diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index e09904f48469474528a56a7096b103f2b84db2f5..62230a3784069329ccf8c2039476e6ba9c5e05d4 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -17,12 +17,11 @@ 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
@@ -30,14 +29,14 @@ class PostProcessing:
name : string
Name of the PostProcessing object to be used for saving the generated files.
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=''):
self.engine = engine
self.name = name
+
+
self.out_dir = f'./{out_dir}/Outputs_PostProcessing_{self.name}/'
@@ -59,7 +58,7 @@ 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`.
@@ -546,7 +545,6 @@ class PostProcessing:
------
AttributeError
MetaModel in given Engine needs to be of type 'pce' or 'apce'.
-
Returns
-------
sobol_cell: dict
@@ -556,21 +554,12 @@ class PostProcessing:
"""
# This function currently only supports PCE/aPCE
-<<<<<<< HEAD
metamod = self.engine.MetaModel
if not hasattr(metamod, 'meta_model_type'):
raise AttributeError('Sobol indices only support PCE-type models!')
if metamod.meta_model_type.lower() not in ['pce', 'apce']:
raise AttributeError('Sobol indices only support PCE-type models!')
-=======
- PCEModel = self.engine.MetaModel
- if not hasattr(PCEModel, 'meta_model_type'):
- raise AttributeError('Sobol indices only support PCE-type models!')
- if PCEModel.meta_model_type.lower() not in ['pce', 'apce']:
- raise AttributeError('Sobol indices only support PCE-type models!')
-
->>>>>>> 55b69984 ([tests] Add some PCE PostProcessing tests)
# Extract the necessary variables
basis_dict = metamod._basis_dict
coeffs_dict = metamod._coeffs_dict
@@ -849,11 +838,8 @@ class PostProcessing:
return self.total_sobol
# -------------------------------------------------------------------------
-<<<<<<< HEAD
- def check_reg_quality(self, n_samples: int = 1000, samples=None, outputs: dict = None) -> None:
-=======
- def check_reg_quality(self, n_samples=1000, samples=None, outputs=None):
->>>>>>> 4281a0b3 ([fix] Add output-option to check_reg_quality)
+ 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
@@ -881,11 +867,7 @@ class PostProcessing:
# Evaluate the original and the surrogate model
if outputs is None:
y_val = self._eval_model(samples, key_str='valid')
-<<<<<<< HEAD
else:
-=======
- else:
->>>>>>> 4281a0b3 ([fix] Add output-option to check_reg_quality)
y_val = outputs
y_pce_val, _ = self.engine.eval_metamodel(samples=samples)
@@ -977,7 +959,6 @@ class PostProcessing:
plt.close()
# -------------------------------------------------------------------------
-<<<<<<< HEAD
def plot_metamodel_3d(self, n_samples=10):
"""
Visualize the results of a PCE MetaModel as a 3D surface over two input
@@ -992,18 +973,6 @@ class PostProcessing:
------
AttributeError
This function is only applicable if the MetaModel input dimension is 2.
-=======
- def eval_pce_model_3d(self):
- # This function currently only supports PCE/aPCE
- PCEModel = self.engine.MetaModel
- if not hasattr(PCEModel, 'meta_model_type'):
- raise AttributeError('This evaluation only support PCE-type models!')
- if PCEModel.meta_model_type.lower() not in ['pce', 'apce']:
- raise AttributeError('This evaluation only support PCE-type models!')
-
- self.n_samples = 1000
- n_samples = self.n_samples
->>>>>>> 55b69984 ([tests] Add some PCE PostProcessing tests)
Returns
-------
@@ -1098,7 +1067,6 @@ class PostProcessing:
"""
# This function currently only supports PCE/aPCE
-<<<<<<< HEAD
metamod = self.engine.MetaModel
if not hasattr(metamod, 'meta_model_type'):
raise AttributeError(
@@ -1106,14 +1074,6 @@ class PostProcessing:
if metamod.meta_model_type.lower() not in ['pce', 'apce']:
raise AttributeError(
'This evaluation only support PCE-type models!')
-=======
- PCEModel = self.engine.MetaModel
- if not hasattr(PCEModel, 'meta_model_type'):
- raise AttributeError('This evaluation only support PCE-type models!')
- if PCEModel.meta_model_type.lower() not in ['pce', 'apce']:
- raise AttributeError('This evaluation only support PCE-type models!')
-
->>>>>>> 55b69984 ([tests] Add some PCE PostProcessing tests)
# get the samples
y_pce_val = self.pce_out_mean
@@ -1188,7 +1148,6 @@ class PostProcessing:
"""
# This function currently only supports PCE/aPCE
-<<<<<<< HEAD
metamod = self.engine.MetaModel
if not hasattr(metamod, 'meta_model_type'):
raise AttributeError(
@@ -1196,17 +1155,6 @@ class PostProcessing:
if metamod.meta_model_type.lower() not in ['pce', 'apce']:
raise AttributeError(
'This evaluation only support PCE-type models!')
-=======
- PCEModel = self.engine.MetaModel
- if not hasattr(PCEModel, 'meta_model_type'):
- raise AttributeError('This evaluation only support PCE-type models!')
- if PCEModel.meta_model_type.lower() not in ['pce', 'apce']:
- raise AttributeError('This evaluation only support PCE-type models!')
-
- newpath = f'Outputs_PostProcessing_{self.name}/'
- if not os.path.exists(newpath):
- os.makedirs(newpath)
->>>>>>> 55b69984 ([tests] Add some PCE PostProcessing tests)
# List of markers and colors
color = cycle((['b', 'g', 'r', 'y', 'k']))