From 92d98b86e844cf7dbbb14a4d5e81fa83520b17f9 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 23 Oct 2024 09:29:58 +0200
Subject: [PATCH] Update PostProcessing tests
---
.gitignore | 5 +-
.../post_processing/post_processing.py | 286 +++++++++---------
tests/test_PostProcessing.py | 9 +-
3 files changed, 158 insertions(+), 142 deletions(-)
diff --git a/.gitignore b/.gitignore
index 9cbc5104e..3b43b0c9d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,4 +32,7 @@ Outputs_*
*_env/*
# Ignore test results
-.coverage*
\ No newline at end of file
+.coverage*
+
+# Ignore files for vscode
+.vscode/*
\ No newline at end of file
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 652918026..9755d0dcc 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -1,27 +1,28 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-import numpy as np
import math
import os
from itertools import combinations, cycle
+import numpy as np
import pandas as pd
-import scipy.stats as stats
+from scipy import stats
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
+from matplotlib import ticker
from matplotlib.offsetbox import AnchoredText
from matplotlib.patches import Patch
# Load the mplstyle
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
@@ -31,22 +32,34 @@ class PostProcessing:
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 = ''):
+ def __init__(self, engine, name='calib', out_dir=''):
self.engine = engine
self.name = name
-
- self.out_dir = f'{out_dir}/Outputs_PostProcessing_{self.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)
+ # Initialize attributes
+ self.mc_reference = None
+ self.total_sobol = None
+ self.valid_error = None
+ self.rmse = None
+ self.model_out_dict = {}
+ self.means = None
+ self.stds = None
+
+ # TODO: Check if these could be removed!
+ self.pce_out_mean = None
+ 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`.
@@ -92,7 +105,7 @@ class PostProcessing:
std_data = self.stds[key]
# Extract a list of x values
- if type(x_values_orig) is dict:
+ if isinstance(x_values_orig, dict):
x = x_values_orig[key]
else:
x = x_values_orig
@@ -143,15 +156,15 @@ class PostProcessing:
# save the current figure
fig.savefig(
- f'./{self.out_dir}Mean_Std_PCE_{key}.pdf',
+ f'{self.out_dir}Mean_Std_PCE_{key}.pdf',
bbox_inches='tight'
- )
+ )
return self.means, self.stds
# -------------------------------------------------------------------------
def valid_metamodel(self, n_samples=1, samples=None, model_out_dict=None,
- x_axis='Time [s]')-> None:
+ x_axis='Time [s]') -> None:
"""
Evaluates and plots the meta model and the PCEModel outputs for the
given number of samples or the given samples.
@@ -170,10 +183,9 @@ class PostProcessing:
"""
if samples is None:
- self.n_samples = n_samples
- samples = self._get_sample()
+ samples = self._get_sample(n_samples)
else:
- self.n_samples = samples.shape[0]
+ n_samples = samples.shape[0]
# Extract x_values
x_values = self.engine.ExpDesign.x_values
@@ -183,7 +195,6 @@ class PostProcessing:
else:
self.model_out_dict = self._eval_model(samples, key_str='valid')
self.pce_out_mean, self.pce_out_std = self.engine.eval_metamodel(samples)
- self.pce_out_mean, self.pce_out_std = self.engine.eval_metamodel(samples)
try:
key = self.engine.out_names[1]
@@ -195,15 +206,16 @@ class PostProcessing:
n_obs = self.model_out_dict[key].shape[1]
if n_obs == 1:
- self._plot_validation()
+ self._plot_validation(samples)
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_')
+ 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:
+ def check_accuracy(self, n_samples=None, samples=None, outputs=None) -> None:
"""
Checks accuracy of the metamodel by computing the root mean square
error and validation error for all outputs.
@@ -220,29 +232,24 @@ class PostProcessing:
Raises
------
- Exception
+ AttributeError
When neither n_samples nor samples are provided.
"""
# Set the number of samples
- if n_samples:
- self.n_samples = n_samples
- elif samples is not None:
- self.n_samples = samples.shape[0]
- else:
- raise Exception("Please provide either samples or pass the number"
+ if samples is None and n_samples is None:
+ raise AttributeError("Please provide either samples or pass the number"
" of samples!")
+ n_samples = samples.shape[0] if samples is not None else n_samples
# Generate random samples if necessary
- samples = self._get_sample() if samples is None else samples
+ samples = self._get_sample(n_samples) if samples is None else samples
# Run the original model with the generated samples
- if outputs is None:
- outputs = self._eval_model(samples, key_str='validSet')
+ outputs = self._eval_model(samples, key_str='validSet') if outputs is None else outputs
# Run the PCE model with the generated samples
- pce_outputs, _ = self.engine.eval_metamodel(samples)
- pce_outputs, _ = self.engine.eval_metamodel(samples)
+ metamod_outputs, _ = self.engine.eval_metamodel(samples)
self.rmse = {}
self.valid_error = {}
@@ -250,7 +257,7 @@ class PostProcessing:
for key in self.engine.out_names:
for key in self.engine.out_names:
# Root mena square
- self.rmse[key] = mean_squared_error(outputs[key], pce_outputs[key],
+ self.rmse[key] = mean_squared_error(outputs[key], metamod_outputs[key],
squared=False,
multioutput='raw_values')
# Validation error
@@ -258,7 +265,7 @@ class PostProcessing:
np.var(outputs[key], ddof=1, axis=0)
# Print a report table
- print("\n>>>>> Errors of {} <<<<<".format(key))
+ print(f"\n>>>>> Errors of {key} <<<<<")
print("\nIndex | RMSE | Validation Error")
print('-'*35)
print('\n'.join(f'{i+1} | {k:.3e} | {j:.3e}' for i, (k, j)
@@ -270,9 +277,8 @@ class PostProcessing:
self.engine.MetaModel.rmse = self.rmse
self.engine.MetaModel.valid_error = self.valid_error
-
# -------------------------------------------------------------------------
- def plot_seq_design_diagnostics(self, ref_BME_KLD=None)->None:
+ def plot_seq_design_diagnostics(self, ref_BME_KLD=None) -> None:
"""
Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
(KLD) for the sequential design.
@@ -392,7 +398,8 @@ class PostProcessing:
ls='--', lw=2)
# Plot each UtilFuncs
- labels = np.arange(n_init_samples, n_total_samples+1, step1)
+ labels = np.arange(
+ n_init_samples, n_total_samples+1, step1)
draw_plot(all_errors[:, ::step2], labels, edge_color,
fill_color, idx)
@@ -411,7 +418,7 @@ class PostProcessing:
label=util))
plt.legend(handles=legend_elements[::-1], loc='best')
- if plot != 'BME' and plot != 'KLD':
+ if plot not in ['BME','KLD']:
plt.yscale('log')
plt.autoscale(True)
plt.xlabel('\\# of training samples')
@@ -423,7 +430,7 @@ class PostProcessing:
fig.savefig(
f'./{newpath}/seq_{plot_name}.pdf',
bbox_inches='tight'
- )
+ )
# Destroy the current plot
plt.close()
# Save arrays into files
@@ -441,7 +448,7 @@ class PostProcessing:
if n_total_samples not in x_idx:
x_idx = np.hstack((x_idx, n_total_samples))
- if plot == 'KLD' or plot == 'BME':
+ if plot in ['KLD', 'BME']:
# BME convergence if refBME is provided
if ref_BME_KLD is not None:
if plot == 'BME':
@@ -482,8 +489,8 @@ class PostProcessing:
seq_values = np.nan_to_num(seq_values)
# Plot the error evolution for each output
- #print(x_idx.shape)
- #print(seq_values.mean(axis=1).shape)
+ # print(x_idx.shape)
+ # print(seq_values.mean(axis=1).shape)
plt.semilogy(x_idx, seq_values.mean(axis=1),
marker=markers[idx], ls='--', lw=2,
color=colors[idx],
@@ -509,17 +516,16 @@ class PostProcessing:
fig.savefig(
f'./{newpath}/seq_{plot_name}.pdf',
bbox_inches='tight'
- )
+ )
# Destroy the current plot
plt.close()
# ---------------- Saving arrays into files ---------------
np.save(f'./{newpath}/seq_{plot_name}.npy', seq_values)
- return
# -------------------------------------------------------------------------
- def sobol_indices(self, xlabel:str='Time [s]', plot_type:str=None):
+ def sobol_indices(self, xlabel: str = 'Time [s]', plot_type: str = None):
"""
Provides Sobol indices as a sensitivity measure to infer the importance
of the input parameters. See Eq. 27 in [1] for more details. For the
@@ -547,7 +553,7 @@ class PostProcessing:
------
AttributeError
MetaModel in given Engine needs to be of type 'pce' or 'apce'.
-
+
Returns
-------
sobol_cell: dict
@@ -557,22 +563,22 @@ class PostProcessing:
"""
# This function currently only supports PCE/aPCE
- PCEModel = self.engine.MetaModel
- if not hasattr(PCEModel, 'meta_model_type'):
+ metamod = self.engine.MetaModel
+ if not hasattr(metamod, 'meta_model_type'):
raise AttributeError('Sobol indices only support PCE-type models!')
- if PCEModel.meta_model_type.lower() not in ['pce', 'apce']:
+ if metamod.meta_model_type.lower() not in ['pce', 'apce']:
raise AttributeError('Sobol indices only support PCE-type models!')
-
+
# Extract the necessary variables
- basis_dict = PCEModel._basis_dict
- coeffs_dict = PCEModel._coeffs_dict
- n_params = PCEModel.ndim
- max_order = np.max(PCEModel._pce_deg)
+ basis_dict = metamod._basis_dict
+ coeffs_dict = metamod._coeffs_dict
+ n_params = metamod.ndim
+ max_order = np.max(metamod._pce_deg)
sobol_cell_b = {}
total_sobol_b = {}
cov_Z_p_q = np.zeros((n_params))
- for b_i in range(PCEModel.n_bootstrap_itrs):
+ for b_i in range(metamod.n_bootstrap_itrs):
sobol_cell_, total_sobol_ = {}, {}
@@ -588,7 +594,8 @@ class PostProcessing:
for i_order in range(1, max_order+1):
n_comb = math.comb(n_params, i_order)
- sobol_cell_array[i_order] = np.zeros((n_comb, n_meas_points))
+ sobol_cell_array[i_order] = np.zeros(
+ (n_comb, n_meas_points))
total_sobol_array = np.zeros((n_params, n_meas_points))
@@ -601,7 +608,7 @@ class PostProcessing:
Basis = basis_dict[f'b_{b_i+1}'][output][f'y_{pIdx+1}']
try:
- clf_poly = PCEModel._clf_poly[f'b_{b_i+1}'][output][f'y_{pIdx+1}']
+ clf_poly = metamod._clf_poly[f'b_{b_i+1}'][output][f'y_{pIdx+1}']
PCECoeffs = clf_poly.coef_
except:
PCECoeffs = coeffs_dict[f'b_{b_i+1}'][output][f'y_{pIdx+1}']
@@ -621,9 +628,11 @@ class PostProcessing:
else:
nz_basis = Basis[nzidx]
for i_order in range(1, max_order+1):
- idx = np.where(np.sum(nz_basis > 0, axis=1) == i_order)
+ idx = np.where(
+ np.sum(nz_basis > 0, axis=1) == i_order)
subbasis = nz_basis[idx]
- Z = np.array(list(combinations(range(n_params), i_order)))
+ Z = np.array(
+ list(combinations(range(n_params), i_order)))
for q in range(Z.shape[0]):
Zq = Z[q]
@@ -633,7 +642,8 @@ class PostProcessing:
if TotalVariance[pIdx] == 0.0:
sobol_cell_array[i_order][q, pIdx] = 0.0
else:
- sobol = np.sum(np.square(PCECoeffs[sum_ind]))
+ sobol = np.sum(
+ np.square(PCECoeffs[sum_ind]))
sobol /= TotalVariance[pIdx]
sobol_cell_array[i_order][q, pIdx] = sobol
@@ -650,20 +660,22 @@ class PostProcessing:
total_sobol_array[ParIdx, pIdx] = sobol
# ----- if PCA selected: Compute covariance -----
- if PCEModel.dim_red_method.lower() == 'pca':
+ if metamod.dim_red_method.lower() == 'pca':
# Extract the basis indices (alpha) and coefficients for
# next component
if pIdx < n_meas_points-1:
nextBasis = basis_dict[f'b_{b_i+1}'][output][f'y_{pIdx+2}']
- if PCEModel.bootstrap_method != 'fast' or b_i == 0:
- clf_poly = PCEModel._clf_poly[f'b_{b_i+1}'][output][f'y_{pIdx+2}']
+ if metamod.bootstrap_method != 'fast' or b_i == 0:
+ clf_poly = metamod._clf_poly[f'b_{b_i+1}'][output][f'y_{pIdx+2}']
nextPCECoeffs = clf_poly.coef_
else:
nextPCECoeffs = coeffs_dict[f'b_{b_i+1}'][output][f'y_{pIdx+2}']
# Choose the common non-zero basis
- mask = (Basis[:, None] == nextBasis).all(-1).any(-1)
- n_mask = (nextBasis[:, None] == Basis).all(-1).any(-1)
+ mask = (Basis[:, None] ==
+ nextBasis).all(-1).any(-1)
+ n_mask = (nextBasis[:, None] ==
+ Basis).all(-1).any(-1)
# Compute the covariance in Eq 17.
for ParIdx in range(n_params):
@@ -672,14 +684,14 @@ class PostProcessing:
try:
cov_Z_p_q[ParIdx] += np.sum(np.dot(
PCECoeffs[idx], nextPCECoeffs[n_idx])
- )
+ )
except:
pass
# Compute the sobol indices according to Ref. 2
- if PCEModel.dim_red_method.lower() == 'pca':
+ if metamod.dim_red_method.lower() == 'pca':
n_c_points = self.engine.ExpDesign.Y[output].shape[1]
- PCA = PCEModel.pca[f'b_{b_i+1}'][output]
+ PCA = metamod.pca[f'b_{b_i+1}'][output]
compPCA = PCA.components_
nComp = compPCA.shape[0]
var_Z_p = PCA.explained_variance_
@@ -688,7 +700,8 @@ class PostProcessing:
for i_order in range(1, max_order+1):
n_comb = math.comb(n_params, i_order)
sobol_array[i_order] = np.zeros((n_comb, n_c_points))
- Z = np.array(list(combinations(range(n_params), i_order)))
+ Z = np.array(
+ list(combinations(range(n_params), i_order)))
# Loop over parameters
for q in range(Z.shape[0]):
@@ -714,7 +727,8 @@ class PostProcessing:
# term2 *= compPCA[i+1, tIdx]
# term2 *= 2
- sobol_array[i_order][q, tIdx] = term1 #+ term2
+ sobol_array[i_order][q,
+ tIdx] = term1 # + term2
# Devide over total output variance Eq. 18
sobol_array[i_order][q, tIdx] /= var_Y_t
@@ -725,7 +739,8 @@ class PostProcessing:
S_Z_i = total_sobol_array[ParIdx]
for tIdx in range(n_c_points):
- var_Y_t = np.var(self.engine.ExpDesign.Y[output][:, tIdx])
+ var_Y_t = np.var(
+ self.engine.ExpDesign.Y[output][:, tIdx])
if var_Y_t == 0.0:
term1, term2 = 0.0, 0.0
else:
@@ -741,7 +756,7 @@ class PostProcessing:
* compPCA[i+1, tIdx]
term2 *= 2
- total_sobol[ParIdx, tIdx] = term1 #+ term2
+ total_sobol[ParIdx, tIdx] = term1 # + term2
# Devide over total output variance Eq. 18
total_sobol[ParIdx, tIdx] /= var_Y_t
@@ -825,7 +840,7 @@ class PostProcessing:
fig.savefig(
f'./{self.out_dir}Sobol_indices_{output}.pdf',
bbox_inches='tight'
- )
+ )
# Destroy the current plot
plt.close()
@@ -833,7 +848,7 @@ class PostProcessing:
return self.total_sobol
# -------------------------------------------------------------------------
- def check_reg_quality(self, n_samples:int=1000, samples=None, outputs:dict=None)->None:
+ 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
@@ -854,15 +869,14 @@ class PostProcessing:
"""
if samples is None:
- self.n_samples = n_samples
- samples = self._get_sample()
+ samples = self._get_sample(n_samples)
else:
- self.n_samples = samples.shape[0]
+ 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:
+ else:
y_val = outputs
y_pce_val, _ = self.engine.eval_metamodel(samples=samples)
@@ -954,11 +968,11 @@ class PostProcessing:
plt.close()
# -------------------------------------------------------------------------
- def plot_metamodel_3d(self, n_samples = 10):
+ 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
@@ -974,65 +988,67 @@ class PostProcessing:
None.
"""
- if self.engine.ExpDesign.ndim !=2:
- raise AttributeError('This function is only applicable if the MetaModel input dimension is 2.')
+ 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)
-
+ 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])
+ 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,
+ 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')
+ bbox_inches='tight')
plt.close(fig)
-
# -------------------------------------------------------------------------
- def _get_sample(self, n_samples=None):
+
+ def _get_sample(self, n_samples):
"""
Generates random samples taken from the input parameter space.
+ Parameters
+ ----------
+ n_samples : int
+ Number of samples to generate.
+
Returns
-------
samples : array of shape (n_samples, n_params)
Generated samples.
"""
- if n_samples is None:
- n_samples = self.n_samples
- self.samples = self.engine.ExpDesign.generate_samples(
- self.samples = self.engine.ExpDesign.generate_samples(
+ samples = self.engine.ExpDesign.generate_samples(
n_samples,
sampling_method='random')
- return self.samples
+ return samples
# -------------------------------------------------------------------------
- def _eval_model(self, samples=None, key_str='Valid'):
+ def _eval_model(self, samples, key_str='Valid'):
"""
- Evaluates Forward Model for the given number of self.samples or given
- samples.
+ Evaluates Forward Model on the given samples.
Parameters
----------
- samples : array of shape (n_samples, n_params), optional
- Samples to evaluate the model at. The default is None.
+ samples : array of shape (n_samples, n_params)
+ Samples to evaluate the model at.
key_str : str, optional
Key string pass to the model. The default is 'Valid'.
@@ -1042,19 +1058,14 @@ class PostProcessing:
Dictionary of results.
"""
- if samples is None:
- samples = self._get_sample()
- self.samples = samples
- else:
- self.n_samples = len(samples)
-
- model_outs, _ = self.engine.Model.run_model_parallel(samples, key_str=key_str)
- model_outs, _ = self.engine.Model.run_model_parallel(samples, key_str=key_str)
+ samples = self._get_sample()
+ model_outs, _ = self.engine.Model.run_model_parallel(
+ samples, key_str=key_str)
return model_outs
# -------------------------------------------------------------------------
- def _plot_validation(self):
+ def _plot_validation(self, samples):
"""
Plots outputs for visual comparison of metamodel outputs with that of
the (full) original model.
@@ -1065,15 +1076,15 @@ class PostProcessing:
"""
# 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!')
-
+ metamod = self.engine.MetaModel
+ if not hasattr(metamod, 'meta_model_type'):
+ raise AttributeError(
+ 'This evaluation only support PCE-type models!')
+ if metamod.meta_model_type.lower() not in ['pce', 'apce']:
+ raise AttributeError(
+ 'This evaluation only support PCE-type models!')
# get the samples
- x_val = self.samples
y_pce_val = self.pce_out_mean
y_val = self.model_out_dict
@@ -1098,7 +1109,7 @@ class PostProcessing:
# the total number of explanatory variables in the model
# (not including the constant term)
length_list = []
- for key, value in PCEModel._coeffs_dict['b_1'][key].items():
+ for key, value in metamod._coeffs_dict['b_1'][key].items():
length_list.append(len(value))
n_predictors = min(length_list)
n_samples = x_val.shape[0]
@@ -1114,7 +1125,7 @@ class PostProcessing:
plt.ylabel("Original Model")
plt.xlabel("PCE Model")
plt.grid()
-
+
# save the current figure
plot_name = key.replace(' ', '_')
fig.savefig(f'./{self.out_dir}/Model_vs_PCEModel_{plot_name}.pdf',
@@ -1135,7 +1146,7 @@ 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!
@@ -1146,11 +1157,13 @@ class PostProcessing:
"""
# 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!')
+ metamod = self.engine.MetaModel
+ if not hasattr(metamod, 'meta_model_type'):
+ raise AttributeError(
+ 'This evaluation only support PCE-type models!')
+ if metamod.meta_model_type.lower() not in ['pce', 'apce']:
+ raise AttributeError(
+ 'This evaluation only support PCE-type models!')
# List of markers and colors
color = cycle((['b', 'g', 'r', 'y', 'k']))
@@ -1158,8 +1171,7 @@ class PostProcessing:
fig = plt.figure()
# Plot the model vs PCE model
- for keyIdx, key in enumerate(self.engine.out_names):
- for keyIdx, key in enumerate(self.engine.out_names):
+ for _, key in enumerate(self.engine.out_names):
y_pce_val = self.pce_out_mean[key]
y_pce_val_std = self.pce_out_std[key]
@@ -1170,18 +1182,18 @@ class PostProcessing:
x = x_values
for idx in range(y_val.shape[0]):
- Color = next(color)
- Marker = next(marker)
+ color_ = next(color)
+ marker_ = next(marker)
- plt.plot(x, y_val[idx], color=Color, marker=Marker,
- label='$Y_{%s}^M$'%(idx+1))
+ plt.plot(x, y_val[idx], color=color_, marker=marker_,
+ label='$Y_{%s}^M$' % (idx+1))
- plt.plot(x, y_pce_val[idx], color=Color, marker=Marker,
+ plt.plot(x, y_pce_val[idx], color=color_, marker=marker_,
linestyle='--',
- label='$Y_{%s}^{PCE}$'%(idx+1))
+ label='$Y_{%s}^{PCE}$' % (idx+1))
plt.fill_between(x, y_pce_val[idx]-1.96*y_pce_val_std[idx],
y_pce_val[idx]+1.96*y_pce_val_std[idx],
- color=Color, alpha=0.15)
+ color=color_, alpha=0.15)
# Calculate the RMSE
rmse = mean_squared_error(y_pce_val, y_val, squared=False)
diff --git a/tests/test_PostProcessing.py b/tests/test_PostProcessing.py
index ba905d647..5f63962be 100644
--- a/tests/test_PostProcessing.py
+++ b/tests/test_PostProcessing.py
@@ -158,16 +158,16 @@ def test_check_reg_quality_pce(pce_engine) -> None:
post = PostProcessing(engine)
post.check_reg_quality(samples=engine.ExpDesign.X, outputs=engine.ExpDesign.Y)
-#%% eval_pce_model_3d
+#%% eplot_metamodel_3d
-def test_eval_pce_model_3d_nopce(basic_engine) -> None:
+def test_plot_metamodel_3d_nopce(basic_engine) -> None:
"""
3d eval of non-PCE metamodel
"""
engine = basic_engine
post = PostProcessing(engine)
with pytest.raises(AttributeError) as excinfo:
- post.eval_pce_model_3d()
+ post.plot_metamodel_3d()
assert str(excinfo.value) == 'This evaluation only support PCE-type models!'
@@ -180,9 +180,10 @@ def test_plot_validation_nopce(basic_engine) -> None:
Plot validation of non-PCE metamodel
"""
engine = basic_engine
+ samples = engine.ExpDesign.generate_samples(10,'random')
post = PostProcessing(engine)
with pytest.raises(AttributeError) as excinfo:
- post._plot_validation()
+ post._plot_validation(samples)
assert str(excinfo.value) == 'This evaluation only support PCE-type models!'
#%% _plot_validation_multi
--
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