From 3bc4bba9e68602f95dee382bdfd6c6f622ac92ea Mon Sep 17 00:00:00 2001
From: Farid Mohammadi <farid.mohammadi@iws.uni-stuttgart.de>
Date: Fri, 17 Jun 2022 18:19:57 +0200
Subject: [PATCH] [Postprocessing] update residual vs variables plot for
linearity check.
---
.../post_processing/post_processing.py | 38 ++++++++++---------
1 file changed, 20 insertions(+), 18 deletions(-)
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 2d3b16c46..4ee885b3b 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -871,32 +871,34 @@ class PostProcessing:
y_pce_val_ = y_pce_val[key]
y_val_ = y_val[key]
+ residuals = y_val_ - y_pce_val_
# ------ Residuals vs. predicting variables ------
# Check the assumptions of linearity and independence
fig1 = plt.figure()
- plt.title(key+": Residuals vs. Predicting variables")
- residuals = y_val_ - y_pce_val_
- plt.scatter(x=y_val_, y=residuals, color='blue', edgecolor='k')
- plt.grid(True)
- xmin, xmax = min(y_val_), max(y_val_)
- plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', lw=3,
- linestyle='--')
- plt.xlabel(key)
- plt.ylabel('Residuals')
- plt.show()
+ for i, par in enumerate(MetaModel.ExpDesign.par_names):
+ plt.title(f"{key}: Residuals vs. {par}")
+ plt.scatter(
+ x=samples[:, i], y=residuals, color='blue', edgecolor='k')
+ plt.grid(True)
+ xmin, xmax = min(samples[:, i]), max(samples[:, i])
+ plt.hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red',
+ lw=3, linestyle='--')
+ plt.xlabel(par)
+ plt.ylabel('Residuals')
+ plt.show()
- if save_fig:
- # save the current figure
- fig1.savefig(f'./{newpath}/Residuals_vs_PredVariables.pdf',
- bbox_inches='tight')
- # Destroy the current plot
- plt.clf()
+ if save_fig:
+ # save the current figure
+ fig1.savefig(f'./{newpath}/Residuals_vs_Par_{i+1}.pdf',
+ bbox_inches='tight')
+ # Destroy the current plot
+ plt.clf()
# ------ Fitted vs. residuals ------
# Check the assumptions of linearity and independence
fig2 = plt.figure()
- plt.title(key+": Residuals vs. predicting variables")
+ plt.title(f"{key}: Residuals vs. fitted values")
plt.scatter(x=y_pce_val_, y=residuals, color='blue', edgecolor='k')
plt.grid(True)
xmin, xmax = min(y_val_), max(y_val_)
@@ -949,7 +951,7 @@ class PostProcessing:
plt.yticks()
plt.xlabel("Theoretical quantiles")
plt.ylabel("Sample quantiles")
- plt.title(key+": Q-Q plot of normalized residuals")
+ plt.title(f"{key}: Q-Q plot of normalized residuals")
plt.grid(True)
plt.show()
--
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