From 708d354f183a464a621ff066cdeaca7ee2cff57a Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 11 Dec 2024 21:24:04 +0100
Subject: [PATCH] Fix test
---
.../post_processing/post_processing.py | 19 ++++++++-----------
1 file changed, 8 insertions(+), 11 deletions(-)
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 3b33fd1e6..1ab736ec9 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -856,18 +856,14 @@ class PostProcessing:
n_samples = samples.shape[0]
# Evaluate the original and the surrogate model
- if outputs is None:
- y_val = self._eval_model(samples, key_str='valid')
- else:
- y_val = outputs
- y_pce_val, _ = self.engine.eval_metamodel(samples=samples)
+ y_val = outputs
+ if y_val is None:
+ y_val, _ = self.engine.Model.run_model_parallel(samples, key_str="valid")
+ y_metamod_val, _ = self.engine.eval_metamodel(samples=samples)
# Fit the data(train the model)
- for key in y_pce_val.keys():
-
- y_pce_val_ = y_pce_val[key]
- y_val_ = y_val[key]
- residuals = y_val_ - y_pce_val_
+ for key in y_metamod_val.keys():
+ residuals = y_val[key] - y_metamod_val[key]
# ------ Residuals vs. predicting variables ------
# Check the assumptions of linearity and independence
@@ -891,7 +887,8 @@ class PostProcessing:
# ------ Fitted vs. residuals ------
# Check the assumptions of linearity and independence
- fig2 = plt.figure()
+ for i in range(y_metamod_val[key].shape[0]):
+ plt.scatter(x=y_metamod_val[key][i,:], y=residuals[i,:], color="blue", edgecolor="k")
plt.title(f"{key}: Residuals vs. fitted values")
plt.scatter(x=y_pce_val_, y=residuals, color='blue', edgecolor='k')
plt.grid(True)
--
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