From 20359f86a41d5b884da649c942db4f8e5efc2b6c Mon Sep 17 00:00:00 2001
From: Joerg Martin <joerg.martin@ptb.de>
Date: Tue, 8 Feb 2022 14:10:15 +0000
Subject: [PATCH] Added zoom prediction plot
---
Experiments/plot_prediction.py | 70 +++++++++++++++++++++++++++++++---
1 file changed, 64 insertions(+), 6 deletions(-)
diff --git a/Experiments/plot_prediction.py b/Experiments/plot_prediction.py
index a603d78..a574c52 100644
--- a/Experiments/plot_prediction.py
+++ b/Experiments/plot_prediction.py
@@ -11,11 +11,20 @@ import json
import torch
import numpy as np
+import matplotlib
import matplotlib.pyplot as plt
from EIVArchitectures import Networks
from EIVTrainingRoutines import train_and_store
+
+font = {'family' : 'DejaVu Sans',
+ 'weight' : 'normal',
+ 'size' : 16}
+
+matplotlib.rc('font', **font)
+linewidth = 2.0
+
# coverage factor
k = 1.96
@@ -107,6 +116,7 @@ def compute_predictions_and_uncertainties(data, x_range, eiv, number_of_draws,
return_ground_truth=False,
return_normalized_func=True,
normalize=normalize)
+ plotting_dictionary['func'] = normalized_func
input_dim = test_data[0][0].numel()
output_dim = test_data[0][1].numel()
assert output_dim == 1
@@ -223,8 +233,19 @@ list_x_range = [torch.linspace(-1.0,1.0, 50),
torch.linspace(-0.2,0.8, 50)]
list_color = [('red','blue')] * len(data_list)
list_number_of_draws = [((100,5), 100)] * len(data_list)
-for i, (data, x_range, color, number_of_draws) in enumerate(zip(data_list,
- list_x_range, list_color, list_number_of_draws)):
+
+# create an extra zoom plot for zoom_example
+zoom_example = 'linear'
+# where to zoom
+zoom_point = 14
+# size of the zoom plot
+x_zoom_radius = 0.4
+y_zoom_radius = x_zoom_radius
+
+fignum = 0
+for data, x_range, color, number_of_draws in zip(data_list,
+ list_x_range, list_color, list_number_of_draws):
+ fignum += 1
eiv_plotting_dictionary = compute_predictions_and_uncertainties(
data=data,
x_range=x_range,
@@ -237,25 +258,62 @@ for i, (data, x_range, color, number_of_draws) in enumerate(zip(data_list,
number_of_draws=number_of_draws[1])
input_dim = eiv_plotting_dictionary['input_dim']
if input_dim == 1:
- plt.figure(i+1)
+ plt.figure(fignum)
plt.clf()
x_values, y_values = eiv_plotting_dictionary['range_points']
- plt.plot(x_values.flatten(), y_values.flatten(),'-', color='k')
+ noisy_x_values, _ = eiv_plotting_dictionary['noisy_range_points']
+ plt.plot(x_values.flatten(), y_values.flatten(),'-', color='k', linewidth=linewidth)
eiv_pred = eiv_plotting_dictionary['prediction']
eiv_unc = eiv_plotting_dictionary['uncertainty']
plt.plot(x_values, eiv_pred,'-',
- color=color[0])
+ color=color[0], linewidth=linewidth)
plt.fill_between(x_values.flatten(), eiv_pred-k * eiv_unc,
eiv_pred + k * eiv_unc,
color=color[0], alpha=0.5)
noneiv_pred = noneiv_plotting_dictionary['prediction']
noneiv_unc = noneiv_plotting_dictionary['uncertainty']
plt.plot(x_values.flatten(), noneiv_pred,'-',
- color=color[1])
+ color=color[1], linewidth=linewidth)
plt.fill_between(x_values.flatten(), noneiv_pred-k * noneiv_unc,
noneiv_pred + k * noneiv_unc,
color=color[1], alpha=0.5)
+ plt.tight_layout()
plt.savefig(f'results/figures/prediction_{data}.pdf')
+ if data == zoom_example:
+ fignum += 1
+ func = eiv_plotting_dictionary['func']
+ x_point = x_values[zoom_point]
+ y_point = func(x_point)
+ noisy_x_point = noisy_x_values[zoom_point]
+ func_noisy_x_point = func(noisy_x_point)
+ plt.figure(fignum)
+ plt.clf()
+ plt.plot(x_values.flatten(), y_values.flatten(),'-', color='k', linewidth=linewidth)
+ plt.plot(x_values, eiv_pred,'-',
+ color=color[0], linewidth=linewidth)
+ plt.fill_between(x_values.flatten(), eiv_pred-k * eiv_unc,
+ eiv_pred + k * eiv_unc,
+ color=color[0], alpha=0.5)
+ plt.plot(x_values.flatten(), noneiv_pred,'-',
+ color=color[1], linewidth=linewidth)
+ plt.fill_between(x_values.flatten(), noneiv_pred-k * noneiv_unc,
+ noneiv_pred + k * noneiv_unc,
+ color=color[1], alpha=0.5)
+ plt.axvline(x_point, color='black', linestyle='dotted')
+ plt.axhline(y_point, color='black', linestyle='dotted')
+ plt.axvline(noisy_x_point, color='gray', linestyle='dashed')
+ plt.axhline(func_noisy_x_point, color='gray', linestyle='dashed')
+ plt.text(x_point - 0.1 * x_zoom_radius,y_point-0.9 * y_zoom_radius, r'$\zeta$', color='k')
+ plt.text(noisy_x_point - 0.1 * x_zoom_radius,y_point-0.9 * y_zoom_radius, r'$x$', color='gray')
+ plt.text(x_point - 0.92 * x_zoom_radius,y_point-0.13 * y_zoom_radius, r'$g(\zeta)$', color='k')
+ plt.text(x_point - 0.92 * x_zoom_radius,func_noisy_x_point-0.13 * y_zoom_radius, r'$g(x)$', color='gray')
+ plt.gca().set_xlim(left=x_point - x_zoom_radius,
+ right=x_point + x_zoom_radius)
+ plt.gca().set_ylim(bottom=y_point - y_zoom_radius,
+ top=y_point + y_zoom_radius)
+ plt.gca().set_aspect('equal', adjustable='box')
+ plt.tight_layout()
+ plt.savefig(f'results/figures/prediction_{data}_zoom.pdf')
else:
# multidimensional handling not included yet
pass
--
GitLab