-
Notifications
You must be signed in to change notification settings - Fork 4
/
acgan_cv.py
704 lines (661 loc) · 29.3 KB
/
acgan_cv.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
from header import *
import header
import importlib
importlib.reload(header) # For reloading after making changes
class ACGAN_CV:
def __init__(self, IMG_SHAPE=0):
# Input shape
self.img_rows = 28
self.img_cols = 28
self.channels = 1
self.img_shape = (self.img_rows, self.img_cols, self.channels)
self.num_classes = 2
self.latent_dim = 100
# tf.compat.v1.disable_eager_execution()
optimizer = Adam(0.0002, 0.5)
losses = ["binary_crossentropy", "sparse_categorical_crossentropy"]
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
self.discriminator.compile(loss=losses, optimizer=optimizer)
# Build the generator
self.generator = self.build_generator()
# The generator takes noise and the target label as input
# and generates the corresponding digit of that label
noise = Input(shape=(self.latent_dim,))
label = Input(shape=(1,))
img = self.generator([noise, label])
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The discriminator takes generated image as input and determines validity
# and the label of that image
valid, target_label = self.discriminator(img)
# The combined model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
self.combined = Model([noise, label], [valid, target_label])
self.combined.compile(loss=losses, optimizer=optimizer)
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
label = Input(shape=(1,), dtype="int32")
label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
print("label Shape: " + str(label.shape))
print("Noise Shape: " + str(noise.shape))
print("label_embedding: " + str(label_embedding.shape))
model_input = tf.keras.layers.multiply([noise, label_embedding])
# model_input = tf.keras.layers.Multiply()([noise, label_embedding])
img = model(model_input)
return Model([noise, label], img)
def build_discriminator(self):
model = Sequential()
model.add(
Conv2D(
16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"
)
)
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten())
model.summary()
img = Input(shape=self.img_shape)
# Extract feature representation
features = model(img)
# Determine validity and label of the image
validity = Dense(1, activation="sigmoid")(features)
label = Dense(self.num_classes, activation="softmax")(features)
return Model(img, [validity, label])
def sample_images(self, epoch, path):
r, c = 1, 2
noise = np.random.normal(0, 1, (r * c, self.latent_dim))
sampled_labels = np.array([num for _ in range(r) for num in range(c)])
gen_imgs = self.generator.predict([noise, sampled_labels])
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(1, 2)
axs[0].imshow(gen_imgs[0, :, :, 0], cmap="gray")
axs[0].axis("off")
axs[1].imshow(gen_imgs[1, :, :, 0], cmap="gray")
axs[1].axis("off")
fig.savefig(path + "/epoch_%d.png" % epoch)
# plt.show()
plt.close()
def train(self, model_components):
[
cache_prefix,
with_class,
starting_step,
train,
Train,
Test,
data_cols,
data_dim,
label_cols,
label_dim,
rand_noise_dim,
nb_steps,
batch_size,
k_d,
k_g,
critic_pre_train_steps,
log_interval,
learning_rate,
base_n_count,
CACHE_PATH,
FIGS_PATH,
show,
comb_loss,
disc_loss_generated,
disc_loss_real,
xgb_losses,
dt_losses,
nb_losses,
knn_losses,
rf_losses,
lr_losses,
test_size,
epoch_list_disc_loss_real,
epoch_list_disc_loss_generated,
epoch_list_comb_loss,
gpu_device,
EVALUATION_PARAMETER,
TODAY,
] = model_components
batch_iteration = 0
total_time_so_far = 0
list_batch_iteration = []
list_log_iteration = []
list_disc_loss_real = []
list_disc_loss_generated = []
list_comb_loss = []
list_disc_perf = []
Disc_G_Z_Real_acc_ = []
Disc_G_Z_N_acc_ = []
Disc_G_Z_B_acc_ = []
Disc_N_acc_ = []
Disc_B_acc_ = []
xgb_acc = []
xgb_rcl = []
xgb_c2st = []
epoch_list_disc_loss_real = []
epoch_list_disc_loss_generated = []
epoch_list_gen_loss = []
epoch_list_disc_acc_real = []
epoch_list_disc_acc_generated = []
epoch_list_gen_acc = []
dt_acc = []
dt_rcl = []
dt_c2st = []
nb_acc = []
nb_rcl = []
nb_c2st = []
rf_acc = []
rf_rcl = []
rf_c2st = []
lr_acc = []
lr_rcl = []
lr_c2st = []
knn_acc = []
knn_rcl = []
knn_c2st = []
svm_acc = []
svm_rcl = []
svm_c2st = []
list_loss_real = []
list_loss_fake = []
list_loss_g = []
x = 0
g_z = 0
acc_ = 0
rcl_ = 0
best_xgb_acc_index = 0
best_xgb_rcl_index = 0
best_dt_acc_index = 0
best_dt_rcl_index = 0
best_nb_acc_index = 0
best_nb_rcl_index = 0
best_rf_acc_index = 0
best_rf_rcl_index = 0
best_lr_acc_index = 0
best_lr_rcl_index = 0
best_knn_acc_index = 0
best_knn_rcl_index = 0
# Directory
os.mkdir(CACHE_PATH + TODAY)
print("Directory '% s' created" % TODAY)
os.mkdir(FIGS_PATH + TODAY)
print("Directory '% s' created" % TODAY)
epoch_number = 0
log_iteration = 0
if DEBUG:
print(cache_prefix, batch_size, base_n_count)
i = 0
# ---------------------------------------------------------------------------------------------------------------
mean = 0
stdv = 1
# ---------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------
if DEBUG:
print("======================================================")
print("Batch Size Selected -------->>>>>>> " + str(batch_size))
print("======================================================")
# with tf.device(gpu_device):
# Load the dataset
(X_train, Y_train), (_, _) = mnist.load_data()
X_Normal, Y_Normal = (
X_train[np.where(Y_train == 1)],
Y_train[np.where(Y_train == 1)],
)
X_Bots, Y_Bots = (
X_train[np.where(Y_train == 0)],
Y_train[np.where(Y_train == 0)],
)
x_Normal = X_Normal[0 : int(X_Normal.shape[0] * 0.7)]
y_Normal = Y_Normal[0 : int(Y_Normal.shape[0] * 0.7)]
x_Bots = X_Bots[0 : int(X_Bots.shape[0] * 0.7 * 0.1)] # Undersampling here
y_Bots = Y_Bots[0 : int(Y_Bots.shape[0] * 0.7 * 0.1)] # Undersampling here
print("x_Normal.shape: " + str(x_Normal.shape))
print("x_Bots.shape: " + str(x_Bots.shape))
x_T = np.concatenate([x_Bots, x_Normal], axis=0)
y_T = np.concatenate([y_Bots, y_Normal], axis=0)
# Configure inputs
x_T = (x_T.astype(np.float32) - 127.5) / 127.5
x_T = np.expand_dims(x_T, axis=3)
y_T = y_T.reshape(-1, 1)
print(y_T.shape)
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
print("Starting GAN Training..")
start_log_time = time.time()
for i in range(starting_step, starting_step + nb_steps):
idx = np.random.randint(0, x_T.shape[0], batch_size)
imgs = x_T[idx]
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
sampled_labels = np.random.randint(0, 2, (batch_size, 1))
# Generate a half batch of new images
gen_imgs = self.generator.predict([noise, sampled_labels])
# Image labels. 0-1
img_labels = y_T[idx]
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs, [valid, img_labels])
d_loss_fake = self.discriminator.train_on_batch(
gen_imgs, [fake, sampled_labels]
)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
g_loss = self.combined.train_on_batch(
[noise, sampled_labels], [valid, sampled_labels]
)
# Plot the progress
# print("[d_loss_real:" + str(d_loss_real))
# print("[d_loss_fake:" + str(d_loss_fake))
# print("[g_loss:" + str(g_loss))
list_batch_iteration.append(batch_iteration)
# ---------------------------------------------------------------------------------------------------------------
# Determine xgb loss each step, after training generator and discriminator
if not i % log_interval: # 2x faster than testing each step...
print("d_loss: " + str(d_loss[0]) + " g_loss: " + str(g_loss[0]))
list_log_iteration.append(log_iteration)
log_iteration = log_iteration + 1
list_loss_real.append(d_loss_real[0]) # epoch_list_disc_loss_real
list_loss_fake.append(d_loss_fake[0]) # epoch_list_disc_loss_gen
list_loss_g.append(g_loss[0])
# ====================Testing on 30% of train data ================================
test_x_Normal = X_Normal[
int(X_Normal.shape[0] * 0.7) : X_Normal.shape[0]
]
test_x_Bots = X_Bots[int(X_Bots.shape[0] * 0.7) : X_Bots.shape[0]]
# print('test_x_Normal.shape: ' + str(test_x_Normal.shape))
# print('test_x_Bots.shape: ' + str(test_x_Bots.shape))
# Configure inputs
test_x_Normal = (test_x_Normal.astype(np.float32) - 127.5) / 127.5
test_x_Normal = np.expand_dims(test_x_Normal, axis=3)
test_x_Bots = (test_x_Bots.astype(np.float32) - 127.5) / 127.5
test_x_Bots = np.expand_dims(test_x_Bots, axis=3)
noise = np.random.normal(0, 1, (test_x_Bots.shape[0], self.latent_dim))
# sampled_labels = np.random.randint(0, 2, (test_x_Bots, 1))
sampled_labels = np.zeros((test_x_Bots.shape[0], 1))
gen_imgs = self.generator.predict([noise, sampled_labels])
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# gen_imgs = 0.5 * gen_imgs + 0.5
# fig, axs = plt.subplots(1, 1)
# axs.imshow(gen_imgs[0, :, :, 0], cmap='gray')
# axs.axis('off')
# # fig.savefig("/home/riz/Insync/[email protected]/Google_Drive/PhD/Development/code/Current/Paper_2(v7)/figs/EVAGAN_CV/%d.png" % epoch)
# plt.show()
# plt.close()
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
pred_Gen_Real_Fake = self.discriminator.predict(gen_imgs)
pred_Normal = self.discriminator.predict(test_x_Normal)
pred_Bots = self.discriminator.predict(test_x_Bots)
# print('pred_Bots: ' + str(pred_Bots))
G_Z_Real_acc_ = 0
G_Z_N_acc_ = 0
sum_pred_Normal = 0
sum_pred_Bots = 0
Gen_Real_Fake = round(
sum(pred_Gen_Real_Fake[0]) / test_x_Bots.shape[0], 4
)
# print("pred_Normal<><><><><><><><><><><><><><><<><<><><")
for i in range(test_x_Normal.shape[0]):
# print(pred_Normal)
sum_pred_Normal += pred_Normal[1][i][1]
average_pred_Normal = sum_pred_Normal / test_x_Normal.shape[0]
# print("pred_Bots<><><><><><><><><><><><><><><<><<><><")
for i in range(test_x_Bots.shape[0]):
# print(pred_Bots)
sum_pred_Bots += pred_Bots[1][i][0]
average_pred_Bots = sum_pred_Bots / test_x_Bots.shape[0]
N_acc_ = round(
average_pred_Normal, 4
) # predict normal being normal. If it maintains near 1 then it means it is normal becasue normal has been labeled as 0.
Ev_Real_Bot = 1 - round(
average_pred_Bots, 4
) # predict evasion of real bot. If it maintains near 0 then it means it is bot because bot has been labeled as 1.
# print('N_acc_: ' + str(N_acc_))
# print('Ev_Real_Bot: ' + str(Ev_Real_Bot))
Disc_G_Z_Real_acc_ = np.append(Disc_G_Z_Real_acc_, Gen_Real_Fake)
Disc_G_Z_N_acc_ = np.append(Disc_G_Z_N_acc_, G_Z_N_acc_)
Disc_N_acc_ = np.append(Disc_N_acc_, N_acc_)
Disc_B_acc_ = np.append(Disc_B_acc_, Ev_Real_Bot)
# xgb_loss = Evaluate_Parameter(x, g_z, data_cols, label_cols, seed=0, with_class=with_class, data_dim=data_dim , classifier = 'XGB', EVALUATION_PARAMETER = EVALUATION_PARAMETER)
# xgb_acc = np.append(xgb_acc, xgb_loss[0])
# xgb_rcl = np.append(xgb_rcl, xgb_loss[1])
# best_xgb_acc_index = list(xgb_acc).index( xgb_acc.min())
# best_xgb_rcl_index = list(xgb_rcl).index( xgb_rcl.min())
path = CACHE_PATH + TODAY
self.sample_images(log_iteration, path)
# print("+++++++++++++++++++++++++")
# print("GEN_Real/Fake" + str(Disc_G_Z_Real_acc_))
# print("Normal_Est" + str(Disc_N_acc_))
# print("Real_Eva" + str(Disc_B_acc_))
# print("+++++++++++++++++++++++++")
# ----------------------------------------------------------------------------------
if SHOW_TIME:
end_log_time = time.time()
log_interval_time = end_log_time - start_log_time
start_log_time = time.time()
total_time_so_far += log_interval_time
if DEBUG:
print(
"log_iteration: "
+ str(log_iteration)
+ "/"
+ str(nb_steps // log_interval)
)
# print("Time taken so far: " + str(total_time_so_far) + " seconds")
total_time = (
total_time_so_far / log_iteration * nb_steps // log_interval
)
if DEBUG:
print(
"Average time per log_iteration: "
+ str(total_time_so_far / log_iteration)
)
time_left = round((total_time - total_time_so_far) / 3600, 2)
time_unit = "hours"
if time_left < 1:
time_left = round(time_left * 60, 2)
time_unit = "minutes"
print("Time left = " + str(time_left) + " " + time_unit)
print(
"Total Time Taken: "
+ str(round(total_time_so_far / 60, 1))
+ " minutes"
)
# ---------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------
# save model checkpoints
# model_checkpoint_base_name = CACHE_PATH + TODAY + \
# '/' + cache_prefix + '_{}_model_weights_step_{}.h5'
# self.generator.save_weights(
# model_checkpoint_base_name.format('self.generator',
# epoch_number))
# self.discriminator.save_weights(
# model_checkpoint_base_name.format('discriminator',
# epoch_number))
print("Epoch#: " + str(epoch_number) + " completed")
epoch_number = epoch_number + 1
# if PLOT_AFTER_EPOCH:
# PlotData(
# x,
# g_z,
# data_cols=data_cols,
# label_cols=label_cols,
# seed=0,
# with_class=with_class,
# data_dim=data_dim,
# save=False,
# list_batch_iteration=list_batch_iteration,
# Disc_G_Z_Real_acc_=Disc_G_Z_Real_acc_,
# Disc_G_Z_N_acc_=Disc_G_Z_N_acc_,
# Disc_G_Z_B_acc_=Disc_G_Z_B_acc_,
# Disc_N_acc_=Disc_N_acc_,
# Disc_B_acc_=Disc_B_acc_,
# xgb_acc=xgb_acc,
# dt_acc=dt_acc,
# nb_acc=nb_acc,
# knn_acc=knn_acc,
# rf_acc=rf_acc,
# lr_acc=lr_acc,
# xgb_rcl=xgb_rcl,
# dt_rcl=dt_rcl,
# nb_rcl=nb_rcl,
# knn_rcl=knn_rcl,
# rf_rcl=rf_rcl,
# lr_rcl=lr_rcl,
# xgb_c2st=xgb_c2st,
# dt_c2st=dt_c2st,
# nb_c2st=nb_c2st,
# knn_c2st=knn_c2st,
# rf_c2st=rf_c2st,
# lr_c2st=lr_c2st,
# epoch_list_disc_loss_real=epoch_list_disc_loss_real,
# epoch_list_disc_loss_generated=
# epoch_list_disc_loss_generated,
# epoch_list_gen_loss=epoch_list_gen_loss,
# epoch_list_disc_acc_real=epoch_list_disc_acc_real,
# epoch_list_disc_acc_generated=
# epoch_list_disc_acc_generated,
# epoch_list_gen_acc=epoch_list_gen_acc,
# list_log_iteration=list_log_iteration,
# figs_path=FIGS_PATH,
# cache_prefix=cache_prefix,
# EVALUATION_PARAMETER=EVALUATION_PARAMETER,
# save_fig=False,
# GAN_type='ACGAN_CV',
# TODAY=TODAY)
epoch_number = 0
# if DEBUG:
# print('Plotting...\n')
# PlotData(x,
# g_z,
# data_cols=data_cols,
# label_cols=label_cols,
# seed=0,
# with_class=with_class,
# data_dim=data_dim,
# save=False,
# list_batch_iteration=list_batch_iteration,
# Disc_G_Z_Real_acc_=Disc_G_Z_Real_acc_,
# Disc_G_Z_N_acc_=Disc_G_Z_N_acc_,
# Disc_G_Z_B_acc_=Disc_G_Z_B_acc_,
# Disc_N_acc_=Disc_N_acc_,
# Disc_B_acc_=Disc_B_acc_,
# xgb_acc=xgb_acc,
# dt_acc=dt_acc,
# nb_acc=nb_acc,
# knn_acc=knn_acc,
# rf_acc=rf_acc,
# lr_acc=lr_acc,
# xgb_rcl=xgb_rcl,
# dt_rcl=dt_rcl,
# nb_rcl=nb_rcl,
# knn_rcl=knn_rcl,
# rf_rcl=rf_rcl,
# lr_rcl=lr_rcl,
# xgb_c2st=xgb_c2st,
# dt_c2st=dt_c2st,
# nb_c2st=nb_c2st,
# knn_c2st=knn_c2st,
# rf_c2st=rf_c2st,
# lr_c2st=lr_c2st,
# epoch_list_disc_loss_real=epoch_list_disc_loss_real,
# epoch_list_disc_loss_generated=epoch_list_disc_loss_generated,
# epoch_list_gen_loss=epoch_list_gen_loss,
# epoch_list_disc_acc_real=epoch_list_disc_acc_real,
# epoch_list_disc_acc_generated=epoch_list_disc_acc_generated,
# epoch_list_gen_acc=epoch_list_gen_acc,
# list_log_iteration=list_log_iteration,
# figs_path=FIGS_PATH,
# cache_prefix=cache_prefix,
# EVALUATION_PARAMETER=EVALUATION_PARAMETER,
# save_fig=True,
# GAN_type='ACGAN_CV',
# TODAY=TODAY)
# save_losses(list_log_iteration=list_log_iteration, xgb_acc= xgb_acc, dt_acc = dt_acc, nb_acc = nb_acc, knn_acc = knn_acc, rf_acc = rf_acc, lr_acc = lr_acc, xgb_rcl= xgb_rcl, dt_rcl = dt_rcl, nb_rcl = nb_rcl, rf_rcl = rf_rcl, lr_rcl = lr_rcl, knn_rcl = knn_rcl,
# best_xgb_acc_index = best_xgb_acc_index, best_xgb_rcl_index = best_xgb_rcl_index,
# best_dt_acc_index = best_dt_acc_index, best_dt_rcl_index = best_dt_rcl_index,
# best_nb_acc_index = best_nb_acc_index, best_nb_rcl_index = best_nb_rcl_index,
# best_rf_acc_index = best_rf_acc_index, best_rf_rcl_index = best_rf_rcl_index,
# best_lr_acc_index = best_lr_acc_index, best_lr_rcl_index = best_lr_rcl_index,
# best_knn_acc_index = best_knn_acc_index, best_knn_rcl_index = best_knn_rcl_index,
# epoch_list_disc_loss_real = epoch_list_disc_loss_real, epoch_list_disc_loss_generated = epoch_list_disc_loss_generated, epoch_list_comb_loss = epoch_list_comb_loss, GAN_type = 'ACGAN')
# save_losses(list_log_iteration=list_log_iteration, xgb_acc= xgb_acc, dt_acc = dt_acc, nb_acc = nb_acc, knn_acc = knn_acc, rf_acc = rf_acc, lr_acc = lr_acc, xgb_rcl= xgb_rcl, dt_rcl = dt_rcl, nb_rcl = nb_rcl, rf_rcl = rf_rcl, lr_rcl = lr_rcl, knn_rcl = knn_rcl,
# best_xgb_acc_index = best_xgb_acc_index, best_xgb_rcl_index = best_xgb_rcl_index,
# best_dt_acc_index = best_dt_acc_index, best_dt_rcl_index = best_dt_rcl_index,
# best_nb_acc_index = best_nb_acc_index, best_nb_rcl_index = best_nb_rcl_index,
# best_rf_acc_index = best_rf_acc_index, best_rf_rcl_index = best_rf_rcl_index,
# best_lr_acc_index = best_lr_acc_index, best_lr_rcl_index = best_lr_rcl_index,
# best_knn_acc_index = best_knn_acc_index, best_knn_rcl_index = best_knn_rcl_index,
# epoch_list_disc_loss_real = epoch_list_disc_loss_real, epoch_list_disc_loss_generated = epoch_list_disc_loss_generated, epoch_list_comb_loss = epoch_list_comb_loss, GAN_type = 'GAN')
log_iteration = 0
epoch_number = 0
Disc_G_Z_Real_acc_ = pd.DataFrame(Disc_G_Z_Real_acc_, columns=["GEN_Validity"])
Disc_N_acc_ = pd.DataFrame(Disc_N_acc_, columns=["ONE_Est"])
Disc_B_acc_ = pd.DataFrame(Disc_B_acc_, columns=["ZERO_Eva"])
D_Loss_Real = pd.DataFrame(list_loss_real, columns=["D_Loss_Real"])
D_Loss_Fake = pd.DataFrame(list_loss_fake, columns=["D_Loss_Fake"])
G_Loss = pd.DataFrame(list_loss_g, columns=["G_Loss"])
frames = [
Disc_G_Z_Real_acc_,
Disc_N_acc_,
Disc_B_acc_,
D_Loss_Real,
D_Loss_Fake,
G_Loss,
]
LISTS = pd.concat(frames, sort=True, axis=1).to_csv(
CACHE_PATH + TODAY + "/ACGAN_CV_LISTS.csv"
)
print("Finished..")
return [
best_xgb_acc_index,
best_xgb_rcl_index,
best_dt_acc_index,
best_dt_rcl_index,
best_nb_acc_index,
best_nb_rcl_index,
best_rf_acc_index,
best_rf_rcl_index,
best_lr_acc_index,
best_lr_rcl_index,
best_knn_acc_index,
best_knn_rcl_index,
]
def train_ACGAN_CV(
arguments, train, Train, Test, data_cols, label_cols=[], seed=0, starting_step=0
):
[
rand_noise_dim,
nb_steps,
batch_size,
k_d,
k_g,
critic_pre_train_steps,
log_interval,
learning_rate,
base_n_count,
CACHE_PATH,
FIGS_PATH,
show,
test_size,
gpu_device,
EVALUATION_PARAMETER,
TODAY,
] = arguments
with_class = False
# np.random.seed(seed) # set random seed
data_dim = len(data_cols)
# print('data_dim: ', data_dim)
# print('data_cols: ', data_cols)
label_dim = 0
cache_prefix = "ACGAN_CV"
(
comb_loss,
disc_loss_generated,
disc_loss_real,
xgb_losses,
dt_losses,
nb_losses,
knn_losses,
rf_losses,
lr_losses,
epoch_list_disc_loss_real,
epoch_list_disc_loss_generated,
epoch_list_comb_loss,
) = ([], [], [], [], [], [], [], [], [], [], [], [])
model_components = [
cache_prefix,
with_class,
starting_step,
train,
Train,
Test,
data_cols,
data_dim,
label_cols,
label_dim,
rand_noise_dim,
nb_steps,
batch_size,
k_d,
k_g,
critic_pre_train_steps,
log_interval,
learning_rate,
base_n_count,
CACHE_PATH,
FIGS_PATH,
show,
comb_loss,
disc_loss_generated,
disc_loss_real,
xgb_losses,
dt_losses,
nb_losses,
knn_losses,
rf_losses,
lr_losses,
test_size,
epoch_list_disc_loss_real,
epoch_list_disc_loss_generated,
epoch_list_comb_loss,
gpu_device,
EVALUATION_PARAMETER,
TODAY,
]
[
best_xgb_acc_index,
best_xgb_rcl_index,
best_dt_acc_index,
best_dt_rcl_index,
best_nb_acc_index,
best_nb_rcl_index,
best_rf_acc_index,
best_rf_rcl_index,
best_lr_acc_index,
best_lr_rcl_index,
best_knn_acc_index,
best_knn_rcl_index,
] = ACGAN_CV(IMG_SHAPE=len(data_cols)).train(model_components)
return [
best_xgb_acc_index,
best_xgb_rcl_index,
best_dt_acc_index,
best_dt_rcl_index,
best_nb_acc_index,
best_nb_rcl_index,
best_rf_acc_index,
best_rf_rcl_index,
best_lr_acc_index,
best_lr_rcl_index,
best_knn_acc_index,
best_knn_rcl_index,
]