From 0e99146f1904162be20827d9a1d96803557a025f Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Thu, 10 Oct 2024 16:26:29 +0200
Subject: [PATCH] [docs] Update docstrings for PostProcessing
---
.../post_processing/post_processing.py | 57 ++++++++-----------
tests/test_ExpDesign.py | 1 -
2 files changed, 23 insertions(+), 35 deletions(-)
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index d5ad267b5..a111cc150 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -17,28 +17,29 @@ from matplotlib.patches import Patch
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.
-
- Attributes
+
+ Parameters
----------
- MetaModel : obj
- MetaModel object to do postprocessing on.
- name : str
- Type of the anaylsis. The default is `'calib'`. If a validation is
- expected to be performed change this to `'valid'`.
+ engine : obj
+ Trained Engine object, does not need to contain a trained MetaModel object.
+ name : string
+ Name of the PostProcessing object to be used for saving the generated files.
+ The default is 'calib'.
+
"""
def __init__(self, engine, name='calib'):
self.engine = engine
self.name = name
+
+
# -------------------------------------------------------------------------
- def plot_moments(self, xlabel='Time [s]', plot_type=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`.
@@ -145,7 +146,7 @@ class PostProcessing:
# -------------------------------------------------------------------------
def valid_metamodel(self, n_samples=1, samples=None, model_out_dict=None,
- x_axis='Time [s]'):
+ 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.
@@ -161,9 +162,6 @@ class PostProcessing:
x_axis : str, optional
Label of x axis. The default is `'Time [s]'`.
- Returns
- -------
- None.
"""
if samples is None:
@@ -194,7 +192,7 @@ class PostProcessing:
self._plot_validation_multi(x_values=x_values, x_axis=x_axis)
# -------------------------------------------------------------------------
- def check_accuracy(self, n_samples=None, samples=None, outputs=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.
@@ -214,13 +212,6 @@ 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:
@@ -264,10 +255,9 @@ class PostProcessing:
self.engine.MetaModel.rmse = self.rmse
self.engine.MetaModel.valid_error = self.valid_error
- return
# -------------------------------------------------------------------------
- def plot_seq_design_diagnostics(self, ref_BME_KLD=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.
@@ -277,10 +267,6 @@ class PostProcessing:
ref_BME_KLD : array, optional
Reference BME and KLD . The default is `None`.
- Returns
- -------
- None.
-
"""
engine = self.engine
n_init_samples = engine.ExpDesign.n_init_samples
@@ -517,7 +503,7 @@ class PostProcessing:
return
# -------------------------------------------------------------------------
- def sobol_indices(self, xlabel='Time [s]', plot_type=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
@@ -541,6 +527,11 @@ class PostProcessing:
Plot type. The default is `None`. This corresponds to line plot.
Bar chart can be selected by `bar`.
+ Raises
+ ------
+ AttributeError
+ MetaModel in given Engine needs to be of type 'pce' or 'apce'.
+
Returns
-------
sobol_cell: dict
@@ -827,7 +818,9 @@ class PostProcessing:
return self.total_sobol
# -------------------------------------------------------------------------
- def check_reg_quality(self, n_samples=1000, samples=None, outputs=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
@@ -843,10 +836,6 @@ class PostProcessing:
Output dictionary with model outputs for all given output types in
`Model.Output.names`. The default is None.
- Returns
- -------
- None.
-
"""
if samples is None:
self.n_samples = n_samples
diff --git a/tests/test_ExpDesign.py b/tests/test_ExpDesign.py
index 9cef6cd13..979fbb26c 100644
--- a/tests/test_ExpDesign.py
+++ b/tests/test_ExpDesign.py
@@ -16,7 +16,6 @@ import pytest
import numpy as np
from bayesvalidrox.surrogate_models.inputs import Input
-import bayesvalidrox.surrogate_models.exp_designs as exp
from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
#%% Test ExpDesign.pcm_sampler
--
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