def train_system(self):
for epoch in range(self.num_epochs):
for batch_idx, real in enumerate(self.dl):
stock, dt = real
stock = stock.view(*stock.shape, 1)
stock = stock.float()
stock = stock.to(self.device)
mean = stock.mean()
dt = dt.view(*dt.shape, 1)
dt = dt.float()
dt = dt.to(self.device)
noise_stock = self.dist_latent.sample(
sample_shape=(self.batch_size, self.seq_len,
self.g_dim_latent))
noise_stock = noise_stock.to(self.device)
loss_g, _ = self.train_generator(noise_stock, dt, mean)
loss_d, fake = self.train_discriminator(
noise_stock, stock, dt, mean)
if batch_idx == 0:
print(
f"Epoch [{epoch}/{self.num_epochs}] Batch {batch_idx}/{len(self.dl)} \
Loss D: {loss_d:.4f}, loss G: {loss_g:.4f}")
wandb.log({
'epoch':
epoch,
'd loss':
loss_d,
'g loss':
loss_g,
'mae':
mean_absolute_error(
stock[0].view(-1).cpu().numpy(),
fake[0].view(-1).detach().cpu().numpy()),
# torch.abs(fake - stock).mean().item() => mae
'Conditional on deltas fake sample':
plot_time_series(
fake[0].view(-1).detach().cpu().numpy(),
'[Conditional (on deltas)] Fake sample'),
'Real sample':
plot_time_series(stock[0].view(-1).cpu().numpy(),
'[Corresponding] Real sample')
})
def train_system(self):
for epoch in range(self.num_epochs):
for batch_idx, real in enumerate(self.dl):
real_data, dt = real
real_data = real_data.view(*real_data.shape, 1)
real_data = real_data.float()
real_data = real_data.to(self.device)
dt = dt.view(*dt.shape, 1)
dt = dt.float()
dt = dt.to(self.device)
noise = self.dist_latent.sample(sample_shape=(self.batch_size, self.seq_len, self.g_dim_latent))
noise = noise.to(self.device)
g_iter = self.g_iter
d_iter = self.d_iter
for var_name in range(g_iter):
loss_g, _ = self.train_generator(noise, real_data, dt)
for var_name in range(d_iter):
loss_d, fake = self.train_discriminator(noise, real_data, dt)
if batch_idx == 0:
print(
f"Epoch [{epoch}/{self.num_epochs}] Batch {batch_idx}/{len(self.dl)} \
Loss D: {loss_d.detach().cpu().item():.4f}, loss G: {loss_g.detach().cpu().item():.4f}"
)
# Visualize the whole distribution
rd, gd = self.generate_distributions()
fig = visualization_metrics.visualize(gd, rd)
wandb.log({
'epoch': epoch,
'd loss': loss_d.detach().cpu().item(),
'g loss': loss_g.detach().cpu().item(),
'mae': mean_absolute_error(real_data[0].view(-1).cpu().numpy(),
fake[0].view(-1).detach().cpu().numpy()),
'Conditional on deltas fake sample': plot_time_series(
fake[0].view(-1).detach().cpu().numpy(),
'[Conditional (on deltas)] Fake sample'),
'Real sample': plot_time_series(
real_data[0].view(-1).cpu().numpy(),
'[Corresponding] Real sample'),
'Distribution': fig
})
def analyse_owd(test_id='', out_dir='', replot_only='0', source_filter='',
min_values='3', omit_const='0', ymin='0', ymax='0',
lnames='', stime='0.0', etime='0.0', out_name='', pdf_dir='',
ts_correct='1',plot_params='', plot_script='', burst_sep='0.0',
sburst='1', eburst='0', seek_window='', anchor_map='', owd_midpoint='0'):
"Plot OWD of flows"
# Note we allow ts_correct as a parameter for syntactic similarity to other
# analyse_* tasks, but abort with warning if user tries explicitly to
# make it 0 (which is unacceptable for OWD calculations)
if ts_correct == '0':
abort("Warning: Cannot do OWD calculations with ts_correct=0")
(test_id_arr,
out_files,
out_groups) = _extract_owd_pktloss(test_id, out_dir, replot_only,
source_filter, ts_correct,
burst_sep, sburst, eburst,
seek_window, log_loss='0',
anchor_map=anchor_map,
owd_midpoint = owd_midpoint)
(out_files, out_groups) = filter_min_values(out_files, out_groups, min_values)
out_name = get_out_name(test_id_arr, out_name)
burst_sep = float(burst_sep)
if burst_sep == 0.0:
plot_time_series(out_name, out_files, 'OWD (ms)', 2, 1000.0, 'pdf',
out_name + '_owd', pdf_dir=pdf_dir, omit_const=omit_const,
ymin=float(ymin), ymax=float(ymax), lnames=lnames,
stime=stime, etime=etime, groups=out_groups, plot_params=plot_params,
plot_script=plot_script, source_filter=source_filter)
else:
# Each trial has multiple files containing data from separate bursts detected within the trial
plot_incast_ACK_series(out_name, out_files, 'OWD (ms)', 2, 1000.0, 'pdf',
out_name + '_owd', pdf_dir=pdf_dir, aggr='',
omit_const=omit_const, ymin=float(ymin), ymax=float(ymax),
lnames=lnames, stime=stime, etime=etime, groups=out_groups, burst_sep=burst_sep,
sburst=int(sburst), plot_params=plot_params, plot_script=plot_script,
source_filter=source_filter)
# done
puts('\n[MAIN] COMPLETED plotting OWDs %s \n' % out_name)
def analyse_pktloss(test_id='', out_dir='', replot_only='0', source_filter='',
min_values='3', omit_const='0', ymin='0', ymax='0',
lnames='', stime='0.0', etime='0.0', out_name='', pdf_dir='',
ts_correct='1', plot_params='', plot_script='', burst_sep='0.0',
sburst='1', eburst='0', seek_window='', log_loss='2'):
"Plot per-flow packet loss events vs time (or cumlative over time)"
if log_loss != '1' and log_loss !='2':
abort("Must set log_loss=1 (pkt loss events) or log_loss=2 (cumulative pkt loss)")
(test_id_arr,
out_files,
out_groups) = _extract_owd_pktloss(test_id, out_dir, replot_only,
source_filter, ts_correct,
burst_sep, sburst, eburst,
seek_window, log_loss)
(out_files, out_groups) = filter_min_values(out_files, out_groups, min_values)
out_name = get_out_name(test_id_arr, out_name)
burst_sep = float(burst_sep)
if burst_sep == 0.0:
plot_time_series(out_name, out_files, 'Lost packets', 2, 1, 'pdf',
out_name + '_loss2', pdf_dir=pdf_dir, omit_const=omit_const,
ymin=float(ymin), ymax=float(ymax), lnames=lnames,
stime=stime, etime=etime, groups=out_groups, plot_params=plot_params,
plot_script=plot_script, source_filter=source_filter)
else:
# Each trial has multiple files containing data from separate bursts detected within the trial
plot_incast_ACK_series(out_name, out_files, 'Lost packets', 2, 1, 'pdf',
out_name + '_loss2', pdf_dir=pdf_dir, aggr='',
omit_const=omit_const, ymin=float(ymin), ymax=float(ymax),
lnames=lnames, stime=stime, etime=etime, groups=out_groups, burst_sep=burst_sep,
sburst=int(sburst), plot_params=plot_params, plot_script=plot_script,
source_filter=source_filter)
# done
puts('\n[MAIN] COMPLETED plotting pktloss %s \n' % out_name)