bayesvalidrox.post_processing.post_processing.PostProcessing¶

class bayesvalidrox.post_processing.post_processing.PostProcessing(engine, name='calib', out_dir='', out_format='pdf')¶

Bases: object

This class provides post-processing functions for the trained metamodels.

Parameters¶

engineobj: Trained Engine object, is expected to contain a trained MetaModel object.
namestring: Name of the PostProcessing object to be used for saving the generated files. The default is ‘calib’.
out_dirstring: Output directory in which the PostProcessing results are placed. The results are contained in a subfolder ‘/Outputs_PostProcessing_name’ The default is ‘’.
out_formatstring: Format of the saved plots. Supports ‘png’ and ‘pdf’. The default is ‘pdf’.

Raises¶

AttributeError: engine must be trained.

__init__(engine, name='calib', out_dir='', out_format='pdf')¶

Methods

`__init__`(engine[, name, out_dir, out_format])
`check_accuracy`([n_samples, samples, outputs])	Checks accuracy of the metamodel by computing the root mean square error and validation error for all outputs.
`check_reg_quality`([n_samples, samples, outputs])	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
`plot_metamodel_3d`([n_samples])	Visualize the results of a MetaModel as a 3D surface over two input parameters.
`plot_moments`([plot_type])	Plots the moments in a user defined output format (standard is pdf) in the directory Outputs_PostProcessing.
`plot_seq_design_diagnostics`([ref_bme_kld])	Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence (KLD) for the sequential design.
`plot_sobol`(par_names, outputs[, sobol_type, ...])	Generate plots for each output in the given set of Sobol' indices.
`sobol_indices`([plot_type, save])	Visualizes and writes out Sobol' and Total Sobol' indices of the trained metamodel.
`valid_metamodel`([n_samples, samples, ...])	Evaluates and plots the meta model and the PCEModel outputs for the given number of samples or the given samples.

check_accuracy(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.

Parameters¶

n_samplesint, optional: Number of samples. The default is None.
samplesarray of shape (n_samples, n_params), optional: Parameter sets to be checked. The default is None.
outputsdict, optional: Output dictionary with model outputs for all given output types in engine.out_names. The default is None.

Raises¶

AttributeError: When neither n_samples nor samples are provided.

Returns¶

None

check_reg_quality(n_samples: int = 1000, samples=None, outputs: dict | None = 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

Parameters¶

n_samplesint, optional: Number of parameter sets to use for the check. The default is 1000.
samplesarray of shape (n_samples, n_params), optional: Parameter sets to use for the check. The default is None.
outputsdict, optional: Output dictionary with model outputs for all given output types in engine.out_names. The default is None.

Returns¶

None

plot_metamodel_3d(n_samples=10)¶

Visualize the results of a MetaModel as a 3D surface over two input parameters.

Parameters¶

n_samplesint: Number of samples that are used to generate the 3D plot.

Raises¶

AttributeError: This function is only applicable if the MetaModel input dimension is 2.

Returns¶

None

plot_moments(plot_type: str = 'line')¶

Plots the moments in a user defined output format (standard is pdf) in the directory Outputs_PostProcessing.

Parameters¶

plot_typestr, optional: Supports ‘bar’ for barplots and ‘line’ for lineplots The default is line.

Raises¶

AttributeError: Plot type must be ‘bar’ or ‘line’.

Returns¶

pce_means: dict: Mean of the model outputs.
pce_means: dict: Standard deviation of the model outputs.

plot_seq_design_diagnostics(ref_bme_kld=None) → None¶

Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence (KLD) for the sequential design.

Parameters¶

ref_bme_kldarray, optional: Reference BME and KLD . The default is None.

Returns¶

None

plot_sobol(par_names: list, outputs: list, sobol_type: str = 'sobol', i_order: int = 0) → None¶

Generate plots for each output in the given set of Sobol’ indices.

Parameters¶

par_nameslist: Parameter names for each Sobol’ index.
outputslist: Output names to be plotted.
sobol_typestring, optional: Type of Sobol’ indices to visualize. Can be either ‘sobol’ or ‘totalsobol’. The default is ‘sobol’.
i_orderint, optional: Order of Sobol’ index that should be plotted. This parameter is only applied for sobol_type = ‘sobol’. The default is 0.

Returns¶

None

sobol_indices(plot_type: str = 'line', save: bool = True)¶

Visualizes and writes out Sobol’ and Total Sobol’ indices of the trained metamodel. One file is created for each index and output key.

Parameters¶

plot_typestr, optional: Plot type, supports ‘line’ for lineplots and ‘bar’ for barplots. The default is line. Bar chart can be selected by bar.
savebool, optional: Write out the inidces as csv files if set to True. The default is True.

Raises¶

AttributeError: MetaModel in given Engine needs to be of type ‘pce’ or ‘apce’.
AttributeError: Plot-type must be ‘line’ or ‘bar’.

Returns¶

sobol_alldict: All possible Sobol’ indices for the given metamodel.
total_sobol_alldict: All Total Sobol’ indices for the given metamodel.

valid_metamodel(n_samples=1, samples=None, model_out_dict=None) → None¶

Evaluates and plots the meta model and the PCEModel outputs for the given number of samples or the given samples.

Parameters¶

n_samplesint, optional: Number of samples to be evaluated. The default is 1.
samplesarray of shape (n_samples, n_params), optional: Samples to be evaluated. The default is None.
model_out_dict: dict: The model runs using the samples provided.

Returns¶

None