From 73cc5aea61050b81fa293594b80c104edc1c08a8 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Thu, 10 Oct 2024 17:25:44 +0200
Subject: [PATCH] [fix] Sorted out_dir, changed eval_pce_model_3d to
plot_metamodel_3d
---
.../post_processing/post_processing.py | 193 +++++++-----------
1 file changed, 70 insertions(+), 123 deletions(-)
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index a111cc150..c7b8bf98e 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -29,14 +29,21 @@ 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'):
+ def __init__(self, engine, name='calib', out_dir = ''):
self.engine = engine
self.name = 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)
+
# -------------------------------------------------------------------------
def plot_moments(self, xlabel:str='Time [s]', plot_type:str=None):
@@ -72,11 +79,6 @@ 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:
@@ -138,7 +140,7 @@ class PostProcessing:
# save the current figure
fig.savefig(
- f'./{newpath}Mean_Std_PCE_{key}.pdf',
+ f'./{self.out_dir}Mean_Std_PCE_{key}.pdf',
bbox_inches='tight'
)
@@ -190,6 +192,9 @@ class PostProcessing:
self._plot_validation()
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_')
# -------------------------------------------------------------------------
def check_accuracy(self, n_samples=None, samples=None, outputs=None)-> None:
@@ -272,6 +277,7 @@ class PostProcessing:
n_init_samples = engine.ExpDesign.n_init_samples
n_total_samples = engine.ExpDesign.X.shape[0]
+ # TODO: move this one!
newpath = f'Outputs_PostProcessing_{self.name}/seq_design_diagnostics/'
if not os.path.exists(newpath):
os.makedirs(newpath)
@@ -755,10 +761,6 @@ class PostProcessing:
par_names = self.engine.ExpDesign.par_names
x_values_orig = self.engine.ExpDesign.x_values
- newpath = (f'Outputs_PostProcessing_{self.name}/')
- if not os.path.exists(newpath):
- os.makedirs(newpath)
-
fig = plt.figure()
for outIdx, output in enumerate(self.engine.out_names):
@@ -801,14 +803,14 @@ class PostProcessing:
plt.legend(loc='best', frameon=True)
# Save indices
- np.savetxt(f'./{newpath}totalsobol_' +
+ np.savetxt(f'./{self.out_dir}totalsobol_' +
output.replace('/', '_') + '.csv',
total_sobol.T, delimiter=',',
header=','.join(par_names), comments='')
# save the current figure
fig.savefig(
- f'./{newpath}Sobol_indices_{output}.pdf',
+ f'./{self.out_dir}Sobol_indices_{output}.pdf',
bbox_inches='tight'
)
@@ -850,11 +852,6 @@ class PostProcessing:
y_val = outputs
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():
@@ -877,7 +874,7 @@ class PostProcessing:
plt.ylabel('Residuals')
# save the current figure
- fig1.savefig(f'./{newpath}/Residuals_vs_Par_{i+1}.pdf',
+ fig1.savefig(f'./{self.out_dir}/Residuals_vs_Par_{i+1}.pdf',
bbox_inches='tight')
# Destroy the current plot
plt.close()
@@ -895,7 +892,7 @@ class PostProcessing:
plt.ylabel('Residuals')
# save the current figure
- fig2.savefig(f'./{newpath}/Fitted_vs_Residuals.pdf',
+ fig2.savefig(f'./{self.out_dir}/Fitted_vs_Residuals.pdf',
bbox_inches='tight')
# Destroy the current plot
plt.close()
@@ -921,7 +918,7 @@ class PostProcessing:
ax.add_artist(at)
# save the current figure
- fig3.savefig(f'./{newpath}/Hist_NormResiduals.pdf',
+ fig3.savefig(f'./{self.out_dir}/Hist_NormResiduals.pdf',
bbox_inches='tight')
# Destroy the current plot
plt.close()
@@ -937,103 +934,61 @@ class PostProcessing:
plt.grid(True)
# save the current figure
- plt.savefig(f'./{newpath}/QQPlot_NormResiduals.pdf',
+ plt.savefig(f'./{self.out_dir}/QQPlot_NormResiduals.pdf',
bbox_inches='tight')
# Destroy the current plot
plt.close()
# -------------------------------------------------------------------------
- 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
-
- # 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('$f(x_1,x_2)$')
+ 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.
- plt.grid()
+ Raises
+ ------
+ AttributeError
+ This function is only applicable if the MetaModel input dimension is 2.
- # Saving the figure
- newpath = f'Outputs_PostProcessing_{self.name}/'
- if not os.path.exists(newpath):
- os.makedirs(newpath)
+ Returns
+ -------
+ None.
- # 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()
+ """
+ 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)
- # 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):
@@ -1106,11 +1061,6 @@ class PostProcessing:
y_pce_val = self.pce_out_mean
y_val = self.model_out_dict
- # Open a pdf for the plots
- newpath = f'Outputs_PostProcessing_{self.name}/'
- if not os.path.exists(newpath):
- os.makedirs(newpath)
-
fig = plt.figure()
# Fit the data(train the model)
for key in y_pce_val.keys():
@@ -1151,7 +1101,7 @@ class PostProcessing:
# save the current figure
plot_name = key.replace(' ', '_')
- fig.savefig(f'./{newpath}/Model_vs_PCEModel_{plot_name}.pdf',
+ fig.savefig(f'./{self.out_dir}/Model_vs_PCEModel_{plot_name}.pdf',
bbox_inches='tight')
# Destroy the current plot
@@ -1169,6 +1119,10 @@ 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!
Returns
-------
@@ -1182,10 +1136,6 @@ class PostProcessing:
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']))
marker = cycle(('x', 'd', '+', 'o', '*'))
@@ -1231,11 +1181,8 @@ class PostProcessing:
# save the current figure
plot_name = key.replace(' ', '_')
- fig.savefig(f'./{newpath}/Model_vs_PCEModel_{plot_name}.pdf',
+ fig.savefig(f'./{self.out_dir}/Model_vs_PCEModel_{plot_name}.pdf',
bbox_inches='tight')
# 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_')
--
GitLab