From 260b4d80e0c6bdd899027aba486eb6d0b9adb20b Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Thu, 27 Jun 2024 17:46:39 +0200
Subject: [PATCH] Fix for the BMC issue
---
.../bayes_inference/bayes_inference.py | 6 ++-
.../bayes_inference/bayes_model_comparison.py | 54 ++++++++++---------
2 files changed, 32 insertions(+), 28 deletions(-)
diff --git a/src/bayesvalidrox/bayes_inference/bayes_inference.py b/src/bayesvalidrox/bayes_inference/bayes_inference.py
index ade1662c9..e6748a878 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_inference.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_inference.py
@@ -226,8 +226,6 @@ class BayesInference:
self.log_BME = None
self.KLD = None
self.__mean_pce_prior_pred = None
- if perturbed_data is None:
- perturbed_data = []
self.engine = engine
self.Discrepancy = discrepancy
self.emulator = emulator
@@ -268,6 +266,9 @@ class BayesInference:
self.__model_prior_pred = None
self.MCMC_Obj = None
+ # Empty perturbed data init
+ if self.perturbed_data is None:
+ self.perturbed_data = []
# System settings
if os.name == 'nt':
print('')
@@ -315,6 +316,7 @@ class BayesInference:
# Convert measured_data to a data frame
if not isinstance(self.measured_data, pd.DataFrame):
self.measured_data = pd.DataFrame(self.measured_data)
+
# Extract the total number of measurement points
if self.name.lower() == 'calib':
diff --git a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
index 3cc0918e4..dc66e1cf5 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
@@ -254,37 +254,39 @@ class BayesModelComparison:
# Generate data
# TODO: generate the datset only if it does not exist yet
+ # TODO: shape of this is still ok
self.just_data = self.generate_dataset(
model_dict, True, n_bootstrap=self.n_bootstrap)
# Run inference for each model if this is not available
- if self.just_bayes_dict is None:
- self.just_bayes_dict = {}
- for model in model_dict.keys():
- print("-"*20)
- print("Bayesian inference of {}.\n".format(model))
- BayesOpts = BayesInference(model_dict[model])
-
- # Set BayesInference options
- for key, value in opts_dict.items():
- if key in BayesOpts.__dict__.keys():
- if key == "Discrepancy" and isinstance(value, dict):
- setattr(BayesOpts, key, value[model])
- else:
- setattr(BayesOpts, key, value)
-
- # Pass justifiability data as perturbed data
- BayesOpts.bmc = True
- BayesOpts.emulator= self.emulator
- BayesOpts.just_analysis = True
- BayesOpts.perturbed_data = self.just_data
-
- self.just_bayes_dict[model] = BayesOpts.create_inference()
- print("-"*20)
+ #if self.just_bayes_dict is None:
+ self.just_bayes_dict = {}
+ for model in model_dict.keys():
+ print("-"*20)
+ print("Bayesian inference of {}.\n".format(model))
+ BayesOpts = BayesInference(model_dict[model])
+
+ # Set BayesInference options
+ for key, value in opts_dict.items():
+ if key in BayesOpts.__dict__.keys():
+ if key == "Discrepancy" and isinstance(value, dict):
+ setattr(BayesOpts, key, value[model])
+ else:
+ setattr(BayesOpts, key, value)
+
+ # Pass justifiability data as perturbed data
+ BayesOpts.bmc = True
+ BayesOpts.emulator= self.emulator
+ BayesOpts.just_analysis = True
+ BayesOpts.perturbed_data = self.just_data
+
+ self.just_bayes_dict[model] = BayesOpts.create_inference()
+ print("-"*20)
# Compute model weights
+ # TODO: shape of this now ok as well
self.BME_dict = dict()
- for modelName, bayesObj in self.bayes_dict.items():
+ for modelName, bayesObj in self.just_bayes_dict.items():
self.BME_dict[modelName] = np.exp(bayesObj.log_BME, dtype=self.dtype)
# BME correction in BayesInference class
@@ -293,7 +295,7 @@ class BayesModelComparison:
# Split the model weights and save in a dict
list_ModelWeights = np.split(
- just_model_weights, self.model_weights.shape[1]/self.n_meas, axis=1)
+ just_model_weights, just_model_weights.shape[1]/self.n_meas, axis=1)
self.just_model_weights_dict = {key: weights for key, weights in
zip(model_names, list_ModelWeights)}
@@ -458,7 +460,7 @@ class BayesModelComparison:
return model_weights
# -------------------------------------------------------------------------
- def plot_just_analysis(self, model_weights_dict):
+ def plot_just_analysis(self):
"""
Visualizes the confusion matrix and the model wights for the
justifiability analysis.
--
GitLab