def make_intercept_regression_plot(expname, default, center, affine, index, index_test, savedir):
rank = comm.get_rank()
if center is not None:
comm.resize(center.world_size)
default_reg = get_regression(default, index, index_test)
center_reg = get_regression(center, index, index_test)
comm.reset()
if affine is not None:
affine_reg = get_regression(affine, index, index_test)
if rank == 0:
# Regression Comp Plot
fig, ax = plt.subplots(1,1)
ax.set_ylabel('Meter Voltage (V)')
plot_train_test(ax, center, index, index_test)
t = np.linspace(0, 24, len(index)+len(index_test))
ax.plot(t, default_reg.T, label=f'Regression - Default', linestyle='--', color='tab:purple')
ax.plot(t, center_reg.T, label=f'Regression - Center', linestyle='-', color='black')
if affine is not None:
ax.plot(t, affine_reg.T, label=f'Regression - Affine', linestyle=':', color='tab:green')
ax.legend()
ax.set_xlabel('Time (h)')
fig.tight_layout()
fig.savefig(os.path.join(savedir, f'{expname}regression.png'), dpi=300)
plt.close(fig)
def make_thm1_plot(expname, default, center, savedir, no_reg=False):
rank = comm.get_rank()
if rank == 0:
fig, ax = plt.subplots(1, 2)
ax[0].set_xlabel('Iterations')
ax[1].set_xlabel('Iterations')
for i, mon in enumerate([default, center]):
comm.barrier()
if mon is None:
continue
size = mon.world_size
comm.resize(size)
if rank == 0:
global_data = get_dataframe(mon)
local_data = get_dataframe(mon, local=True)
local_data.replace(np.nan, 0)
if no_reg:
local_subproblem = np.array(local_data['subproblem'] -
local_data['gk'])
else:
local_subproblem = np.array(local_data['subproblem'])
local_subproblem = comm.reduce(local_subproblem, op='SUM', root=0)
if rank == 0:
ax[i].set_xlabel("Iterations")
iters = np.asarray(local_data['i_iter'])
label = r"$\sum_k \Gamma_{k}^{\sigma'}" + (r'-g_{[k]}$'
if no_reg else "$")
ax[i].semilogy(iters,
local_subproblem,
color='tab:cyan',
linestyle='--',
label=label)
y_axis = 'f' if no_reg else 'P'
label = r"$f(Ax)$" if no_reg else r"$\mathcal{O}_A(x)$"
ax[i].semilogy('i_iter',
y_axis,
'',
data=global_data,
color='tab:orange',
label=label)
if rank == 0:
ax[1].legend(loc='best')
fig.tight_layout()
suf = '_no_reg' if no_reg else ''
fig.savefig(os.path.join(savedir, f'{expname}thm1{suf}.png'), dpi=300)
plt.close(fig)
comm.reset()
def make_stop_plot(expname, default, center, savedir):
rank = comm.get_rank()
for mon in [default, center]:
comm.barrier()
if mon is None:
continue
size = mon.world_size
comm.resize(size)
global_data = get_dataframe(mon)
local_data = get_dataframe(mon, local=True)
sendbuf = np.array(local_data['delta_xk'])
local_updates = None
if rank == 0:
local_updates = np.empty([size, len(sendbuf)])
comm.comm.Gather(sendbuf, local_updates, root=0)
if rank == 0:
fig, ax_l = plt.subplots(1, 1)
ax_r = plt.twinx(ax=ax_l)
ax_l.set_xlabel('Iterations')
ax_l.set_ylabel(r'$\|\|\Delta x_{k}\|\|$')
ax_r.set_ylabel(r'$f(Ax)$')
iters = np.asarray(local_data['i_iter'])
for k in range(size):
ax_l.semilogy(iters,
local_updates[k, :],
linestyle='--',
label=r'$\|\|\Delta x_{k}\|\|$')
ax_r.plot('i_iter',
'f',
'',
data=global_data,
color='black',
label='$f(Ax)$')
ymin, ymax = ax_r.get_ylim()
dist = ymax - ymin
if dist < 0.01:
ymax += (0.01 - dist) / 2
ymin -= (0.01 - dist) / 2
ax_r.set_ylim(ymin, ymax)
fig.tight_layout()
fig.savefig(os.path.join(savedir, f'{expname}{mon.name}_stop.png'),
dpi=300)
plt.close(fig)
comm.reset()
def make_intercept_local_cert_plot(expname, default, center, savedir, type='gap'):
rank = comm.get_rank()
for mon in [default, center]:
comm.barrier()
if mon is None:
continue
size = mon.world_size
comm.resize(size)
global_data = get_dataframe(mon)
local_data = get_dataframe(mon, local=True)
sendbuf = np.abs(np.array(local_data[f'cert_{type}']))
local_updates = None
if rank == 0:
local_updates = np.empty([size, len(sendbuf)])
comm.comm.Gather(sendbuf, local_updates, root=0)
if rank == 0:
fig, ax_l = plt.subplots(1, 1)
fig.set_size_inches(6.5, 3.5)
ax_r = plt.twinx(ax=ax_l)
ax_l.set_xlabel('Iterations')
label = 'Local Gap' if type=='gap' else 'Local CV'
ax_l.set_ylabel(label)
ax_r.set_ylabel(r'$f(Ax)$')
iters = np.asarray(local_data['i_iter'])
for k in range(size):
ax_l.semilogy(iters, local_updates[k,:], linestyle='--', label=label)
data = np.abs(global_data['gap'])
ax_r.semilogy('i_iter', data, '', data=global_data, color='black', label='$f(Ax)$')
fig.tight_layout()
fig.savefig(os.path.join(savedir, f'{expname}{mon.name}_cert_{type}.png'), dpi=300)
plt.close(fig)
comm.reset()