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Commit 73cc5aea authored by kohlhaasrebecca's avatar kohlhaasrebecca
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[fix] Sorted out_dir, changed eval_pce_model_3d to plot_metamodel_3d

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src/bayesvalidrox/post_processing/post_processing.py
+ 70
− 123
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......@@ -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_')
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