ConvGeNBenchmarkResults/data_result/convGAN-proximary-5/imblearn_ozone_level.log

///////////////////////////////////////////
// Running convGAN-proximary-5 on imblearn_ozone_level
///////////////////////////////////////////

Load 'data_input/imblearn_ozone_level'
from imblearn
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 394, 99
GAN fn, tp: 3, 12
GAN f1 score: 0.190
GAN cohens kappa score: 0.146

-> test with 'LR'
LR tn, fp: 425, 68
LR fn, tp: 1, 14
LR f1 score: 0.289
LR cohens kappa score: 0.251
LR average precision score: 0.349

-> test with 'GB'
GB tn, fp: 478, 15
GB fn, tp: 7, 8
GB f1 score: 0.421
GB cohens kappa score: 0.400

-> test with 'KNN'
KNN tn, fp: 391, 102
KNN fn, tp: 9, 6
KNN f1 score: 0.098
KNN cohens kappa score: 0.048


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 484, 9
GAN fn, tp: 15, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.023

-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 4, 11
LR f1 score: 0.253
LR cohens kappa score: 0.214
LR average precision score: 0.202

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 7, 8
GB f1 score: 0.516
GB cohens kappa score: 0.501

-> test with 'KNN'
KNN tn, fp: 446, 47
KNN fn, tp: 13, 2
KNN f1 score: 0.062
KNN cohens kappa score: 0.018


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 492, 1
GAN fn, tp: 15, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.004

-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 4, 11
LR f1 score: 0.253
LR cohens kappa score: 0.214
LR average precision score: 0.119

-> test with 'GB'
GB tn, fp: 478, 15
GB fn, tp: 7, 8
GB f1 score: 0.421
GB cohens kappa score: 0.400

-> test with 'KNN'
KNN tn, fp: 426, 67
KNN fn, tp: 11, 4
KNN f1 score: 0.093
KNN cohens kappa score: 0.047


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 387, 106
GAN fn, tp: 4, 11
GAN f1 score: 0.167
GAN cohens kappa score: 0.121

-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 5, 10
LR f1 score: 0.233
LR cohens kappa score: 0.193
LR average precision score: 0.202

-> test with 'GB'
GB tn, fp: 485, 8
GB fn, tp: 9, 6
GB f1 score: 0.414
GB cohens kappa score: 0.397

-> test with 'KNN'
KNN tn, fp: 430, 63
KNN fn, tp: 10, 5
KNN f1 score: 0.120
KNN cohens kappa score: 0.076


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 163, 328
GAN fn, tp: 1, 12
GAN f1 score: 0.068
GAN cohens kappa score: 0.019

-> test with 'LR'
LR tn, fp: 427, 64
LR fn, tp: 3, 10
LR f1 score: 0.230
LR cohens kappa score: 0.195
LR average precision score: 0.180

-> test with 'GB'
GB tn, fp: 475, 16
GB fn, tp: 9, 4
GB f1 score: 0.242
GB cohens kappa score: 0.218

-> test with 'KNN'
KNN tn, fp: 383, 108
KNN fn, tp: 5, 8
KNN f1 score: 0.124
KNN cohens kappa score: 0.081


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 39, 454
GAN fn, tp: 0, 15
GAN f1 score: 0.062
GAN cohens kappa score: 0.005

-> test with 'LR'
LR tn, fp: 425, 68
LR fn, tp: 5, 10
LR f1 score: 0.215
LR cohens kappa score: 0.174
LR average precision score: 0.308

-> test with 'GB'
GB tn, fp: 483, 10
GB fn, tp: 12, 3
GB f1 score: 0.214
GB cohens kappa score: 0.192

-> test with 'KNN'
KNN tn, fp: 415, 78
KNN fn, tp: 10, 5
KNN f1 score: 0.102
KNN cohens kappa score: 0.055


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 341, 152
GAN fn, tp: 7, 8
GAN f1 score: 0.091
GAN cohens kappa score: 0.040

-> test with 'LR'
LR tn, fp: 444, 49
LR fn, tp: 5, 10
LR f1 score: 0.270
LR cohens kappa score: 0.234
LR average precision score: 0.214

-> test with 'GB'
GB tn, fp: 481, 12
GB fn, tp: 7, 8
GB f1 score: 0.457
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 396, 97
KNN fn, tp: 6, 9
KNN f1 score: 0.149
KNN cohens kappa score: 0.102


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 72, 421
GAN fn, tp: 0, 15
GAN f1 score: 0.067
GAN cohens kappa score: 0.010

-> test with 'LR'
LR tn, fp: 426, 67
LR fn, tp: 1, 14
LR f1 score: 0.292
LR cohens kappa score: 0.255
LR average precision score: 0.413

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 8, 7
GB f1 score: 0.424
GB cohens kappa score: 0.405

-> test with 'KNN'
KNN tn, fp: 394, 99
KNN fn, tp: 11, 4
KNN f1 score: 0.068
KNN cohens kappa score: 0.017


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 450, 43
GAN fn, tp: 13, 2
GAN f1 score: 0.067
GAN cohens kappa score: 0.023

-> test with 'LR'
LR tn, fp: 440, 53
LR fn, tp: 5, 10
LR f1 score: 0.256
LR cohens kappa score: 0.219
LR average precision score: 0.155

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 11, 4
GB f1 score: 0.229
GB cohens kappa score: 0.202

-> test with 'KNN'
KNN tn, fp: 432, 61
KNN fn, tp: 11, 4
KNN f1 score: 0.100
KNN cohens kappa score: 0.055


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 473, 18
GAN fn, tp: 11, 2
GAN f1 score: 0.121
GAN cohens kappa score: 0.093

-> test with 'LR'
LR tn, fp: 430, 61
LR fn, tp: 3, 10
LR f1 score: 0.238
LR cohens kappa score: 0.203
LR average precision score: 0.195

-> test with 'GB'
GB tn, fp: 479, 12
GB fn, tp: 7, 6
GB f1 score: 0.387
GB cohens kappa score: 0.368

-> test with 'KNN'
KNN tn, fp: 377, 114
KNN fn, tp: 2, 11
KNN f1 score: 0.159
KNN cohens kappa score: 0.118


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 379, 114
GAN fn, tp: 8, 7
GAN f1 score: 0.103
GAN cohens kappa score: 0.053

-> test with 'LR'
LR tn, fp: 431, 62
LR fn, tp: 3, 12
LR f1 score: 0.270
LR cohens kappa score: 0.232
LR average precision score: 0.302

-> test with 'GB'
GB tn, fp: 475, 18
GB fn, tp: 10, 5
GB f1 score: 0.263
GB cohens kappa score: 0.236

-> test with 'KNN'
KNN tn, fp: 395, 98
KNN fn, tp: 9, 6
KNN f1 score: 0.101
KNN cohens kappa score: 0.052


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 463, 30
GAN fn, tp: 11, 4
GAN f1 score: 0.163
GAN cohens kappa score: 0.128

-> test with 'LR'
LR tn, fp: 430, 63
LR fn, tp: 3, 12
LR f1 score: 0.267
LR cohens kappa score: 0.229
LR average precision score: 0.147

-> test with 'GB'
GB tn, fp: 476, 17
GB fn, tp: 8, 7
GB f1 score: 0.359
GB cohens kappa score: 0.335

-> test with 'KNN'
KNN tn, fp: 391, 102
KNN fn, tp: 7, 8
KNN f1 score: 0.128
KNN cohens kappa score: 0.080


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 411, 82
GAN fn, tp: 5, 10
GAN f1 score: 0.187
GAN cohens kappa score: 0.143

-> test with 'LR'
LR tn, fp: 449, 44
LR fn, tp: 4, 11
LR f1 score: 0.314
LR cohens kappa score: 0.281
LR average precision score: 0.188

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 9, 6
GB f1 score: 0.324
GB cohens kappa score: 0.300

-> test with 'KNN'
KNN tn, fp: 398, 95
KNN fn, tp: 10, 5
KNN f1 score: 0.087
KNN cohens kappa score: 0.038


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 482, 11
GAN fn, tp: 15, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.026

-> test with 'LR'
LR tn, fp: 448, 45
LR fn, tp: 5, 10
LR f1 score: 0.286
LR cohens kappa score: 0.251
LR average precision score: 0.165

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 9, 6
GB f1 score: 0.324
GB cohens kappa score: 0.300

-> test with 'KNN'
KNN tn, fp: 394, 99
KNN fn, tp: 9, 6
KNN f1 score: 0.100
KNN cohens kappa score: 0.051


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 143, 348
GAN fn, tp: 0, 13
GAN f1 score: 0.070
GAN cohens kappa score: 0.021

-> test with 'LR'
LR tn, fp: 424, 67
LR fn, tp: 2, 11
LR f1 score: 0.242
LR cohens kappa score: 0.207
LR average precision score: 0.367

-> test with 'GB'
GB tn, fp: 478, 13
GB fn, tp: 8, 5
GB f1 score: 0.323
GB cohens kappa score: 0.302

-> test with 'KNN'
KNN tn, fp: 396, 95
KNN fn, tp: 3, 10
KNN f1 score: 0.169
KNN cohens kappa score: 0.130


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 490, 3
GAN fn, tp: 15, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.010

-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 4, 11
LR f1 score: 0.253
LR cohens kappa score: 0.214
LR average precision score: 0.270

-> test with 'GB'
GB tn, fp: 478, 15
GB fn, tp: 10, 5
GB f1 score: 0.286
GB cohens kappa score: 0.261

-> test with 'KNN'
KNN tn, fp: 399, 94
KNN fn, tp: 9, 6
KNN f1 score: 0.104
KNN cohens kappa score: 0.056


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 456, 37
GAN fn, tp: 11, 4
GAN f1 score: 0.143
GAN cohens kappa score: 0.104

-> test with 'LR'
LR tn, fp: 439, 54
LR fn, tp: 4, 11
LR f1 score: 0.275
LR cohens kappa score: 0.238
LR average precision score: 0.238

-> test with 'GB'
GB tn, fp: 478, 15
GB fn, tp: 7, 8
GB f1 score: 0.421
GB cohens kappa score: 0.400

-> test with 'KNN'
KNN tn, fp: 406, 87
KNN fn, tp: 9, 6
KNN f1 score: 0.111
KNN cohens kappa score: 0.063


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 452, 41
GAN fn, tp: 10, 5
GAN f1 score: 0.164
GAN cohens kappa score: 0.125

-> test with 'LR'
LR tn, fp: 433, 60
LR fn, tp: 3, 12
LR f1 score: 0.276
LR cohens kappa score: 0.239
LR average precision score: 0.193

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 7, 8
GB f1 score: 0.444
GB cohens kappa score: 0.425

-> test with 'KNN'
KNN tn, fp: 412, 81
KNN fn, tp: 11, 4
KNN f1 score: 0.080
KNN cohens kappa score: 0.031


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 449, 44
GAN fn, tp: 9, 6
GAN f1 score: 0.185
GAN cohens kappa score: 0.146

-> test with 'LR'
LR tn, fp: 435, 58
LR fn, tp: 3, 12
LR f1 score: 0.282
LR cohens kappa score: 0.246
LR average precision score: 0.283

-> test with 'GB'
GB tn, fp: 477, 16
GB fn, tp: 8, 7
GB f1 score: 0.368
GB cohens kappa score: 0.345

-> test with 'KNN'
KNN tn, fp: 395, 98
KNN fn, tp: 9, 6
KNN f1 score: 0.101
KNN cohens kappa score: 0.052


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 338, 153
GAN fn, tp: 4, 9
GAN f1 score: 0.103
GAN cohens kappa score: 0.058

-> test with 'LR'
LR tn, fp: 422, 69
LR fn, tp: 4, 9
LR f1 score: 0.198
LR cohens kappa score: 0.161
LR average precision score: 0.212

-> test with 'GB'
GB tn, fp: 474, 17
GB fn, tp: 7, 6
GB f1 score: 0.333
GB cohens kappa score: 0.311

-> test with 'KNN'
KNN tn, fp: 393, 98
KNN fn, tp: 6, 7
KNN f1 score: 0.119
KNN cohens kappa score: 0.076


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 17, 476
GAN fn, tp: 0, 15
GAN f1 score: 0.059
GAN cohens kappa score: 0.002

-> test with 'LR'
LR tn, fp: 441, 52
LR fn, tp: 2, 13
LR f1 score: 0.325
LR cohens kappa score: 0.291
LR average precision score: 0.266

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 7, 8
GB f1 score: 0.444
GB cohens kappa score: 0.425

-> test with 'KNN'
KNN tn, fp: 383, 110
KNN fn, tp: 11, 4
KNN f1 score: 0.062
KNN cohens kappa score: 0.010


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 423, 70
GAN fn, tp: 4, 11
GAN f1 score: 0.229
GAN cohens kappa score: 0.189

-> test with 'LR'
LR tn, fp: 422, 71
LR fn, tp: 3, 12
LR f1 score: 0.245
LR cohens kappa score: 0.205
LR average precision score: 0.159

-> test with 'GB'
GB tn, fp: 476, 17
GB fn, tp: 10, 5
GB f1 score: 0.270
GB cohens kappa score: 0.244

-> test with 'KNN'
KNN tn, fp: 410, 83
KNN fn, tp: 11, 4
KNN f1 score: 0.078
KNN cohens kappa score: 0.030


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 455, 38
GAN fn, tp: 9, 6
GAN f1 score: 0.203
GAN cohens kappa score: 0.167

-> test with 'LR'
LR tn, fp: 455, 38
LR fn, tp: 6, 9
LR f1 score: 0.290
LR cohens kappa score: 0.257
LR average precision score: 0.191

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 11, 4
GB f1 score: 0.267
GB cohens kappa score: 0.244

-> test with 'KNN'
KNN tn, fp: 363, 130
KNN fn, tp: 5, 10
KNN f1 score: 0.129
KNN cohens kappa score: 0.080


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 387, 106
GAN fn, tp: 3, 12
GAN f1 score: 0.180
GAN cohens kappa score: 0.135

-> test with 'LR'
LR tn, fp: 425, 68
LR fn, tp: 2, 13
LR f1 score: 0.271
LR cohens kappa score: 0.233
LR average precision score: 0.242

-> test with 'GB'
GB tn, fp: 474, 19
GB fn, tp: 6, 9
GB f1 score: 0.419
GB cohens kappa score: 0.395

-> test with 'KNN'
KNN tn, fp: 404, 89
KNN fn, tp: 8, 7
KNN f1 score: 0.126
KNN cohens kappa score: 0.079


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with GAN.predict
GAN tn, fp: 232, 259
GAN fn, tp: 0, 13
GAN f1 score: 0.091
GAN cohens kappa score: 0.044

-> test with 'LR'
LR tn, fp: 421, 70
LR fn, tp: 2, 11
LR f1 score: 0.234
LR cohens kappa score: 0.198
LR average precision score: 0.277

-> test with 'GB'
GB tn, fp: 476, 15
GB fn, tp: 9, 4
GB f1 score: 0.250
GB cohens kappa score: 0.226

-> test with 'KNN'
KNN tn, fp: 383, 108
KNN fn, tp: 6, 7
KNN f1 score: 0.109
KNN cohens kappa score: 0.066

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 455, 71
LR fn, tp: 6, 14
LR f1 score: 0.325
LR cohens kappa score: 0.291
LR average precision score: 0.413


average:
LR tn, fp: 432.8, 59.8
LR fn, tp: 3.44, 11.16
LR f1 score: 0.262
LR cohens kappa score: 0.225
LR average precision score: 0.233


minimum:
LR tn, fp: 421, 38
LR fn, tp: 1, 9
LR f1 score: 0.198
LR cohens kappa score: 0.161
LR average precision score: 0.119


-----[ GB ]-----
maximum:
GB tn, fp: 485, 19
GB fn, tp: 12, 9
GB f1 score: 0.516
GB cohens kappa score: 0.501


average:
GB tn, fp: 478.44, 14.16
GB fn, tp: 8.4, 6.2
GB f1 score: 0.353
GB cohens kappa score: 0.331


minimum:
GB tn, fp: 474, 8
GB fn, tp: 6, 3
GB f1 score: 0.214
GB cohens kappa score: 0.192


-----[ KNN ]-----
maximum:
KNN tn, fp: 446, 130
KNN fn, tp: 13, 11
KNN f1 score: 0.169
KNN cohens kappa score: 0.130


average:
KNN tn, fp: 400.48, 92.12
KNN fn, tp: 8.44, 6.16
KNN f1 score: 0.107
KNN cohens kappa score: 0.060


minimum:
KNN tn, fp: 363, 47
KNN fn, tp: 2, 2
KNN f1 score: 0.062
KNN cohens kappa score: 0.010


-----[ GAN ]-----
maximum:
GAN tn, fp: 492, 476
GAN fn, tp: 15, 15
GAN f1 score: 0.229
GAN cohens kappa score: 0.189


average:
GAN tn, fp: 354.88, 137.72
GAN fn, tp: 6.92, 7.68
GAN f1 score: 0.109
GAN cohens kappa score: 0.068


minimum:
GAN tn, fp: 17, 1
GAN fn, tp: 0, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.026