From 04dd0ca716b16cfa1af0acf6e65f902d5e87c364 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 23 Oct 2024 09:29:58 +0200
Subject: [PATCH] Update PostProcessing tests
---
.../post_processing/post_processing.py | 49 ++++++++++---------
tests/test_PostProcessing.py | 3 +-
2 files changed, 27 insertions(+), 25 deletions(-)
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 659e3cbaf..91692669b 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -17,11 +17,12 @@ from matplotlib.patches import Patch
plt.style.use(os.path.join(os.path.split(__file__)[0],
'../', 'bayesvalidrox.mplstyle'))
+
class PostProcessing:
"""
This class provides many helper functions to post-process the trained
meta-model.
-
+
Parameters
----------
engine : obj
@@ -31,15 +32,15 @@ class PostProcessing:
The default is 'calib'.
out_dir : string
Output directory in which the images are placed. The default is ''.
-
+
"""
- def __init__(self, engine, name='calib', out_dir = ''):
+ def __init__(self, engine, name='calib', out_dir=''):
self.engine = engine
self.name = name
-
- self.out_dir = f'{out_dir}/Outputs_PostProcessing_{self.name}/'
-
+
+ self.out_dir = f'./{out_dir}/Outputs_PostProcessing_{self.name}/'
+
# Open a pdf for the plots
if not os.path.exists(self.out_dir):
os.makedirs(self.out_dir)
@@ -58,7 +59,7 @@ class PostProcessing:
self.pce_out_std = None
# -------------------------------------------------------------------------
- def plot_moments(self, xlabel:str='Time [s]', plot_type:str=None):
+ def plot_moments(self, xlabel: str = 'Time [s]', plot_type: str = None):
"""
Plots the moments in a pdf format in the directory
`Outputs_PostProcessing`.
@@ -190,7 +191,8 @@ class PostProcessing:
self.model_out_dict = model_out_dict
else:
self.model_out_dict = self._eval_model(samples, key_str='valid')
- self.pce_out_mean, self.pce_out_std = self.engine.eval_metamodel(samples)
+ self.pce_out_mean, self.pce_out_std = self.engine.eval_metamodel(
+ samples)
try:
key = self.engine.out_names[1]
@@ -203,9 +205,10 @@ class PostProcessing:
self._plot_validation(samples)
else:
self._plot_validation_multi(x_values=x_values, x_axis=x_axis)
-
+
# Zip the subdirectories
- self.engine.Model.zip_subdirs(f'{self.engine.Model.name}valid', f'{self.engine.Model.name}valid_')
+ self.engine.Model.zip_subdirs(
+ f'{self.engine.Model.name}valid', f'{self.engine.Model.name}valid_')
# Zip the subdirectories
self.engine.Model.zip_subdirs(
@@ -271,9 +274,8 @@ class PostProcessing:
self.engine.MetaModel.rmse = self.rmse
self.engine.MetaModel.valid_error = self.valid_error
-
# -------------------------------------------------------------------------
- def plot_seq_design_diagnostics(self, ref_BME_KLD=None)->None:
+ def plot_seq_design_diagnostics(self, ref_BME_KLD=None) -> None:
"""
Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
(KLD) for the sequential design.
@@ -520,7 +522,7 @@ class PostProcessing:
# -------------------------------------------------------------------------
- def sobol_indices(self, xlabel:str='Time [s]', plot_type:str=None):
+ def sobol_indices(self, xlabel: str = 'Time [s]', plot_type: str = None):
"""
Provides Sobol indices as a sensitivity measure to infer the importance
of the input parameters. See Eq. 27 in [1] for more details. For the
@@ -548,7 +550,7 @@ class PostProcessing:
------
AttributeError
MetaModel in given Engine needs to be of type 'pce' or 'apce'.
-
+
Returns
-------
sobol_cell: dict
@@ -840,7 +842,7 @@ class PostProcessing:
return self.total_sobol
# -------------------------------------------------------------------------
- def check_reg_quality(self, n_samples:int=1000, samples=None, outputs:dict=None)->None:
+ 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
@@ -960,11 +962,11 @@ class PostProcessing:
plt.close()
# -------------------------------------------------------------------------
- def plot_metamodel_3d(self, n_samples = 10):
+ def plot_metamodel_3d(self, n_samples=10):
"""
Visualize the results of a PCE MetaModel as a 3D surface over two input
parameters.
-
+
Parameters
----------
n_samples : int
@@ -991,25 +993,24 @@ class PostProcessing:
title = 'MetaModel'
else:
title = 'Model'
- X,Y = np.meshgrid(samples[:,0],samples[:,1])
+ X, Y = np.meshgrid(samples[:, 0], samples[:, 1])
for name in self.engine.out_names:
for t in range(mean[name].shape[1]):
fig = plt.figure()
ax = plt.axes(projection='3d')
- ax.plot_surface(X, Y, np.atleast_2d(mean[name][:,t]), rstride=1, cstride=1,
+ ax.plot_surface(X, Y, np.atleast_2d(mean[name][:, t]), rstride=1, cstride=1,
cmap='viridis', edgecolor='none')
ax.set_title(title)
ax.set_xlabel('$x_1$')
ax.set_ylabel('$x_2$')
ax.set_zlabel('$f(x_1,x_2)$')
-
+
plt.grid()
-
+
# save the figure to file
fig.savefig(f'./{self.out_dir}/3DPlot_{title}_{name}{t}.pdf',
- bbox_inches='tight')
+ bbox_inches='tight')
plt.close(fig)
-
# -------------------------------------------------------------------------
@@ -1139,7 +1140,7 @@ class PostProcessing:
List of x values. The default is [].
x_axis : str, optional
Label of the x axis. The default is "x [m]".
-
+
Raises
------
AttributeError: This evaluation only support PCE-type models!
diff --git a/tests/test_PostProcessing.py b/tests/test_PostProcessing.py
index 64cc61533..a57e8c085 100644
--- a/tests/test_PostProcessing.py
+++ b/tests/test_PostProcessing.py
@@ -158,7 +158,8 @@ def test_check_reg_quality_pce(pce_engine) -> None:
post = PostProcessing(engine)
post.check_reg_quality(samples=engine.ExpDesign.X, outputs=engine.ExpDesign.Y)
-#%% plot_metamodel_3d
+#%% eplot_metamodel_3d
+
def test_plot_metamodel_3d_nopce(basic_engine) -> None:
"""
3d eval of non-PCE metamodel
--
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