From 6cf3ef4ced94d2490db378e2102734255aed7384 Mon Sep 17 00:00:00 2001
From: Joerg Martin <joerg.martin@ptb.de>
Date: Wed, 9 Feb 2022 16:28:24 +0100
Subject: [PATCH] deviation_scatter added
---
Experiments/plot_coverage_vs_q.py | 44 ++++-----
Experiments/plot_deviation_scatter.py | 80 +++++++++++++++++
Experiments/plot_prediction.py | 125 +++++++++++++-------------
3 files changed, 165 insertions(+), 84 deletions(-)
create mode 100644 Experiments/plot_deviation_scatter.py
diff --git a/Experiments/plot_coverage_vs_q.py b/Experiments/plot_coverage_vs_q.py
index 5cb7f8f..3ae205a 100644
--- a/Experiments/plot_coverage_vs_q.py
+++ b/Experiments/plot_coverage_vs_q.py
@@ -231,25 +231,25 @@ def compute_coverages(data, eiv, number_of_draws):
q_range=q_range,
noisy_y = False)
return numerical_coverage, theoretical_coverage
-
-# loop through data
-for data, color in tqdm(zip(datasets, colors)):
- # compute coverages
- eiv_coverages = compute_coverages(data=data, eiv=True,
- number_of_draws=[100,5])
- noneiv_coverages = compute_coverages(data=data, eiv=False,
- number_of_draws=100)
- # create plots
- coverage_diagonal_plot(eiv_coverages, noneiv_coverages,
- color=color, against_theoretical=False, label=data)
-
-# add diagonal
-x_diag = np.linspace(0.0, 1.0)
-plt.plot(x_diag, x_diag, color='k', linestyle='dotted' )
-
-# add legend
-plt.legend()
-
-# save and show
-plt.savefig('results/figures/true_coverage_vs_q.pdf')
-plt.show()
+if __name__ == '__main__':
+ # loop through data
+ for data, color in tqdm(zip(datasets, colors)):
+ # compute coverages
+ eiv_coverages = compute_coverages(data=data, eiv=True,
+ number_of_draws=[100,5])
+ noneiv_coverages = compute_coverages(data=data, eiv=False,
+ number_of_draws=100)
+ # create plots
+ coverage_diagonal_plot(eiv_coverages, noneiv_coverages,
+ color=color, against_theoretical=False, label=data)
+
+ # add diagonal
+ x_diag = np.linspace(0.0, 1.0)
+ plt.plot(x_diag, x_diag, color='k', linestyle='dotted' )
+
+ # add legend
+ plt.legend()
+
+ # save and show
+ plt.savefig('results/figures/true_coverage_vs_q.pdf')
+ plt.show()
diff --git a/Experiments/plot_deviation_scatter.py b/Experiments/plot_deviation_scatter.py
new file mode 100644
index 0000000..be33ea5
--- /dev/null
+++ b/Experiments/plot_deviation_scatter.py
@@ -0,0 +1,80 @@
+"""
+Plot predictions with uncertainties for (simulated) datasets with a ground
+truth. At the moment it is assumed that the used datasets have an output
+dimension equal to 1. Plots are only produced for datasets with input dimension
+and output dimension 1.
+"""
+import importlib
+import os
+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
+from plot_prediction import compute_predictions_and_uncertainties, data_list, list_x_range, list_number_of_draws
+from plot_coverage_vs_q import colors
+
+font = {'family' : 'DejaVu Sans',
+ 'weight' : 'normal',
+ 'size' : 16*0.80}
+
+matplotlib.rc('font', **font)
+
+
+list_x_range = [torch.linspace(-1.0,1.0, 200),
+ torch.linspace(-1.0,1.0, 200),
+ torch.linspace(-1.0,1.0, 200),
+ torch.linspace(-0.2,0.8, 200)]
+plt.figure(1)
+plt.clf()
+for data, x_range, number_of_draws, color in zip(data_list,
+ list_x_range, list_number_of_draws, colors):
+ eiv_plotting_dictionary = compute_predictions_and_uncertainties(
+ data=data,
+ x_range=x_range,
+ eiv=True,
+ number_of_draws=number_of_draws[0])
+ noneiv_plotting_dictionary = compute_predictions_and_uncertainties(
+ data=data,
+ x_range=x_range,
+ eiv=False,
+ number_of_draws=number_of_draws[1])
+ _, y_values = eiv_plotting_dictionary['range_points']
+ noisy_x_points, _ = eiv_plotting_dictionary['noisy_range_points']
+ func = eiv_plotting_dictionary['func']
+ func_noisy_x_points = func(torch.tensor(noisy_x_points)).numpy()
+ y_values, func_noisy_x_points = y_values.flatten(), func_noisy_x_points.flatten()
+ eiv_pred = eiv_plotting_dictionary['prediction'].flatten()
+ noneiv_pred = noneiv_plotting_dictionary['prediction'].flatten()
+ assert eiv_pred.shape == noneiv_pred.shape
+ assert y_values.shape == eiv_pred.shape
+ assert func_noisy_x_points.shape == eiv_pred.shape
+ eiv_true_deviation = np.abs(eiv_pred - y_values).flatten()
+ eiv_noisy_deviation = np.abs(eiv_pred - func_noisy_x_points).flatten()
+ noneiv_true_deviation = np.abs(noneiv_pred - y_values).flatten()
+ noneiv_noisy_deviation = np.abs(noneiv_pred - func_noisy_x_points).flatten()
+ plt.loglog(noneiv_true_deviation, noneiv_noisy_deviation,'x', color=color, markersize=5.0)
+ plt.loglog(eiv_true_deviation, eiv_noisy_deviation,'+', color=color, markersize=5.0)
+
+
+plt.ylim(bottom=1e-4)
+plt.xlim(left=1e-4)
+plt.xlabel(r'$|$'+'prediction'+r'$-g(\zeta)$'+r'$|$')
+plt.ylabel(r'$|$'+'prediction'+r'$-g(x)$'+r'$|$')
+
+
+
+
+# plt.ylim([0,1.0])
+# plt.xlim([0,1.0])
+x_values = np.linspace(0, 1)
+plt.plot(x_values, x_values, 'k--')
+plt.gca().set_aspect('equal', adjustable='box')
+plt.tight_layout()
+plt.savefig(f'results/figures/deviation_scatter.pdf')
+plt.show()
diff --git a/Experiments/plot_prediction.py b/Experiments/plot_prediction.py
index f1f985f..78e2212 100644
--- a/Experiments/plot_prediction.py
+++ b/Experiments/plot_prediction.py
@@ -242,81 +242,82 @@ zoom_point = 14
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,
- eiv=True,
- number_of_draws=number_of_draws[0])
- noneiv_plotting_dictionary = compute_predictions_and_uncertainties(
- data=data,
- x_range=x_range,
- eiv=False,
- number_of_draws=number_of_draws[1])
- input_dim = eiv_plotting_dictionary['input_dim']
- if input_dim == 1:
- plt.figure(fignum)
- plt.clf()
- x_values, y_values = eiv_plotting_dictionary['range_points']
- 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], 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], 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(torch.tensor(x_point)).numpy()
- noisy_x_point = noisy_x_values[zoom_point]
- func_noisy_x_point = func(torch.tensor(noisy_x_point)).numpy()
+if __name__ == '__main__':
+ 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,
+ eiv=True,
+ number_of_draws=number_of_draws[0])
+ noneiv_plotting_dictionary = compute_predictions_and_uncertainties(
+ data=data,
+ x_range=x_range,
+ eiv=False,
+ number_of_draws=number_of_draws[1])
+ input_dim = eiv_plotting_dictionary['input_dim']
+ if input_dim == 1:
plt.figure(fignum)
plt.clf()
+ x_values, y_values = eiv_plotting_dictionary['range_points']
+ 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], 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], 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
+ 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(torch.tensor(x_point)).numpy()
+ noisy_x_point = noisy_x_values[zoom_point]
+ func_noisy_x_point = func(torch.tensor(noisy_x_point)).numpy()
+ 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
-plt.show()
+ plt.show()
--
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