color_norm_ratio = visual_util.make_color_norm(post_uncn_dict["noise"] /
post_uncn_dict["overall"],
method="percentile")
color_norm_pred = visual_util.make_color_norm(
list(post_mean_dict.values())[:2], # exclude "resid" vales from pal
method="percentile")
""" 3.1. posterior predictive uncertainty """
for unc_name, unc_value in post_uncn_dict.items():
save_name = os.path.join(
_SAVE_ADDR_PREFIX,
'{}/ensemble_posterior_uncn_{}.png'.format(family_name, unc_name))
color_norm = visual_util.posterior_heatmap_2d(unc_value,
X=X_valid,
X_monitor=X_train,
cmap='inferno_r',
norm=color_norm_unc,
norm_method="percentile",
save_addr=save_name)
""" 3.2. posterior predictive mean """
for mean_name, mean_value in post_mean_dict.items():
save_name = os.path.join(
_SAVE_ADDR_PREFIX,
'{}/ensemble_posterior_mean_{}.png'.format(family_name, mean_name))
color_norm = visual_util.posterior_heatmap_2d(mean_value,
X=X_valid,
X_monitor=X_train,
cmap='RdYlGn_r',
norm=color_norm_pred,
norm_method="percentile",
save_addr=save_name)
for i, model in enumerate(_MODEL_DICTIONARY['root']):
weights_dict[model] = weights[:, i]
# prepare color norms for plt.scatter
color_norm_weights = visual_util.make_color_norm(
list(weights_dict.values())[:1],
method="percentile")
for model_name, model_weight in weights_dict.items():
save_name = os.path.join(_SAVE_ADDR_PREFIX,
'{}/ensemble_weights_val_{}.png'.format(
family_name, model_name))
color_norm = visual_util.posterior_heatmap_2d(model_weight,
X=X_valid, X_monitor=X_train,
cmap='viridis',
norm=color_norm_weights,
norm_method="percentile",
save_addr=save_name)
# prepare color norms for plt.scatter
# color_norm_unc = visual_util.make_color_norm(
# list(post_uncn_dict.values())[:1], # use "overall" and "mean" for pal
# method="percentile")
# color_norm_ratio = visual_util.make_color_norm(
# post_uncn_dict["noise"] / post_uncn_dict["overall"],
# method="percentile")
# color_norm_pred = visual_util.make_color_norm(
# list(post_mean_dict.values())[:2], # exclude "resid" vales from pal
# method="percentile")
""" 3.1. posterior predictive uncertainty """