/
train.py
304 lines (240 loc) · 11 KB
/
train.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
"""
Procedure for calibrating generative models using the unconditional Sig-Wasserstein metric.
"""
import os
from os import path as pt
from typing import Optional
import argparse
from lib.augmentations import parse_augmentations
from lib.networks import get_generator, get_discriminator
from lib.utils import to_numpy, set_seed, save_obj, load_obj
from lib.trainers.sig_wgan import SigWGANTrainer
from lib.trainers.wgan import WGANTrainer
from lib.test_metrics import get_standard_test_metrics
from lib.datasets import rolling_window, get_dataset, train_test_split
from lib.trainers.sig_wgan import compute_expected_signature
import itertools
import matplotlib.pyplot as plt
import numpy as np
import torch
from evaluate import evaluate_generator
def plot_signature(sig):
plt.plot(to_numpy(sig).T, 'o')
def plot_test_metrics(test_metrics, losses_history, mode):
fig, axes = plt.subplots(len(test_metrics), 1, figsize=(10, 8))
for i, test_metric in enumerate(test_metrics):
name = test_metric.name
loss = losses_history[name + '_' + mode]
try:
loss = np.concatenate(loss, 1).T
except:
loss = np.array(loss)
axes[i].plot(loss, label=name)
axes[i].grid()
axes[i].legend()
axes[i].set_ylim(bottom=0.)
if i == len(test_metrics):
axes[i].set_xlabel('Number of generator weight updates')
def main(
data_config: dict,
dataset: str,
experiment_dir: str,
gan_algo: str,
gan_config: dict,
generator_config: dict,
device: str = 'cpu',
discriminator_config: Optional = None,
seed: Optional[int] = 0
):
"""
Full training procedure.
Includes: initialising the dataset / generator / GAN and training the GAN.
"""
n_lags = data_config.pop("n_lags")
# Get / prepare dataset
x_real = get_dataset(dataset, data_config, n_lags=n_lags)
x_real = x_real.to(device)
set_seed(seed)
#x_real_rolled = rolling_window(x_real, n_lags, )
x_real_rolled = x_real
x_real_train, x_real_test = train_test_split(x_real_rolled, train_test_ratio=0.8)
x_real_dim: int = x_real.shape[2]
# Compute test metrics for train and test set
test_metrics_train = get_standard_test_metrics(x_real_train)
test_metrics_test = get_standard_test_metrics(x_real_test)
# Get generator
set_seed(seed)
generator_config.update(output_dim=x_real_dim)
G = get_generator(**generator_config).to(device)
# Get GAN
if gan_algo == 'SigWGAN':
trainer = SigWGANTrainer(G=G,
x_real_rolled=x_real_rolled,
test_metrics_train=test_metrics_train,
test_metrics_test=test_metrics_test,
**gan_config
)
elif gan_algo == 'WGAN':
set_seed(seed)
discriminator_config.update(input_dim=x_real_dim * n_lags)
D = get_discriminator(**discriminator_config)
trainer = WGANTrainer(D, G,
x_real=x_real_rolled,
test_metrics_train=test_metrics_train,
test_metrics_test=test_metrics_test,
**gan_config
)
else:
raise NotImplementedError()
# Start training
set_seed(seed)
trainer.fit(device=device)
# Store relevant training results
save_obj(to_numpy(x_real), pt.join(experiment_dir, 'x_real.pkl'))
save_obj(to_numpy(x_real_test), pt.join(experiment_dir, 'x_real_test.pkl'))
save_obj(to_numpy(x_real_train), pt.join(experiment_dir, 'x_real_train.pkl'))
save_obj(trainer.losses_history, pt.join(experiment_dir, 'losses_history.pkl')) # dev of losses / metrics
save_obj(trainer.G.state_dict(), pt.join(experiment_dir, 'generator_state_dict.pt'))
save_obj(generator_config, pt.join(experiment_dir, 'generator_config.pkl'))
if gan_algo == 'SigWGAN':
plt.plot(trainer.losses_history['sig_w1_loss'], alpha=0.8)
plt.grid()
plt.yscale('log')
plt.savefig(pt.join(experiment_dir, 'sig_loss.png'))
plt.close()
else:
plt.plot(trainer.losses_history['D_loss_fake'])
plt.plot(trainer.losses_history['D_loss_real'])
plt.plot(np.array(trainer.losses_history['D_loss_real'])+np.array(trainer.losses_history['D_loss_fake']))
plt.savefig(pt.join(experiment_dir, 'wgan_loss.png'))
plt.close()
plot_test_metrics(trainer.test_metrics_train, trainer.losses_history, 'train')
plt.savefig(pt.join(experiment_dir, 'loss_development_train.png'))
plt.close()
plot_test_metrics(trainer.test_metrics_train, trainer.losses_history, 'test')
plt.savefig(pt.join(experiment_dir, 'loss_development_test.png'))
plt.close()
with torch.no_grad():
x_fake = G(1024, n_lags, device)
for i in range(x_real_dim):
plt.plot(to_numpy(x_fake[:250, :, i]).T, 'C%s' % i, alpha=0.1)
plt.savefig(pt.join(experiment_dir, 'x_fake.png'))
plt.close()
for i in range(x_real_dim):
random_indices = torch.randint(0, x_real_rolled.shape[0], (250,))
plt.plot(to_numpy(x_real_rolled[random_indices, :, i]).T, 'C%s' % i, alpha=0.1)
plt.savefig(pt.join(experiment_dir, 'x_real.png'))
plt.close()
evaluate_generator(experiment_dir, batch_size=5000,)
if gan_algo == 'WGAN':
save_obj(trainer.D.state_dict(), pt.join(experiment_dir, 'discriminator_state_dict.pt'))
save_obj(generator_config, pt.join(experiment_dir, 'discriminator_config.pkl'))
else:
plot_signature(trainer.sig_w1_metric.expected_signature_mu)
plt.savefig(pt.join(experiment_dir, 'sig_real.png'))
plt.close()
plot_signature(trainer.sig_w1_metric.expected_signature_mu)
plot_signature(compute_expected_signature(x_fake, trainer.sig_w1_metric.depth, trainer.sig_w1_metric.augmentations))
plt.savefig(pt.join(experiment_dir, 'sig_real_fake.png'))
plt.close()
pass
def get_config_path(config, dataset):
return './configs/{dataset}/{config}.json'.format(config=config, dataset=dataset)
def get_config_path_generator(config, dataset):
return './configs/{dataset}/generator/{config}.json'.format(
dataset=dataset, config=config
)
def get_config_path_discriminator(config, dataset):
return './configs/{dataset}/discriminator/{config}.json'.format(config=config, dataset=dataset)
def get_sigwgan_experiment_dir(dataset, generator, gan, seed):
return './numerical_results/{dataset}/{gan}_{generator}_{seed}'.format(
dataset=dataset, gan=gan, generator=generator, seed=seed)
def get_wgan_experiment_dir(dataset, discriminator, generator, gan, seed):
return './numerical_results/{dataset}/{gan}_{generator}_{discriminator}_{seed}'.format(
dataset=dataset, gan=gan, generator=generator, discriminator=discriminator, seed=seed)
list_of_datasets = ('GBM', 'STOCKS', 'ECG')
def benchmark_wgan(
datasets=list_of_datasets,
discriminators=('ResFNN',),
generators=('LSTM', 'NSDE',),
n_seeds=10,
device='cuda:0',
):
""" Benchmark for WGAN. """
seeds = list(range(n_seeds))
grid = itertools.product(datasets, discriminators, generators, seeds)
for dataset, discriminator, generator, seed in grid:
data_config = load_obj(get_config_path(dataset, dataset))
discriminator_config = load_obj(get_config_path_discriminator(discriminator, dataset))
gan_config = load_obj(get_config_path('WGAN', dataset))
generator_config = load_obj(get_config_path_generator(generator, dataset))
if generator_config.get('augmentations') is not None:
generator_config['augmentations'] = parse_augmentations(generator_config.get('augmentations'))
if generator_config['generator_type'] == 'LogSigRNN':
generator_config['n_lags'] = data_config['n_lags']
experiment_dir = get_wgan_experiment_dir(dataset, discriminator, generator, 'WGAN', seed)
if not pt.exists(experiment_dir):
os.makedirs(experiment_dir)
save_obj(data_config, pt.join(experiment_dir, 'data_config.pkl'))
save_obj(discriminator_config, pt.join(experiment_dir, 'discriminator_config.pkl'))
save_obj(gan_config, pt.join(experiment_dir, 'gan_config.pkl'))
save_obj(generator_config, pt.join(experiment_dir, 'generator_config.pkl'))
print('Training: %s' % experiment_dir.split('/')[-2:])
main(
dataset=dataset,
data_config=data_config,
device=device,
experiment_dir=experiment_dir,
gan_algo='WGAN',
seed=seed,
discriminator_config=discriminator_config,
gan_config=gan_config,
generator_config=generator_config
)
def benchmark_sigwgan(
datasets=list_of_datasets,
generators=('LSTM', 'NSDE',),
n_seeds=10,
device='cuda:0',
):
""" Benchmark for SigWGAN. """
seeds = list(range(n_seeds))
grid = itertools.product(datasets, generators, seeds)
for dataset, generator, seed in grid:
data_config = load_obj(get_config_path(dataset, dataset))
gan_config = load_obj(get_config_path('SigWGAN', dataset))
generator_config = load_obj(get_config_path_generator(generator, dataset))
if gan_config.get('augmentations') is not None:
gan_config['augmentations'] = parse_augmentations(gan_config.get('augmentations'))
if generator_config.get('augmentations') is not None:
generator_config['augmentations'] = parse_augmentations(generator_config.get('augmentations'))
if generator_config['generator_type'] == 'LogSigRNN':
generator_config['n_lags'] = data_config['n_lags']
experiment_dir = get_sigwgan_experiment_dir(dataset, generator, 'SigWGAN', seed)
if not pt.exists(experiment_dir):
os.makedirs(experiment_dir)
save_obj(data_config, pt.join(experiment_dir, 'data_config.pkl'))
save_obj(gan_config, pt.join(experiment_dir, 'gan_config.pkl'))
save_obj(generator_config, pt.join(experiment_dir, 'generator_config.pkl'))
print('Training: %s' % experiment_dir.split('/')[-2:])
main(
dataset=dataset,
data_config=data_config,
device=device,
experiment_dir=experiment_dir,
gan_algo='SigWGAN',
seed=seed,
gan_config=gan_config,
generator_config=generator_config,
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=int, default=0)
args = parser.parse_args()
if torch.cuda.is_available():
device = 'cuda:{}'.format(args.device)
else:
device = 'cpu'
# Test run
benchmark_sigwgan(datasets=('STOCKS', 'GBM'), generators=('LogSigRNN', 'LSTM',), n_seeds=1, device=device)
benchmark_wgan(datasets=('STOCKS', 'GBM'), generators=('LogSigRNN', 'LSTM'), n_seeds=1, device=device)