From 1f7014d63c9933a0737d57b9d6649f042b6ad93a Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 9 Oct 2024 12:25:24 +0200
Subject: [PATCH] [fix] Add output-option to check_reg_quality
Also added related PCE-tests
---
src/bayesvalidrox/post_processing/post_processing.py | 12 ++++++++----
1 file changed, 8 insertions(+), 4 deletions(-)
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 10a27595a..2fc4c84de 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -846,13 +846,10 @@ class PostProcessing:
# -------------------------------------------------------------------------
def check_reg_quality(self, n_samples:int=1000, samples=None, outputs:dict=None)->None:
- """
- """
"""
Checks the quality of the metamodel for single output models based on:
https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685
-
Parameters
----------
n_samples : int, optional
@@ -863,6 +860,10 @@ class PostProcessing:
Output dictionary with model outputs for all given output types in
`Model.Output.names`. The default is None.
+ Return
+ ------
+ None
+
"""
if samples is None:
self.n_samples = n_samples
@@ -871,7 +872,10 @@ class PostProcessing:
self.n_samples = samples.shape[0]
# Evaluate the original and the surrogate model
- y_val = self._eval_model(samples, key_str='valid')
+ 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)
# Open a pdf for the plots
--
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