diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 0c94946c7a3b4c257f43de0d51912a59181ed988..2d3ba019661195b7b9d93212e1a531e532344dbc 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -69,6 +69,7 @@ class PostProcessing:
bar_plot = True if plot_type == 'bar' else False
meta_model_type = self.engine.MetaModel.meta_model_type
+ meta_model_type = self.engine.MetaModel.meta_model_type
# Read Monte-Carlo reference
self.mc_reference = self.engine.Model.read_observation('mc_ref')
@@ -80,6 +81,11 @@ class PostProcessing:
# Compute the moments with the PCEModel object
self.means, self.stds = self.engine.MetaModel.compute_moments()
+ # Open a pdf for the plots
+ newpath = (f'Outputs_PostProcessing_{self.name}/')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
# Plot the best fit line, set the linewidth (lw), color and
# transparency (alpha) of the line
for key in self.engine.out_names:
@@ -222,6 +228,13 @@ class PostProcessing:
Exception
When neither n_samples nor samples are provided.
+ Returns
+ -------
+ rmse: dict
+ Root mean squared error for each output.
+ valid_error : dict
+ Validation error for each output.
+
"""
# Set the number of samples
if n_samples:
@@ -558,9 +571,9 @@ class PostProcessing:
# This function currently only supports PCE/aPCE
PCEModel = self.engine.MetaModel
if not hasattr(PCEModel, 'meta_model_type'):
- raise AttributeError('Sobol indices only support PCE-type models!')
+ raise AttributeError('Sobol indices currently only support PCE-type models!')
if PCEModel.meta_model_type.lower() not in ['pce', 'apce']:
- raise AttributeError('Sobol indices only support PCE-type models!')
+ raise AttributeError('Sobol indices currently only support PCE-type models!')
# Extract the necessary variables
basis_dict = PCEModel._basis_dict
@@ -764,6 +777,7 @@ class PostProcessing:
total_sobol_all[k][i] = v
self.total_sobol = {}
+ for output in self.engine.out_names:
for output in self.engine.out_names:
self.total_sobol[output] = np.mean(total_sobol_all[output], axis=0)
@@ -773,6 +787,7 @@ class PostProcessing:
fig = plt.figure()
+ for outIdx, output in enumerate(self.engine.out_names):
for outIdx, output in enumerate(self.engine.out_names):
# Extract total Sobol indices
@@ -857,12 +872,14 @@ class PostProcessing:
self.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_val = self._eval_model(samples, key_str='valid')
y_pce_val, _ = self.engine.eval_metamodel(samples=samples)
+ # Open a pdf for the plots
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
# Fit the data(train the model)
for key in y_pce_val.keys():
@@ -951,55 +968,93 @@ class PostProcessing:
plt.close()
# -------------------------------------------------------------------------
- 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
- Number of samples that are used to generate the 3D plot.
+ def eval_pce_model_3d(self):
- Raises
- ------
- AttributeError
- This function is only applicable if the MetaModel input dimension is 2.
+ self.n_samples = 1000
- Returns
- -------
- None.
+ PCEModel = self.engine.MetaModel
+ n_samples = self.n_samples
- """
- if self.engine.ExpDesign.ndim !=2:
- raise AttributeError('This function is only applicable if the MetaModel input dimension is 2.')
- samples = self.engine.ExpDesign.generate_samples(n_samples)
- samples = np.sort(np.sort(samples, axis = 1), axis = 0)
- mean, stdev = self.engine.eval_metamodel(samples = samples)
-
- if self.engine.emulator:
- title = 'MetaModel'
- else:
- title = 'Model'
- 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,
- 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')
- plt.close(fig)
+ # Create 3D-Grid
+ # TODO: Make it general
+ x = np.linspace(-5, 10, n_samples)
+ y = np.linspace(0, 15, n_samples)
+
+ X, Y = np.meshgrid(x, y)
+ PCE_Z = np.zeros((self.n_samples, self.n_samples))
+ Model_Z = np.zeros((self.n_samples, self.n_samples))
+
+ for idxMesh in range(self.n_samples):
+ sample_mesh = np.vstack((X[:, idxMesh], Y[:, idxMesh])).T
+
+ univ_p_val = PCEModel.univ_basis_vals(sample_mesh)
+
+ for Outkey, ValuesDict in PCEModel.coeffs_dict.items():
+
+ pce_out_mean = np.zeros((len(sample_mesh), len(ValuesDict)))
+ pce_out_std = np.zeros((len(sample_mesh), len(ValuesDict)))
+ model_outs = np.zeros((len(sample_mesh), len(ValuesDict)))
+
+ for Inkey, InIdxValues in ValuesDict.items():
+ idx = int(Inkey.split('_')[1]) - 1
+ basis_deg_ind = PCEModel.basis_dict[Outkey][Inkey]
+ clf_poly = PCEModel.clf_poly[Outkey][Inkey]
+
+ PSI_Val = PCEModel.create_psi(basis_deg_ind, univ_p_val)
+
+ # Perdiction with error bar
+ y_mean, y_std = clf_poly.predict(PSI_Val, return_std=True)
+
+ pce_out_mean[:, idx] = y_mean
+ pce_out_std[:, idx] = y_std
+
+ # Model evaluation
+ model_out_dict, _ = self.engine.Model.run_model_parallel(sample_mesh,
+ key_str='Valid3D')
+ model_outs[:, idx] = model_out_dict[Outkey].T
+
+ PCE_Z[:, idxMesh] = y_mean
+ Model_Z[:, idxMesh] = model_outs[:, 0]
+
+ # ---------------- 3D plot for PCEModel -----------------------
+ fig_PCE = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('PCEModel')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+
+ plt.grid()
+
+ # Saving the figure
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ # save the figure to file
+ fig_PCE.savefig(f'./{newpath}/3DPlot_PCEModel.pdf',
+ bbox_inches='tight')
+ plt.close(fig_PCE)
+
+ # ---------------- 3D plot for Model -----------------------
+ fig_Model = plt.figure()
+ ax = plt.axes(projection='3d')
+ ax.plot_surface(X, Y, PCE_Z, rstride=1, cstride=1,
+ cmap='viridis', edgecolor='none')
+ ax.set_title('Model')
+ ax.set_xlabel('$x_1$')
+ ax.set_ylabel('$x_2$')
+ ax.set_zlabel('$f(x_1,x_2)$')
+ plt.grid()
+ # Save the figure
+ fig_Model.savefig(f'./{newpath}/3DPlot_Model.pdf',
+ bbox_inches='tight')
+ plt.close(fig_Model)
+
+ return
# -------------------------------------------------------------------------
def _get_sample(self, n_samples=None):
@@ -1142,12 +1197,9 @@ class PostProcessing:
None.
"""
- # 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!')
+ newpath = f'Outputs_PostProcessing_{self.name}/'
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
# List of markers and colors
color = cycle((['b', 'g', 'r', 'y', 'k']))
@@ -1200,3 +1252,6 @@ class PostProcessing:
# Destroy the current plot
plt.close()
+
+ # Zip the subdirectories
+ self.engine.Model.zip_subdirs(f'{self.engine.Model.name}valid', f'{self.engine.Model.name}valid_')