 [00:10<00:10,  2.13s/it]
 60%|██████    | 6/10 [00:12<00:08,  2.16s/it]
 70%|███████   | 7/10 [00:14<00:06,  2.13s/it]
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 90%|█████████ | 9/10 [00:18<00:02,  2.06s/it]
100%|██████████| 10/10 [00:20<00:00,  2.01s/it]
100%|██████████| 10/10 [00:20<00:00,  2.08s/it]
    250. 

  250. -> create 1228 synthetic samples
    251. 

  251. -> test with 'LR'
    252. 

  252. LR tn, fp: 316, 1
    253. 

  253. LR fn, tp: 0, 9
    254. 

  254. LR f1 score: 0.947
    255. 

  255. LR cohens kappa score: 0.946
    256. 

  256. LR average precision score: 1.000
    257. 

  257. 

  258. -> test with 'GB'
    259. 

  259. GB tn, fp: 317, 0
    260. 

  260. GB fn, tp: 0, 9
    261. 

  261. GB f1 score: 1.000
    262. 

  262. GB cohens kappa score: 1.000
    263. 

  263. 

  264. -> test with 'KNN'
    265. 

  265. KNN tn, fp: 317, 0
    266. 

  266. KNN fn, tp: 0, 9
    267. 

  267. KNN f1 score: 1.000
    268. 

  268. KNN cohens kappa score: 1.000
    269. 

  269. 

  270. 

  271. ====== Step 3/5 =======
    272. 

  272. -> Shuffling data
    273. 

  273. -> Spliting data to slices
    274. 

  274. 

  275. ------ Step 3/5: Slice 1/5 -------
    276. 

  276. -> Reset the GAN
    277. 

  277. -> Train generator for synthetic samples
    278. 

  278. 
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100%|██████████| 10/10 [00:20<00:00,  2.11s/it]
100%|██████████| 10/10 [00:20<00:00,  2.05s/it]
    279. 

  279. -> create 1229 synthetic samples
    280. 

  280. -> test with 'LR'
    281. 

  281. LR tn, fp: 318, 0
    282. 

  282. LR fn, tp: 0, 11
    283. 

  283. LR f1 score: 1.000
    284. 

  284. LR cohens kappa score: 1.000
    285. 

  285. LR average precision score: 1.000
    286. 

  286. 

  287. -> test with 'GB'
    288. 

  288. GB tn, fp: 318, 0
    289. 

  289. GB fn, tp: 0, 11
    290. 

  290. GB f1 score: 1.000
    291. 

  291. GB cohens kappa score: 1.000
    292. 

  292. 

  293. -> test with 'KNN'
    294. 

  294. KNN tn, fp: 317, 1
    295. 

  295. KNN fn, tp: 0, 11
    296. 

  296. KNN f1 score: 0.957
    297. 

  297. KNN cohens kappa score: 0.955
    298. 

  298. 

  299. 

  300. ------ Step 3/5: Slice 2/5 -------
    301. 

  301. -> Reset the GAN
    302. 

  302. -> Train generator for synthetic samples
    303. 

  303. 
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100%|██████████| 10/10 [00:20<00:00,  2.01s/it]
100%|██████████| 10/10 [00:20<00:00,  2.08s/it]
    304. 

  304. -> create 1229 synthetic samples
    305. 

  305. -> test with 'LR'
    306. 

  306. LR tn, fp: 318, 0
    307. 

  307. LR fn, tp: 0, 11
    308. 

  308. LR f1 score: 1.000
    309. 

  309. LR cohens kappa score: 1.000
    310. 

  310. LR average precision score: 1.000
    311. 

  311. 

  312. -> test with 'GB'
    313. 

  313. GB tn, fp: 318, 0
    314. 

  314. GB fn, tp: 0, 11
    315. 

  315. GB f1 score: 1.000
    316. 

  316. GB cohens kappa score: 1.000
    317. 

  317. 

  318. -> test with 'KNN'
    319. 

  319. KNN tn, fp: 318, 0
    320. 

  320. KNN fn, tp: 0, 11
    321. 

  321. KNN f1 score: 1.000
    322. 

  322. KNN cohens kappa score: 1.000
    323. 

  323. 

  324. 

  325. ------ Step 3/5: Slice 3/5 -------
    326. 

  326. -> Reset the GAN
    327. 

  327. -> Train generator for synthetic samples
    328. 

  328. 
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100%|██████████| 10/10 [00:20<00:00,  2.06s/it]
100%|██████████| 10/10 [00:20<00:00,  2.09s/it]
    329. 

  329. -> create 1229 synthetic samples
    330. 

  330. -> test with 'LR'
    331. 

  331. LR tn, fp: 318, 0
    332. 

  332. LR fn, tp: 0, 11
    333. 

  333. LR f1 score: 1.000
    334. 

  334. LR cohens kappa score: 1.000
    335. 

  335. LR average precision score: 1.000
    336. 

  336. 

  337. -> test with 'GB'
    338. 

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

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

  340. GB f1 score: 1.000
    341. 

  341. GB cohens kappa score: 1.000
    342. 

  342. 

  343. -> test with 'KNN'
    344. 

  344. KNN tn, fp: 318, 0
    345. 

  345. KNN fn, tp: 0, 11
    346. 

  346. KNN f1 score: 1.000
    347. 

  347. KNN cohens kappa score: 1.000
    348. 

  348. 

  349. 

  350. ------ Step 3/5: Slice 4/5 -------
    351. 

  351. -> Reset the GAN
    352. 

  352. -> Train generator for synthetic samples
    353. 

  353. 
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 60%|██████    | 6/10 [00:12<00:08,  2.14s/it]
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100%|██████████| 10/10 [00:21<00:00,  2.07s/it]
100%|██████████| 10/10 [00:21<00:00,  2.11s/it]
    354. 

  354. -> create 1229 synthetic samples
    355. 

  355. -> test with 'LR'
    356. 

  356. LR tn, fp: 318, 0
    357. 

  357. LR fn, tp: 0, 11
    358. 

  358. LR f1 score: 1.000
    359. 

  359. LR cohens kappa score: 1.000
    360. 

  360. LR average precision score: 1.000
    361. 

  361. 

  362. -> test with 'GB'
    363. 

  363. GB tn, fp: 318, 0
    364. 

  364. GB fn, tp: 0, 11
    365. 

  365. GB f1 score: 1.000
    366. 

  366. GB cohens kappa score: 1.000
    367. 

  367. 

  368. -> test with 'KNN'
    369. 

  369. KNN tn, fp: 318, 0
    370. 

  370. KNN fn, tp: 0, 11
    371. 

  371. KNN f1 score: 1.000
    372. 

  372. KNN cohens kappa score: 1.000
    373. 

  373. 

  374. 

  375. ------ Step 3/5: Slice 5/5 -------
    376. 

  376. -> Reset the GAN
    377. 

  377. -> Train generator for synthetic samples
    378. 

  378. 
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100%|██████████| 10/10 [00:21<00:00,  2.21s/it]
100%|██████████| 10/10 [00:21<00:00,  2.17s/it]
    379. 

  379. -> create 1228 synthetic samples
    380. 

  380. -> test with 'LR'
    381. 

  381. LR tn, fp: 317, 0
    382. 

  382. LR fn, tp: 0, 9
    383. 

  383. LR f1 score: 1.000
    384. 

  384. LR cohens kappa score: 1.000
    385. 

  385. LR average precision score: 1.000
    386. 

  386. 

  387. -> test with 'GB'
    388. 

  388. GB tn, fp: 317, 0
    389. 

  389. GB fn, tp: 0, 9
    390. 

  390. GB f1 score: 1.000
    391. 

  391. GB cohens kappa score: 1.000
    392. 

  392. 

  393. -> test with 'KNN'
    394. 

  394. KNN tn, fp: 317, 0
    395. 

  395. KNN fn, tp: 0, 9
    396. 

  396. KNN f1 score: 1.000
    397. 

  397. KNN cohens kappa score: 1.000
    398. 

  398. 

  399. 

  400. ====== Step 4/5 =======
    401. 

  401. -> Shuffling data
    402. 

  402. -> Spliting data to slices
    403. 

  403. 

  404. ------ Step 4/5: Slice 1/5 -------
    405. 

  405. -> Reset the GAN
    406. 

  406. -> Train generator for synthetic samples
    407. 

  407. 
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100%|██████████| 10/10 [00:21<00:00,  2.12s/it]
100%|██████████| 10/10 [00:21<00:00,  2.18s/it]
    408. 

  408. -> create 1229 synthetic samples
    409. 

  409. -> test with 'LR'
    410. 

  410. LR tn, fp: 318, 0
    411. 

  411. LR fn, tp: 0, 11
    412. 

  412. LR f1 score: 1.000
    413. 

  413. LR cohens kappa score: 1.000
    414. 

  414. LR average precision score: 1.000
    415. 

  415. 

  416. -> test with 'GB'
    417. 

  417. GB tn, fp: 318, 0
    418. 

  418. GB fn, tp: 0, 11
    419. 

  419. GB f1 score: 1.000
    420. 

  420. GB cohens kappa score: 1.000
    421. 

  421. 

  422. -> test with 'KNN'
    423. 

  423. KNN tn, fp: 318, 0
    424. 

  424. KNN fn, tp: 0, 11
    425. 

  425. KNN f1 score: 1.000
    426. 

  426. KNN cohens kappa score: 1.000
    427. 

  427. 

  428. 

  429. ------ Step 4/5: Slice 2/5 -------
    430. 

  430. -> Reset the GAN
    431. 

  431. -> Train generator for synthetic samples
    432. 

  432. 
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100%|██████████| 10/10 [00:21<00:00,  2.12s/it]
100%|██████████| 10/10 [00:21<00:00,  2.15s/it]
    433. 

  433. -> create 1229 synthetic samples
    434. 

  434. -> test with 'LR'
    435. 

  435. LR tn, fp: 318, 0
    436. 

  436. LR fn, tp: 0, 11
    437. 

  437. LR f1 score: 1.000
    438. 

  438. LR cohens kappa score: 1.000
    439. 

  439. LR average precision score: 1.000
    440. 

  440. 

  441. -> test with 'GB'
    442. 

  442. GB tn, fp: 318, 0
    443. 

  443. GB fn, tp: 0, 11
    444. 

  444. GB f1 score: 1.000
    445. 

  445. GB cohens kappa score: 1.000
    446. 

  446. 

  447. -> test with 'KNN'
    448. 

  448. KNN tn, fp: 318, 0
    449. 

  449. KNN fn, tp: 0, 11
    450. 

  450. KNN f1 score: 1.000
    451. 

  451. KNN cohens kappa score: 1.000
    452. 

  452. 

  453. 

  454. ------ Step 4/5: Slice 3/5 -------
    455. 

  455. -> Reset the GAN
    456. 

  456. -> Train generator for synthetic samples
    457. 

  457. 
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100%|██████████| 10/10 [00:21<00:00,  2.08s/it]
100%|██████████| 10/10 [00:21<00:00,  2.11s/it]
    458. 

  458. -> create 1229 synthetic samples
    459. 

  459. -> test with 'LR'
    460. 

  460. LR tn, fp: 318, 0
    461. 

  461. LR fn, tp: 0, 11
    462. 

  462. LR f1 score: 1.000
    463. 

  463. LR cohens kappa score: 1.000
    464. 

  464. LR average precision score: 1.000
    465. 

  465. 

  466. -> test with 'GB'
    467. 

  467. GB tn, fp: 318, 0
    468. 

  468. GB fn, tp: 0, 11
    469. 

  469. GB f1 score: 1.000
    470. 

  470. GB cohens kappa score: 1.000
    471. 

  471. 

  472. -> test with 'KNN'
    473. 

  473. KNN tn, fp: 318, 0
    474. 

  474. KNN fn, tp: 0, 11
    475. 

  475. KNN f1 score: 1.000
    476. 

  476. KNN cohens kappa score: 1.000
    477. 

  477. 

  478. 

  479. ------ Step 4/5: Slice 4/5 -------
    480. 

  480. -> Reset the GAN
    481. 

  481. -> Train generator for synthetic samples
    482. 

  482. 
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100%|██████████| 10/10 [00:21<00:00,  2.08s/it]
100%|██████████| 10/10 [00:21<00:00,  2.13s/it]
    483. 

  483. -> create 1229 synthetic samples
    484. 

  484. -> test with 'LR'
    485. 

  485. LR tn, fp: 318, 0
    486. 

  486. LR fn, tp: 0, 11
    487. 

  487. LR f1 score: 1.000
    488. 

  488. LR cohens kappa score: 1.000
    489. 

  489. LR average precision score: 1.000
    490. 

  490. 

  491. -> test with 'GB'
    492. 

  492. GB tn, fp: 318, 0
    493. 

  493. GB fn, tp: 0, 11
    494. 

  494. GB f1 score: 1.000
    495. 

  495. GB cohens kappa score: 1.000
    496. 

  496. 

  497. -> test with 'KNN'
    498. 

  498. KNN tn, fp: 318, 0
    499. 

  499. KNN fn, tp: 0, 11
    500. 

  500. KNN f1 score: 1.000
    501. 

  501. KNN cohens kappa score: 1.000
    502. 

  502. 

  503. 

  504. ------ Step 4/5: Slice 5/5 -------
    505. 

  505. -> Reset the GAN
    506. 

  506. -> Train generator for synthetic samples
    507. 

  507. 
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100%|██████████| 10/10 [00:24<00:00,  2.60s/it]
100%|██████████| 10/10 [00:24<00:00,  2.40s/it]
    508. 

  508. -> create 1228 synthetic samples
    509. 

  509. -> test with 'LR'
    510. 

  510. LR tn, fp: 317, 0
    511. 

  511. LR fn, tp: 0, 9
    512. 

  512. LR f1 score: 1.000
    513. 

  513. LR cohens kappa score: 1.000
    514. 

  514. LR average precision score: 1.000
    515. 

  515. 

  516. -> test with 'GB'
    517. 

  517. GB tn, fp: 317, 0
    518. 

  518. GB fn, tp: 0, 9
    519. 

  519. GB f1 score: 1.000
    520. 

  520. GB cohens kappa score: 1.000
    521. 

  521. 

  522. -> test with 'KNN'
    523. 

  523. KNN tn, fp: 316, 1
    524. 

  524. KNN fn, tp: 0, 9
    525. 

  525. KNN f1 score: 0.947
    526. 

  526. KNN cohens kappa score: 0.946
    527. 

  527. 

  528. 

  529. ====== Step 5/5 =======
    530. 

  530. -> Shuffling data
    531. 

  531. -> Spliting data to slices
    532. 

  532. 

  533. ------ Step 5/5: Slice 1/5 -------
    534. 

  534. -> Reset the GAN
    535. 

  535. -> Train generator for synthetic samples
    536. 

  536. 
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100%|██████████| 10/10 [00:21<00:00,  2.11s/it]
    537. 

  537. -> create 1229 synthetic samples
    538. 

  538. -> test with 'LR'
    539. 

  539. LR tn, fp: 317, 1
    540. 

  540. LR fn, tp: 0, 11
    541. 

  541. LR f1 score: 0.957
    542. 

  542. LR cohens kappa score: 0.955
    543. 

  543. LR average precision score: 0.917
    544. 

  544. 

  545. -> test with 'GB'
    546. 

  546. GB tn, fp: 318, 0
    547. 

  547. GB fn, tp: 0, 11
    548. 

  548. GB f1 score: 1.000
    549. 

  549. GB cohens kappa score: 1.000
    550. 

  550. 

  551. -> test with 'KNN'
    552. 

  552. KNN tn, fp: 317, 1
    553. 

  553. KNN fn, tp: 0, 11
    554. 

  554. KNN f1 score: 0.957
    555. 

  555. KNN cohens kappa score: 0.955
    556. 

  556. 

  557. 

  558. ------ Step 5/5: Slice 2/5 -------
    559. 

  559. -> Reset the GAN
    560. 

  560. -> Train generator for synthetic samples
    561. 

  561. 
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100%|██████████| 10/10 [00:21<00:00,  2.27s/it]
100%|██████████| 10/10 [00:21<00:00,  2.19s/it]
    562. 

  562. -> create 1229 synthetic samples
    563. 

  563. -> test with 'LR'
    564. 

  564. LR tn, fp: 318, 0
    565. 

  565. LR fn, tp: 0, 11
    566. 

  566. LR f1 score: 1.000
    567. 

  567. LR cohens kappa score: 1.000
    568. 

  568. LR average precision score: 1.000
    569. 

  569. 

  570. -> test with 'GB'
    571. 

  571. GB tn, fp: 318, 0
    572. 

  572. GB fn, tp: 0, 11
    573. 

  573. GB f1 score: 1.000
    574. 

  574. GB cohens kappa score: 1.000
    575. 

  575. 

  576. -> test with 'KNN'
    577. 

  577. KNN tn, fp: 318, 0
    578. 

  578. KNN fn, tp: 0, 11
    579. 

  579. KNN f1 score: 1.000
    580. 

  580. KNN cohens kappa score: 1.000
    581. 

  581. 

  582. 

  583. ------ Step 5/5: Slice 3/5 -------
    584. 

  584. -> Reset the GAN
    585. 

  585. -> Train generator for synthetic samples
    586. 

  586. 
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100%|██████████| 10/10 [00:21<00:00,  2.13s/it]
    587. 

  587. -> create 1229 synthetic samples
    588. 

  588. -> test with 'LR'
    589. 

  589. LR tn, fp: 318, 0
    590. 

  590. LR fn, tp: 0, 11
    591. 

  591. LR f1 score: 1.000
    592. 

  592. LR cohens kappa score: 1.000
    593. 

  593. LR average precision score: 1.000
    594. 

  594. 

  595. -> test with 'GB'
    596. 

  596. GB tn, fp: 318, 0
    597. 

  597. GB fn, tp: 0, 11
    598. 

  598. GB f1 score: 1.000
    599. 

  599. GB cohens kappa score: 1.000
    600. 

  600. 

  601. -> test with 'KNN'
    602. 

  602. KNN tn, fp: 318, 0
    603. 

  603. KNN fn, tp: 0, 11
    604. 

  604. KNN f1 score: 1.000
    605. 

  605. KNN cohens kappa score: 1.000
    606. 

  606. 

  607. 

  608. ------ Step 5/5: Slice 4/5 -------
    609. 

  609. -> Reset the GAN
    610. 

  610. -> Train generator for synthetic samples
    611. 

  611. 
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100%|██████████| 10/10 [00:20<00:00,  2.03s/it]
    612. 

  612. -> create 1229 synthetic samples
    613. 

  613. -> test with 'LR'
    614. 

  614. LR tn, fp: 318, 0
    615. 

  615. LR fn, tp: 0, 11
    616. 

  616. LR f1 score: 1.000
    617. 

  617. LR cohens kappa score: 1.000
    618. 

  618. LR average precision score: 1.000
    619. 

  619. 

  620. -> test with 'GB'
    621. 

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

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

  623. GB f1 score: 1.000
    624. 

  624. GB cohens kappa score: 1.000
    625. 

  625. 

  626. -> test with 'KNN'
    627. 

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

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

  629. KNN f1 score: 1.000
    630. 

  630. KNN cohens kappa score: 1.000
    631. 

  631. 

  632. 

  633. ------ Step 5/5: Slice 5/5 -------
    634. 

  634. -> Reset the GAN
    635. 

  635. -> Train generator for synthetic samples
    636. 

  636. 
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100%|██████████| 10/10 [00:20<00:00,  2.06s/it]
100%|██████████| 10/10 [00:20<00:00,  2.01s/it]
    637. 

  637. -> create 1228 synthetic samples
    638. 

  638. -> test with 'LR'
    639. 

  639. LR tn, fp: 317, 0
    640. 

  640. LR fn, tp: 0, 9
    641. 

  641. LR f1 score: 1.000
    642. 

  642. LR cohens kappa score: 1.000
    643. 

  643. LR average precision score: 1.000
    644. 

  644. 

  645. -> test with 'GB'
    646. 

  646. GB tn, fp: 317, 0
    647. 

  647. GB fn, tp: 0, 9
    648. 

  648. GB f1 score: 1.000
    649. 

  649. GB cohens kappa score: 1.000
    650. 

  650. 

  651. -> test with 'KNN'
    652. 

  652. KNN tn, fp: 317, 0
    653. 

  653. KNN fn, tp: 0, 9
    654. 

  654. KNN f1 score: 1.000
    655. 

  655. KNN cohens kappa score: 1.000
    656. 

  656. 

  657. ### Exercise is done.
    658. 

  658. 

  659. -----[ LR ]-----
    660. 

  660. maximum:
    661. 

  661. LR tn, fp: 318, 1
    662. 

  662. LR fn, tp: 0, 11
    663. 

  663. LR f1 score: 1.000
    664. 

  664. LR cohens kappa score: 1.000
    665. 

  665. LR average precision score: 1.000
    666. 

  666. 

  667. 

  668. average:
    669. 

  669. LR tn, fp: 317.72, 0.08
    670. 

  670. LR fn, tp: 0.0, 10.6
    671. 

  671. LR f1 score: 0.996
    672. 

  672. LR cohens kappa score: 0.996
    673. 

  673. LR average precision score: 0.997
    674. 

  674. 

  675. 

  676. minimum:
    677. 

  677. LR tn, fp: 316, 0
    678. 

  678. LR fn, tp: 0, 9
    679. 

  679. LR f1 score: 0.947
    680. 

  680. LR cohens kappa score: 0.946
    681. 

  681. LR average precision score: 0.917
    682. 

  682. 

  683. 

  684. -----[ GB ]-----
    685. 

  685. maximum:
    686. 

  686. GB tn, fp: 318, 0
    687. 

  687. GB fn, tp: 0, 11
    688. 

  688. GB f1 score: 1.000
    689. 

  689. GB cohens kappa score: 1.000
    690. 

  690. 

  691. 

  692. average:
    693. 

  693. GB tn, fp: 317.8, 0.0
    694. 

  694. GB fn, tp: 0.0, 10.6
    695. 

  695. GB f1 score: 1.000
    696. 

  696. GB cohens kappa score: 1.000
    697. 

  697. 

  698. 

  699. minimum:
    700. 

  700. GB tn, fp: 317, 0
    701. 

  701. GB fn, tp: 0, 9
    702. 

  702. GB f1 score: 1.000
    703. 

  703. GB cohens kappa score: 1.000
    704. 

  704. 

  705. 

  706. -----[ KNN ]-----
    707. 

  707. maximum:
    708. 

  708. KNN tn, fp: 318, 1
    709. 

  709. KNN fn, tp: 0, 11
    710. 

  710. KNN f1 score: 1.000
    711. 

  711. KNN cohens kappa score: 1.000
    712. 

  712. 

  713. 

  714. average:
    715. 

  715. KNN tn, fp: 317.6, 0.2
    716. 

  716. KNN fn, tp: 0.0, 10.6
    717. 

  717. KNN f1 score: 0.991
    718. 

  718. KNN cohens kappa score: 0.991
    719. 

  719. 

  720. 

  721. minimum:
    722. 

  722. KNN tn, fp: 316, 0
    723. 

  723. KNN fn, tp: 0, 9
    724. 

  724. KNN f1 score: 0.947
    725. 

  725. KNN cohens kappa score: 0.946
    726. 

  726.