fig_size = (12, 10)
# Plot
if args.mode == '1D':
fig = plt.figure(figsize=fig_size, constrained_layout=True)
widths = [1, 1] # columnwise
heights = [1]*(dim_cand//2) # rowwise
spec = fig.add_gridspec(nrows=dim_cand//2, ncols=2, width_ratios=widths, height_ratios=heights)
for idx_r, row in enumerate(range(dim_cand//2)):
for idx_c, col in enumerate(range(2)): # 1st col means, 2nd col stds
ax = fig.add_subplot(spec[row, col])
render_singletask_gp(
ax, cands, cands_values, min_gp_obsnoise=1e-5,
idcs_sel=[idx_r*2 + idx_c], x_label='$\phi_{' + f'{idx_r*2 + idx_c}' + '}$',
y_label=r'$\hat{J}^{\textrm{real}}$', curve_label='mean', num_stds=2,
show_legend_posterior=False, show_legend_std=True, show_legend_data=args.verbose
)
elif args.mode == '2D':
fig_mean = plt.figure(figsize=fig_size, constrained_layout=True)
spec = fig_mean.add_gridspec(nrows=1, ncols=2, width_ratios=[12, 1], height_ratios=[1])
ax_hm_mean = fig_mean.add_subplot(spec[0, 0])
ax_cb_mean = fig_mean.add_subplot(spec[0, 1])
fig_std = plt.figure(figsize=fig_size, constrained_layout=True)
spec = fig_std.add_gridspec(nrows=1, ncols=2, width_ratios=[12, 1], height_ratios=[1])
ax_hm_std = fig_std.add_subplot(spec[0, 0])
ax_cb_std = fig_std.add_subplot(spec[0, 1])
render_singletask_gp(
hm_cmap = ListedColormap(sns.color_palette("YlGnBu", n_colors=100)[::-1])
scat_cmap = LinearSegmentedColormap.from_list("light_to_dark_gray", [(0.95, 0.95, 0.95), (0.05, 0.05, 0.05)], N=256)
if len(args.idcs) == 1:
ax.axvline(gt_val_x, c="firebrick", ls="--", lw=1.5, label="gt")
render_singletask_gp(
ax,
cands,
cands_values,
min_gp_obsnoise=1e-5,
data_x_min=ddp_space.bound_lo[args.idcs],
data_x_max=ddp_space.bound_up[args.idcs],
idcs_sel=args.idcs,
x_label=x_label,
y_label=y_label,
z_label=r"r$\hat{J}^{\textrm{real}}$",
heatmap_cmap=hm_cmap,
num_std=2,
resolution=201,
legend_data_cmap=scat_cmap,
show_legend_data=args.verbose,
show_legend_posterior=True,
show_legend_std=True,
render3D=args.render3D,
)
if len(args.idcs) == 2 and not args.render3D:
# Plot the ground truth domain parameter configuration
ax_hm_mean.scatter(gt_val_x, gt_val_y, c="firebrick", marker="*", s=60) # forestgreen
ax_hm_std.scatter(gt_val_x, gt_val_y, c="firebrick", marker="*", s=60) # forestgreen
# hm_cmap = ListedColormap(sns.color_palette("OrRd", n_colors=100)[::-1])
# scat_cmap = LinearSegmentedColormap.from_list('white_to_gray', [(1., 1., 1.), (.4, .4, .4)], N=256)
scat_cmap = LinearSegmentedColormap.from_list('light_to_dark_gray',
[(.8, .8, .8), (.2, .2, .2)],
N=256)
render_singletask_gp(
[ax_hm_mean, ax_cb_mean, ax_hm_std, ax_cb_std],
cands,
cands_values,
min_gp_obsnoise=1e-5,
# data_x_min=bounds[0, args.idcs], data_x_max=bounds[1, args.idcs],
idcs_sel=args.idcs,
x_label=f'$m_p$',
y_label=f'$m_r$',
heatmap_cmap=hm_cmap,
z_label=r'$\hat{J}^{\textrm{real}}$',
num_stds=2,
resolution=151,
legend_data_cmap=scat_cmap,
show_legend_posterior=True,
show_legend_std=True,
show_legend_data=args.verbose,
render_3D=False,
)
# Plot the ground truth domain parameter configuration
ax_hm_mean.scatter(0.026, 0.097, c='firebrick', marker='o',
s=60) # forestgreen
ax_hm_std.scatter(0.026, 0.097, c='firebrick', marker='o',
s=60) # forestgreen
if args.mode == "1D":
fig = plt.figure(figsize=fig_size, constrained_layout=True)
widths = [1, 1] # columnwise
heights = [1] * (dim_cand // 2) # rowwise
spec = fig.add_gridspec(nrows=dim_cand // 2, ncols=2, width_ratios=widths, height_ratios=heights)
for idx_r, row in enumerate(range(dim_cand // 2)):
for idx_c, col in enumerate(range(2)): # 1st col means, 2nd col stds
ax = fig.add_subplot(spec[row, col])
render_singletask_gp(
ax,
cands,
cands_values,
min_gp_obsnoise=1e-5,
idcs_sel=[idx_r * 2 + idx_c],
x_label="$\phi_{" + f"{idx_r*2 + idx_c}" + "}$",
y_label=r"$\hat{J}^{\textrm{real}}$",
curve_label="mean",
num_std=2,
show_legend_posterior=False,
show_legend_std=True,
show_legend_data=args.verbose,
)
elif args.mode == "2D":
fig_mean = plt.figure(figsize=fig_size, constrained_layout=True)
spec = fig_mean.add_gridspec(nrows=1, ncols=2, width_ratios=[12, 1], height_ratios=[1])
ax_hm_mean = fig_mean.add_subplot(spec[0, 0])
ax_cb_mean = fig_mean.add_subplot(spec[0, 1])
fig_std = plt.figure(figsize=fig_size, constrained_layout=True)
spec = fig_std.add_gridspec(nrows=1, ncols=2, width_ratios=[12, 1], height_ratios=[1])