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style="html=1;whiteSpace=wrap;strokeWidth=2;" vertex="1" parent="1"> + <mxGeometry x="-896" y="666" width="160" height="50" as="geometry" /> + </mxCell> + <mxCell id="HC1H8j6nMwEtLoyIrXXk-17" value="setup" style="html=1;whiteSpace=wrap;" vertex="1" parent="1"> + <mxGeometry x="-666" y="566" width="110" height="50" as="geometry" /> + </mxCell> </root> </mxGraphModel> </diagram> diff --git a/examples/model-comparison/example_model_comparison.py b/examples/model-comparison/example_model_comparison.py index f087570326aba79ae9f6123e321ddd8c22a6601a..d678898c178e9722445f08bd22e3ec93906e2ac7 100644 --- a/examples/model-comparison/example_model_comparison.py +++ b/examples/model-comparison/example_model_comparison.py @@ -290,9 +290,9 @@ if __name__ == "__main__": BayesOpts = BayesModelComparison( justifiability=True, n_bootstrap=100,#0,#00, - just_n_meas=2 + #just_n_meas=2 ) - output_dict = BayesOpts.create_model_comparison( + output_dict = BayesOpts.model_comparison_all( meta_models, opts_bootstrap ) diff --git a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py index ea8bac786dc882d1107438e8b3e96bf75123f853..a26eaa886d6bfda11c23e0e2a490f9f93fee1c45 100644 --- a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py +++ b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py @@ -52,68 +52,19 @@ class BayesModelComparison: # Other parameters self.n_meas = None - self.justData = None - self.BME_Dict = None + self.BF_data = None + self.just_data = None + self.BME_dict = None self.set_up = False self.dtype = None + self.bayes_dict = None + self.just_bayes_dict = None + self.model_weights = None + self.model_weights_dict = None + self.just_model_weights_dict = None - - # -------------------------------------------------------------------------- - def create_model_comparison(self, model_dict, opts_dict): - """ - Starts the two-stage model comparison. - Stage I: Compare models using Bayes factors. - Stage II: Compare models via justifiability analysis. - - Parameters - ---------- - model_dict : dict - A dictionary including the metamodels. - opts_dict : dict - A dictionary given the `BayesInference` options. - - Example: - - >>> opts_bootstrap = { - "bootstrap": True, - "n_samples": 10000, - "Discrepancy": DiscrepancyOpts, - "emulator": True, - "plot_post_pred": True - } - - Returns - ------- - output : dict - A dictionary containing the objects and the model weights for the - comparison using Bayes factors and justifiability analysis. - - """ - # TODO: why are these two separate calls of the same function? - # They should be performable at the same time - # Bayes factor - bayes_dict_bf, model_weights_dict_bf = self.compare_models( - model_dict, opts_dict - ) - - output = { - 'Bayes objects BF': bayes_dict_bf, - 'Model weights BF': model_weights_dict_bf - } - - # Justifiability analysis - if self.justifiability: - bayes_dict_ja, model_weights_dict_ja = self.compare_models( - model_dict, opts_dict, justifiability=True - ) - - # TODO: why does this version of the call not return a summarized confusion matrix? - output['Bayes objects JA'] = bayes_dict_ja - output['Model weights JA'] = model_weights_dict_ja - - return output - + # -------------------------------------------------------------------------- def setup(self, model_dict): """ Initialize parameters that are needed for all types of model comparison @@ -155,13 +106,12 @@ class BayesModelComparison: # -------------------------------------------------------------------------- - def compare_models(self, model_dict, opts_dict, justifiability=False): + def model_comparison_all(self, model_dict, opts_dict): """ - Passes the options to instantiates the BayesInference class for each - model and passes the options from `opts_dict`. Then, it starts the - computations. - It also creates a folder and saves the diagrams, e.g., Bayes factor - plot, confusion matrix, etc. + Perform all three types of model comparison: + * Bayes Factors + * Model weights + * Justifiability analysis Parameters ---------- @@ -169,54 +119,57 @@ class BayesModelComparison: A dictionary including the metamodels. opts_dict : dict A dictionary given the `BayesInference` options. - justifiability : bool, optional - Whether to perform the justifiability analysis. The default is - `False`. Returns ------- - bayes_dict : dict - A dictionary with `BayesInference` objects. - model_weights_dict : dict - A dictionary containing the model weights. + results : dict + A dictionary that contains the calculated BME values, model weights + and confusion matrix """ + self.calc_bayes_factors(model_dict, opts_dict) + self.calc_model_weights(model_dict, opts_dict) + self.calc_justifiability_analysis(model_dict, opts_dict) + + results = {'BME': self.BME_dict, 'Model weights': self.model_weights_dict, + 'Confusion matrix': self.confusion_matrix} + return results + - self.setup(model_dict) + # -------------------------------------------------------------------------- + def calc_bayes_factors(self, model_dict, opts_dict): + """ + Calculate the BayesFactors for each pair of models in the model_dict + with respect to given data. + + Parameters + ---------- + model_dict : dict + A dictionary including the metamodels. + opts_dict : dict + A dictionary given the `BayesInference` options. + + Returns + ------- + None. + + """ + # Do the setup + if self.n_meas is None: + self.setup(model_dict) # ----- Generate data ----- # Create dataset - self.justData = self.generate_dataset( - model_dict, justifiability, n_bootstrap=self.n_bootstrap) + self.BF_data = self.generate_dataset( + model_dict, False, n_bootstrap=self.n_bootstrap) # Run create Interface for each model self.bayes_dict = {} for model in model_dict.keys(): print("-"*20) print("Bayesian inference of {}.\n".format(model)) - BayesOpts = BayesInference(model_dict[model]) - - # Explicitly set the settings of the BayesOpts - if self.use_Bayes_settings: - BayesOpts.emulator= True - BayesOpts.plot_post_pred = True - #BayesOpts.inference_method = 'rejection' - BayesOpts.bootstrap = True - BayesOpts.n_bootstrap_itrs = 10 - BayesOpts.bootstrap_noise = 0.05 - BayesOpts.bmc = True - # Set the MCMC parameters - BayesOpts.inference_method = "MCMC" - BayesOpts.mcmc_params = { - 'n_steps': 1e3,#5, - 'n_walkers': 30, - 'moves': emcee.moves.KDEMove(), - 'multiprocessing': False, - 'verbose': False - } - # Set BayesInference options for key, value in opts_dict.items(): if key in BayesOpts.__dict__.keys(): @@ -226,49 +179,54 @@ class BayesModelComparison: setattr(BayesOpts, key, value) # Pass justifiability data as perturbed data - BayesOpts.perturbed_data = self.justData - BayesOpts.just_analysis = justifiability + BayesOpts.bmc = True + BayesOpts.emulator= self.emulator + BayesOpts.just_analysis = False + BayesOpts.perturbed_data = self.BF_data self.bayes_dict[model] = BayesOpts.create_inference() print("-"*20) - # Compute model weights - self.BME_Dict = dict() + # Accumulate the BMEs + self.BME_dict = dict() for modelName, bayesObj in self.bayes_dict.items(): - self.BME_Dict[modelName] = np.exp(bayesObj.log_BME, dtype=self.dtype) - - # BME correction in BayesInference class - self.model_weights = self.cal_model_weight( - self.BME_Dict, justifiability, n_bootstrap=self.n_bootstrap) - - # Plot model weights - if justifiability: - model_names = self.model_names - model_names.insert(0, 'Observation') + self.BME_dict[modelName] = np.exp(bayesObj.log_BME, dtype=self.dtype) - # Split the model weights and save in a dict - list_ModelWeights = np.split( - self.model_weights, self.model_weights.shape[1]/self.n_meas, axis=1) - model_weights_dict = {key: weights for key, weights in - zip(model_names, list_ModelWeights)} + # TODO: move the calculation of the Bayes Factors out of the plots to here! + # Create kde plot for bayes factors + self.plot_bayes_factor(self.BME_dict, 'kde_plot') + + + def calc_model_weights(self, model_dict, opts_dict): + """ + Calculate the model weights from BME evaluations for Bayes factors. - #self.plot_just_analysis(model_weights_dict) - else: - # Create box plot for model weights - self.plot_model_weights(self.model_weights, 'model_weights') + Parameters + ---------- + model_dict : TYPE + DESCRIPTION. + opts_dict : TYPE + DESCRIPTION. - # Create kde plot for bayes factors - self.plot_bayes_factor(self.BME_Dict, 'kde_plot') + Returns + ------- + None. - # Store model weights in a dict - model_weights_dict = {key: weights for key, weights in - zip(self.model_names, self.model_weights)} + """ + # Get BMEs via Bayes Factors if not already done so + if self.BME_dict is None: + self.calc_bayes_factors(model_dict, opts_dict) + + # Calculate the model weights + self.model_weights = self.cal_model_weight( + self.BME_dict, False, n_bootstrap=self.n_bootstrap) - return self.bayes_dict, model_weights_dict + # Create box plot for model weights + self.plot_model_weights(self.model_weights, 'model_weights') # ------------------------------------------------------------------------- - def justifiability_analysis(self, model_dict, opts_dict): + def calc_justifiability_analysis(self, model_dict, opts_dict): """ Perform justifiability analysis by calculating the confusion matrix @@ -278,9 +236,6 @@ class BayesModelComparison: A dictionary including the metamodels. opts_dict : dict A dictionary given the `BayesInference` options. - justifiability : bool, optional - Whether to perform the justifiability analysis. The default is - `False`. Returns ------- @@ -299,53 +254,52 @@ class BayesModelComparison: # Generate data # TODO: generate the datset only if it does not exist yet - self.justData = self.generate_dataset( + self.just_data = self.generate_dataset( model_dict, True, n_bootstrap=self.n_bootstrap) - # Run inference for each model - self.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.justData - - self.bayes_dict[model] = BayesOpts.create_inference() - print("-"*20) + # 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) # Compute model weights - self.BME_Dict = dict() + self.BME_dict = dict() for modelName, bayesObj in self.bayes_dict.items(): - self.BME_Dict[modelName] = np.exp(bayesObj.log_BME, dtype=self.dtype) + self.BME_dict[modelName] = np.exp(bayesObj.log_BME, dtype=self.dtype) # BME correction in BayesInference class - self.model_weights = self.cal_model_weight( - self.BME_Dict, True, n_bootstrap=self.n_bootstrap) + just_model_weights = self.cal_model_weight( + self.BME_dict, True, n_bootstrap=self.n_bootstrap) # Split the model weights and save in a dict list_ModelWeights = np.split( - self.model_weights, self.model_weights.shape[1]/self.n_meas, axis=1) - self.model_weights_dict = {key: weights for key, weights in + just_model_weights, self.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)} - # Average the confusion matrix # Confusion matrix over all measurement points cf_m = pd.DataFrame() cf_m['Generated by'] = model_names - # Iterate over eval models for i in range(len(model_names)): # Ignore 'Observation', this not in the model_weights_dict # TODO: how to change the code so that it is included as well? @@ -353,13 +307,14 @@ class BayesModelComparison: continue avg = [] for n in model_names: - avg.append(np.sum(self.model_weights_dict[n][i-1])) - # Norm to sum to 1 over generated by + avg.append(np.sum(self.just_model_weights_dict[n][i-1])) + + # Norm to sum to 1 for each 'Generated by' row cf_m[model_names[i]] = avg/self.n_meas self.confusion_matrix = cf_m # Plot model weights - self.plot_just_analysis(self.model_weights_dict) + self.plot_just_analysis() # ------------------------------------------------------------------------- @@ -474,13 +429,13 @@ class BayesModelComparison: return final_data # ------------------------------------------------------------------------- - def cal_model_weight(self, BME_Dict, justifiability=False, n_bootstrap=1): + def cal_model_weight(self, BME_dict, justifiability=False, n_bootstrap=1): """ Normalize the BME (Asumption: Model Prior weights are equal for models) Parameters ---------- - BME_Dict : dict + BME_dict : dict A dictionary containing the BME values. Returns @@ -490,7 +445,7 @@ class BayesModelComparison: """ # Stack the BME values for all models - all_BME = np.vstack(list(BME_Dict.values())) + all_BME = np.vstack(list(BME_dict.values())) if justifiability: # Compute expected log_BME for justifiabiliy analysis @@ -518,10 +473,11 @@ class BayesModelComparison: None. """ - + model_weights_dict = self.just_model_weights_dict Color = [*mcolors.TABLEAU_COLORS] names = [*model_weights_dict] + # Plot weights for each 'Generated by' model_names = [model.replace('_', '$-$') for model in self.model_names] for name in names: fig, ax = plt.subplots() @@ -543,7 +499,7 @@ class BayesModelComparison: ) plt.close() - # Confusion matrix for some measurement points + # Confusion matrix for each measurement point for index in range(0, self.n_meas): weights = np.array( [model_weights_dict[key][:, index] for key in model_weights_dict] @@ -564,8 +520,6 @@ class BayesModelComparison: ) plt.close() - - # Plot the averaged confusion matrix out_names = names[1:] cf = self.confusion_matrix[out_names].to_numpy() @@ -581,7 +535,6 @@ class BayesModelComparison: ) plt.close() - # ------------------------------------------------------------------------- def plot_model_weights(self, model_weights, plot_name): @@ -601,13 +554,9 @@ class BayesModelComparison: None. """ - font_size = 40 - # mkdir for plots - directory = 'Outputs_Comparison/' - os.makedirs(directory, exist_ok=True) - # Create figure fig, ax = plt.subplots() + font_size = 40 # Filter data using np.isnan mask = ~np.isnan(model_weights.T) @@ -635,44 +584,35 @@ class BayesModelComparison: for median in bp['medians']: median.set(color='#b2df8a', linewidth=2) - # change the style of fliers and their fill - # for flier in bp['fliers']: - # flier.set(marker='o', color='#e7298a', alpha=0.75) - - # Custom x-axis labels + # Customize the axes model_names = [model.replace('_', '$-$') for model in self.model_names] ax.set_xticklabels(model_names) - ax.set_ylabel('Weight', fontsize=font_size) - - # Title - plt.title('Posterior Model Weights') - - # Set y lim ax.set_ylim((-0.05, 1.05)) - - # Set size of the ticks for t in ax.get_xticklabels(): t.set_fontsize(font_size) for t in ax.get_yticklabels(): t.set_fontsize(font_size) + # Title + plt.title('Posterior Model Weights') + # Save the figure fig.savefig( - f'./{directory}{plot_name}.pdf', bbox_inches='tight' + f'./{self.out_dir}{plot_name}.pdf', bbox_inches='tight' ) plt.close() # ------------------------------------------------------------------------- - def plot_bayes_factor(self, BME_Dict, plot_name=''): + def plot_bayes_factor(self, BME_dict, plot_name=''): """ Plots the Bayes factor distibutions in a :math:`N_m \\times N_m` matrix, where :math:`N_m` is the number of the models. Parameters ---------- - BME_Dict : dict + BME_dict : dict A dictionary containing the BME values of the models. plot_name : str, optional Plot name. The default is ''. @@ -682,16 +622,11 @@ class BayesModelComparison: None. """ - + # Plot setup font_size = 40 - - # mkdir for plots - directory = 'Outputs_Comparison/' - os.makedirs(directory, exist_ok=True) - Colors = ["blue", "green", "gray", "brown"] - model_names = list(BME_Dict.keys()) + model_names = list(BME_dict.keys()) nModels = len(model_names) # Plots @@ -713,7 +648,7 @@ class BayesModelComparison: # Null hypothesis: key_j is the better model BayesFactor = np.log10( - np.divide(BME_Dict[key_i], BME_Dict[key_j]) + np.divide(BME_dict[key_i], BME_dict[key_j]) ) # sns.kdeplot(BayesFactor, ax=ax, color=Colors[i], shade=True) @@ -806,10 +741,8 @@ class BayesModelComparison: fontsize=fsize, color=Colors[i], transform=ax.transAxes) - # Defining custom 'ylim' values. + # Customize axes custom_ylim = (0, 1.05) - - # Setting the values for all axes. plt.setp(axes, ylim=custom_ylim) # set labels @@ -821,7 +754,7 @@ class BayesModelComparison: plt.subplots_adjust(wspace=0.2, hspace=0.1) plt.savefig( - f'./{directory}Bayes_Factor{plot_name}.pdf', bbox_inches='tight' + f'./{self.out_dir}Bayes_Factor{plot_name}.pdf', bbox_inches='tight' ) plt.close()