diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 39ec81b75384ecc1a7038cef4c4679dc83172729..c32abd6267fd1038d64359ccf0bf1d439c11f39d 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -35,9 +35,6 @@ class PostProcessing:
def __init__(self, engine, name='calib'):
self.engine = engine
- self.MetaModel = engine.MetaModel
- self.ExpDesign = engine.ExpDesign
- self.ModelObj = engine.Model
self.name = name
# -------------------------------------------------------------------------
@@ -63,20 +60,16 @@ class PostProcessing:
"""
bar_plot = True if plot_type == 'bar' else False
- meta_model_type = self.MetaModel.meta_model_type
- Model = self.ModelObj
+ meta_model_type = self.engine.MetaModel.meta_model_type
# Read Monte-Carlo reference
- self.mc_reference = Model.read_observation('mc_ref')
+ self.mc_reference = self.engine.Model.read_observation('mc_ref')
# Set the x values
x_values_orig = self.engine.ExpDesign.x_values
# Compute the moments with the PCEModel object
- self.means, self.stds = self.MetaModel.calculate_moments()
-
- # Get the variables
- out_names = Model.Output.names
+ self.means, self.stds = self.engine.MetaModel.compute_moments()
# Open a pdf for the plots
newpath = (f'Outputs_PostProcessing_{self.name}/')
@@ -85,7 +78,7 @@ class PostProcessing:
# Plot the best fit line, set the linewidth (lw), color and
# transparency (alpha) of the line
- for key in out_names:
+ for key in self.engine.out_names:
fig, ax = plt.subplots(nrows=1, ncols=2)
# Extract mean and std
@@ -173,9 +166,6 @@ class PostProcessing:
None.
"""
- MetaModel = self.MetaModel
- Model = self.ModelObj
-
if samples is None:
self.n_samples = n_samples
samples = self._get_sample()
@@ -189,12 +179,12 @@ 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 = MetaModel.eval_metamodel(samples)
+ self.pce_out_mean, self.pce_out_std = self.engine.eval_metamodel(samples)
try:
- key = Model.Output.names[1]
+ key = self.engine.out_names[1]
except IndexError:
- key = Model.Output.names[0]
+ key = self.engine.out_names[0]
n_obs = self.model_out_dict[key].shape[1]
@@ -232,9 +222,6 @@ class PostProcessing:
Validation error for each output.
"""
- MetaModel = self.MetaModel
- Model = self.ModelObj
-
# Set the number of samples
if n_samples:
self.n_samples = n_samples
@@ -252,12 +239,12 @@ class PostProcessing:
outputs = self._eval_model(samples, key_str='validSet')
# Run the PCE model with the generated samples
- pce_outputs, _ = MetaModel.eval_metamodel(samples)
+ pce_outputs, _ = self.engine.eval_metamodel(samples)
self.rmse = {}
self.valid_error = {}
# Loop over the keys and compute RMSE error.
- for key in Model.Output.names:
+ for key in self.engine.out_names:
# Root mena square
self.rmse[key] = mean_squared_error(outputs[key], pce_outputs[key],
squared=False,
@@ -274,8 +261,8 @@ class PostProcessing:
in enumerate(zip(self.rmse[key],
self.valid_error[key]))))
# Save error dicts in PCEModel object
- self.MetaModel.rmse = self.rmse
- self.MetaModel.valid_error = self.valid_error
+ self.engine.MetaModel.rmse = self.rmse
+ self.engine.MetaModel.valid_error = self.valid_error
return
@@ -296,7 +283,6 @@ class PostProcessing:
"""
engine = self.engine
- PCEModel = self.MetaModel
n_init_samples = engine.ExpDesign.n_init_samples
n_total_samples = engine.ExpDesign.X.shape[0]
@@ -564,13 +550,13 @@ class PostProcessing:
"""
# This function currently only supports PCE/aPCE
- if not hasattr(self.MetaModel, 'meta_model_type'):
+ PCEModel = self.engine.MetaModel
+ if not hasattr(PCEModel, 'meta_model_type'):
raise AttributeError('Sobol indices currently only support PCE-type models!')
- if self.MetaModel.meta_model_type.lower() not in ['pce', 'apce']:
+ if PCEModel.meta_model_type.lower() not in ['pce', 'apce']:
raise AttributeError('Sobol indices currently only support PCE-type models!')
# Extract the necessary variables
- PCEModel = self.MetaModel
basis_dict = PCEModel._basis_dict
coeffs_dict = PCEModel._coeffs_dict
n_params = PCEModel.ndim
@@ -583,7 +569,7 @@ class PostProcessing:
sobol_cell_, total_sobol_ = {}, {}
- for output in self.ModelObj.Output.names:
+ for output in self.engine.out_names:
n_meas_points = len(coeffs_dict[f'b_{b_i+1}'][output])
@@ -771,7 +757,7 @@ class PostProcessing:
total_sobol_all[k][i] = v
self.total_sobol = {}
- for output in self.ModelObj.Output.names:
+ for output in self.engine.out_names:
self.total_sobol[output] = np.mean(total_sobol_all[output], axis=0)
# ---------------- Plot -----------------------
@@ -784,7 +770,7 @@ class PostProcessing:
fig = plt.figure()
- for outIdx, output in enumerate(self.ModelObj.Output.names):
+ for outIdx, output in enumerate(self.engine.out_names):
# Extract total Sobol indices
total_sobol = self.total_sobol[output]
@@ -859,8 +845,6 @@ class PostProcessing:
None.
"""
- MetaModel = self.MetaModel
-
if samples is None:
self.n_samples = n_samples
samples = self._get_sample()
@@ -869,7 +853,7 @@ class PostProcessing:
# Evaluate the original and the surrogate model
y_val = self._eval_model(samples, key_str='valid')
- y_pce_val, _ = MetaModel.eval_metamodel(samples=samples)
+ y_pce_val, _ = self.engine.eval_metamodel(samples=samples)
# Open a pdf for the plots
newpath = f'Outputs_PostProcessing_{self.name}/'
@@ -968,8 +952,7 @@ class PostProcessing:
self.n_samples = 1000
- PCEModel = self.MetaModel
- Model = self.ModelObj
+ PCEModel = self.engine.MetaModel
n_samples = self.n_samples
# Create 3D-Grid
@@ -1006,7 +989,7 @@ class PostProcessing:
pce_out_std[:, idx] = y_std
# Model evaluation
- model_out_dict, _ = Model.run_model_parallel(sample_mesh,
+ model_out_dict, _ = self.engine.Model.run_model_parallel(sample_mesh,
key_str='Valid3D')
model_outs[:, idx] = model_out_dict[Outkey].T
@@ -1066,7 +1049,7 @@ class PostProcessing:
"""
if n_samples is None:
n_samples = self.n_samples
- self.samples = self.ExpDesign.generate_samples(
+ self.samples = self.engine.ExpDesign.generate_samples(
n_samples,
sampling_method='random')
return self.samples
@@ -1090,15 +1073,13 @@ class PostProcessing:
Dictionary of results.
"""
- Model = self.ModelObj
-
if samples is None:
samples = self._get_sample()
self.samples = samples
else:
self.n_samples = len(samples)
- model_outs, _ = Model.run_model_parallel(samples, key_str=key_str)
+ model_outs, _ = self.engine.Model.run_model_parallel(samples, key_str=key_str)
return model_outs
@@ -1189,8 +1170,6 @@ class PostProcessing:
None.
"""
- Model = self.ModelObj
-
newpath = f'Outputs_PostProcessing_{self.name}/'
if not os.path.exists(newpath):
os.makedirs(newpath)
@@ -1201,7 +1180,7 @@ class PostProcessing:
fig = plt.figure()
# Plot the model vs PCE model
- for keyIdx, key in enumerate(Model.Output.names):
+ for keyIdx, key in enumerate(self.engine.out_names):
y_pce_val = self.pce_out_mean[key]
y_pce_val_std = self.pce_out_std[key]
@@ -1247,4 +1226,4 @@ class PostProcessing:
plt.close()
# Zip the subdirectories
- Model.zip_subdirs(f'{Model.name}valid', f'{Model.name}valid_')
+ self.engine.Model.zip_subdirs(f'{self.engine.Model.name}valid', f'{self.engine.Model.name}valid_')